new horizons of research

Scientists push new paradigm of animal consciousness, saying even insects may be sentient

Bees play by rolling wooden balls — apparently for fun . The cleaner wrasse fish appears to recognize its own visage in an underwater mirror . Octopuses seem to react to anesthetic drugs and will avoid settings where they likely experienced past pain. 

All three of these discoveries came in the last five years — indications that the more scientists test animals, the more they find that many species may have inner lives and be sentient. A surprising range of creatures have shown evidence of conscious thought or experience, including insects, fish and some crustaceans. 

That has prompted a group of top researchers on animal cognition to publish a new pronouncement that they hope will transform how scientists and society view — and care — for animals. 

Nearly 40 researchers signed “ The New York Declaration on Animal Consciousness ,” which was first presented at a conference at New York University on Friday morning. It marks a pivotal moment, as a flood of research on animal cognition collides with debates over how various species ought to be treated. 

The declaration says there is “strong scientific support” that birds and mammals have conscious experience, and a “realistic possibility” of consciousness for all vertebrates — including reptiles, amphibians and fish. That possibility extends to many creatures without backbones, it adds, such as insects, decapod crustaceans (including crabs and lobsters) and cephalopod mollusks, like squid, octopus and cuttlefish.

“When there is a realistic possibility of conscious experience in an animal, it is irresponsible to ignore that possibility in decisions affecting that animal,” the declaration says. “We should consider welfare risks and use the evidence to inform our responses to these risks.” 

Jonathan Birch, a professor of philosophy at the London School of Economics and a principal investigator on the Foundations of Animal Sentience project, is among the declaration’s signatories. Whereas many scientists in the past assumed that questions about animal consciousness were unanswerable, he said, the declaration shows his field is moving in a new direction. 

“This has been a very exciting 10 years for the study of animal minds,” Birch said. “People are daring to go there in a way they didn’t before and to entertain the possibility that animals like bees and octopuses and cuttlefish might have some form of conscious experience.”

From 'automata' to sentient

There is not a standard definition for animal sentience or consciousness, but generally the terms denote an ability to have subjective experiences: to sense and map the outside world, to have capacity for feelings like joy or pain. In some cases, it can mean that animals possess a level of self-awareness. 

In that sense, the new declaration bucks years of historical science orthodoxy. In the 17th century, the French philosopher René Descartes argued that animals were merely “material automata” — lacking souls or consciousness.

Descartes believed that animals “can’t feel or can’t suffer,” said Rajesh Reddy, an assistant professor and director of the animal law program at Lewis & Clark College. “To feel compassion for them, or empathy for them, was somewhat silly or anthropomorphizing.” 

In the early 20th century, prominent behavioral psychologists promoted the idea that science should only study observable behavior in animals, rather than emotions or subjective experiences . But beginning in the 1960s, scientists started to reconsider. Research began to focus on animal cognition, primarily among other primates. 

Birch said the new declaration attempts to “crystallize a new emerging consensus that rejects the view of 100 years ago that we have no way of studying these questions scientifically.” 

Indeed, a surge of recent findings underpin the new declaration. Scientists are developing new cognition tests and trying pre-existing tests on a wider range of species, with some surprises. 

Take, for example, the mirror-mark test, which scientists sometimes use to see if an animal recognizes itself. 

In a series of studies, the cleaner wrasse fish seemed to pass the test . 

The fish were placed in a tank with a covered mirror, to which they exhibited no unusual reaction. But after the cover was lifted, seven of 10 fish launched attacks toward the mirror, signaling they likely interpreted the image as a rival fish. 

After several days, the fish settled down and tried odd behaviors in front of the mirror, like swimming upside down, which had not been observed in the species before. Later, some appeared to spend an unusual amount of time in front of the mirror, examining their bodies. Researchers then marked the fish with a brown splotch under the skin, intended to resemble a parasite. Some fish tried to rub the mark off. 

“The sequence of steps that you would only ever have imagined seeing with an incredibly intelligent animal like a chimpanzee or a dolphin, they see in the cleaner wrasse,” Birch said. “No one in a million years would have expected tiny fish to pass this test.”

In other studies, researchers found that zebrafish showed signs of curiosity when new objects were introduced into their tanks and that cuttlefish could remember things they saw or smelled . One experiment created stress for crayfish by electrically shocking them , then gave them anti-anxiety drugs used in humans. The drugs appeared to restore their usual behavior.

Birch said these experiments are part of an expansion of animal consciousness research over the past 10 to 15 years. “We can have this much broader canvas where we’re studying it in a very wide range of animals and not just mammals and birds, but also invertebrates like octopuses, cuttlefish,” he said. “And even increasingly, people are talking about this idea in relation to insects.”

As more and more species show these types of signs, Reddy said, researchers might soon need to reframe their line of inquiry altogether: “Scientists are being forced to reckon with this larger question — not which animals are sentient, but which animals aren’t?” 

New legal horizons

Scientists’ changing understanding of animal sentience could have implications for U.S. law, which does not classify animals as sentient on a federal level, according to Reddy. Instead, laws pertaining to animals focus primarily on conservation, agriculture or their treatment by zoos, research laboratories and pet retailers.

“The law is a very slow moving vehicle and it really follows societal views on a lot of these issues,” Reddy said. “This declaration, and other means of getting the public to appreciate that animals are not just biological automatons, can create a groundswell of support for raising protections.” 

State laws vary widely. A decade ago, Oregon passed a law recognizing animals as sentient and capable of feeling pain, stress and fear, which Reddy said has formed the bedrock of progressive judicial opinions in the state.  

Meanwhile, Washington and California are among several states where lawmakers this year have considered bans on octopus farming, a species for which scientists have found strong evidence of sentience. 

British law was recently amended to consider octopuses sentient beings — along with crabs and lobsters .

“Once you recognize animals as sentient, the concept of humane slaughter starts to matter, and you need to make sure that the sort of methods you’re using on them are humane,” Birch said. “In the case of crabs and lobsters, there are pretty inhumane methods, like dropping them into pans of boiling water, that are very commonly used.”

Evan Bush is a science reporter for NBC News. He can be reached at [email protected].

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• Published: 27 April 2022

FAIR data enabling new horizons for materials research

• Matthias Scheffler   ORCID: orcid.org/0000-0002-1280-9873 1 , 2 ,
• Martin Aeschlimann   ORCID: orcid.org/0000-0003-3413-5029 3 ,
• Martin Albrecht 4 ,
• Tristan Bereau 5 ,
• Hans-Joachim Bungartz 6 ,
• Claudia Felser   ORCID: orcid.org/0000-0002-8200-2063 7 ,
• Mark Greiner   ORCID: orcid.org/0000-0002-4363-7189 8 ,
• Axel Groß   ORCID: orcid.org/0000-0003-4037-7331 9 ,
• Christoph T. Koch   ORCID: orcid.org/0000-0002-3984-1523 1 ,
• Kurt Kremer 5 ,
• Wolfgang E. Nagel 10 ,
• Markus Scheidgen 1 ,
• Christof Wöll   ORCID: orcid.org/0000-0003-1078-3304 11 &
• Claudia Draxl   ORCID: orcid.org/0000-0003-3523-6657 1 , 2  

Nature volume  604 ,  pages 635–642 ( 2022 ) Cite this article

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• Materials science

The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science.

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Generating FAIR research data in experimental tribology

OPTIMADE, an API for exchanging materials data

Tracking materials science data lineage to manage millions of materials experiments and analyses

The number of possible materials is practically infinite. But even for the so-far known materials, our knowledge about their properties and their synthesis is very shallow. There is no doubt that forms of condensed matter exist, or can be created, that show better, or even new, properties and functions than the materials that are known and used today. How can we find them? High-throughput screening of materials — experimentally or theoretically — collects important information. These results will boost new discoveries, but the immensity of possible materials cannot be covered by such explicit searches. Moreover, in current purpose-focused research, only a small fraction of the data produced in the studies is published, and many data are not fully characterized. Furthermore, the metadata (the information that explains and characterizes the measured or calculated data), ontologies (the relationships in metadata) and workflows of different research groups cannot be easily reconciled. Thus, most research data are neither findable nor interoperable.

A FAIR data infrastructure will foster the exchange of scientific information. The meaning of the acronym—that data should be findable, accessible, interoperable and reusable—is explained in the original publication by Wilkinson et al. 1 and elaborated on, for example, at the GO FAIR web pages ( https://go-fair.org/fair-principles/ ). The crucial and very laborious first step towards the FAIRification of data concerns the need to comprehensively describe data by metadata; that is, to characterize data fully and unambiguously so that the research is reproducible. Then scientists, engineers and others can also combine data and metadata from different studies and use them in different contexts. This will open synergies between materials science subdomains and facilitate inter-institute and cross-discipline research. It will also enable data to be used for deeper analyses and for training AI models. Clearly, a FAIR data infrastructure will also show data provenance.

The US Materials Genome Initiative (MGI, https://mgi.gov/ ) was announced in 2011 for “discovering, manufacturing, and deploying advanced materials twice as fast and at a fraction of the cost compared to traditional methods.” It markedly boosted collaborations and high-throughput experiments and computations. FAIRmat ( https://FAIRmat-NFDI.eu/ ) develops the original MGI concept further by implementing a FAIR data infrastructure for condensed-matter physics and the chemical physics of solids. It is a consortium of the German National Research Data Infrastructure programme ( https://nfdi.de ). FAIRmat interweaves data and tools from and for materials synthesis, experiments, theory and computation, and makes all data available to the whole materials science community and beyond. In this endeavour, it unites researchers from condensed matter physics, the chemical physics of solids and computer science and IT experts.

Materials science is strongly affected by all the four Vs (4V) of big data: volume (the amount of data), variety (the heterogeneity of form and meaning of data), velocity (the rate at which data may change or new data arrive) and veracity (the uncertainty of data quality). The various experimental and theoretical examples provided below will illustrate these different aspects. In general, a FAIR data infrastructure requires an in-depth description of how the data have been obtained, addressing metadata, ontologies and workflows. Obviously, only the experts (those creating the samples or computer codes and performing measurements or calculations that is, producing the data) have the insight and knowledge to provide this critical information.

The topic of this Perspective, as outlined above, includes the request for a notable change in scientific culture. Thinking beyond our present research focus on effect, phenomenon or application requires us to accept publishing ‘clean data’—that is, well-characterized and clearly annotated data—represents a value of similar importance to a standard publication, or even higher. This concept carries analogies to Tycho Brahe, who created the data that enabled Johannes Kepler to find his equations and finally led Newton to formulate his theory of gravitation.

Eventually, after having installed an efficient, FAIR research data infrastructure, hosting all data from synthesis, experimental and theoretical studies for a wide range of materials, we also need to pave the way for carrying out new research. Our scientific vision is to build maps of material properties that will guide us in designing and finding new materials for a desired function. This concept follows the spirit of the creation of the periodic table of elements; organizing the roughly 60 atoms known at the time enabled Mendeleev to predict the existence and properties of yet-to-be discovered elements.

In the following, we will describe the state of the art, highlight the challenges and put forward FAIRmat’s envisaged solutions.

Data-centric materials science

Science is and always has been based on data, but the term ‘data-centric’ indicates a radical shift in how information is handled and research is performed. It refers to extensive data collections, digital repositories and new concepts and methods of data analytics. It also implies that we complement traditional purpose-oriented research by using data from other studies.

Some progress in this direction has been made in recent years in terms of collecting data from the many research groups across the planet (all the data, not just what is published in research manuscripts) and making the data FAIR 1 , 2 . This should be good scientific practice in any case 3 , 4 . Since 1965, data repositories in materials science have moved towards digitization. A comprehensive list can be found in ref. 5 . Among them, the NOMAD (Novel Materials Discovery) Laboratory (a database for computational materials science; online since 2014, https://nomad-lab.eu/ ) is unique as it accepts data from practically all computational materials science codes. As it provides the blueprint for FAIRmat, we will summarize its basic concept (for details, see refs. 4 , 6 ). A key guideline of NOMAD (and FAIRmat) is to help scientists and students to upload and download data in the easiest way. In simple terms, data stored at NOMAD are treated analogously to publications at a journal archive, such as https://arxiv.org/ . Unlike journal archives, an embargo period can be used for collaborations with selected colleagues or may even be crucial for collaborations with industry. At the time of writing (August 2021), NOMAD contains results from more than 100 million open-access calculations. These are from individual researchers all over the world and include entries from other computational materials databases, such as AFLOW ( http://aflow.org ), the Materials Project ( https://materialsproject.org ) and OQMD (the Open Quantum Materials Database, http://oqmd.org ). NOMAD converts the data into a common form and provides an easy materials view presentation by means of the NOMAD Encyclopedia ( https://nomad-lab.eu/encyclopedia ). This allows users to see, compare, explore and understand computed materials data. Furthermore, the NOMAD Artificial Intelligence Toolkit ( https://nomad-lab.eu/AIToolkit ) offers tools for data analytics and predictions.

The overall challenges of FAIRmat are sketched in Fig. 1 : besides organizing and — equally importantly — convincing the community (top left), a critical task concerns the development of metadata standards and ontologies (top right). At present, in materials science, such standards are either totally missing or incomplete. Numerous attempts from standards organizations, such as the International Standards Organization ( https://iso.org/ ), to provide controlled vocabularies, standards for data formats and data handling, have so far failed to reach community-wide adoption.

This schematic summarizes the four main categories of challenge faced in implementing FAIRmat.

FAIRmat has already started to establish metadata and dictionaries for digital translations of the vocabulary used in different domains. The next step concerns the description of relations between them, hence, the development of ontologies. They will become particularly important when involved workflows are needed. The NOMAD Meta Info 7 ( https://nomad-lab.eu/metainfo ) stores descriptive and structured information about materials science data and some interdependencies. Thus, it represents an ontology precursor. There are a lot of discussions regarding ontology within the community; see, for example, refs. 7 , 8 , 9 and the metadata and ontology activities at NIST ( https://data.nist.gov/od/dm/nerdm/ ) and the Materials Ontologies RDA Task Group ( https://rd-alliance.org ). This also concerns collaborations of FAIRmat with EMMC ( https://emmc.info ), OPTIMADE 9 ( https://optimade.org ) and NIST ( https://data.nist.gov/ ).

As illustrated in Fig. 1 , data-centric materials science requires a complex infrastructure (bottom right). Established standards for data models in materials science will be considered; for example, CIF (Crystallographic Information Framework, https://iucr.org/resources/cif ), CSMD (Core Scientific Metadata Model, http://icatproject-contrib.github.io/CSMD ) and NeXus ( https://nexusformat.org/ ). Last but not least, acceptance by researchers requires that the infrastructure also offer support and efficient tools for data processing and analysis (Fig. 1 , bottom left).

Other research fields are facing different yet analogous challenges. International contacts, coordination and collaborations of the various fields are promoted by the GO FAIR initiative ( https://go-fair.org/ ), the Research Data Alliance (RDA, https://rd-alliance.org/ ), the association FAIR-DI ( https://fair-di.eu ), CODATA ( https://codata.org/ ) and others. A recent publication 10 by Wittenburg et al. on ‘FAIR practices in Europe’ describes the situation in the areas of humanities, environmental sciences and natural sciences. Although basic concepts and IT tasks are being discussed, true collaborations and reaching the final goal of growing together still need time.

Preparing the research of tomorrow

Putting what is outlined above into practice is a rocky road. To motivate the community to join a culture of extensive data sharing, FAIRmat’s policy is to lead by example. Two issues are obviously important to speed up the process and trigger active support: (1) successful, living examples of daily data-centric research 11 to demonstrate what and how things work; and (2) outreach to the wider community, including the education of future scientists and engineers.

To cope with the first point, FAIRmat will demonstrate its approach with specific examples from diverse research fields, including battery research, heterogeneous catalysis, optoelectronics, magnetism and spintronics, multifunctional materials and biophysics. In all of this, FAIRmat will demonstrate the synergistic interplay of materials synthesis, sample preparation and experiments, as well as theory and computation, and provide a much more comprehensive picture than the single subcommunities can achieve. As such, FAIRmat will bring together not only data and tools but, most notably, also people, who will learn each other’s ‘language’. In fact, the necessary width of competences goes along with a diversity in the nomenclature, which can hamper communication as well as the definition of metadata and ontologies. Likewise, electronic lab notebooks (ELNs) must be standardized to allow seamless integration of data into automatic workflows. Dedicated data-analysis and AI tools will be developed and demonstrated that help to identify the key descriptive physicochemical parameters 12 , 13 , 14 , 15 . This will allow for predictions that go beyond the immediately studied systems and will show trends and enable the identification of materials with statistically exceptional properties 16 . Combining data from different repositories opens further opportunities.

Let us exemplify with two emerging classes of materials that the exploitation of an efficient data infrastructure will be not only helpful but simply mandatory for the digitization of materials research 17 . These examples are high-entropy alloys (HEAs) and metal–organic frameworks (MOFs). For these classes, the sheer number of possible materials is so large that conventional approaches will never be able to unleash even a small part of their full potential. For HEAs, a number of 10 9 possible composite materials with distinctly different properties has been estimated 18 , with many of them showing, for example, mechanical properties that exceed by far those of conventional alloys. This huge space of materials further contains HEA oxides with interesting properties in catalysis and energy storage. In the case of MOFs, the situation is even more pronounced. As a result of the huge diversity of MOF building blocks, inorganic clusters and multitopic molecules, the number of compounds is unlimited. Even if one limits the building block weight to that of fullerene (C 60 ), synthesizing only one replica of each compound would already need more atoms than are available on planet Earth. Using AI to analyse the huge amount of experimental information (data for about 100,000 MOFs are stored in databases 19 ), we will be able to identify or predict MOFs with particular properties dictated by conceived applications 20 ; for example, in optoelectronics 21 , biomedicine or catalysis 22 .

Turning to the second point—to foster awareness of the importance of FAIR scientific data management and stewardship 1 —FAIRmat will reach out to current students of physics, chemistry, materials science and engineering. We aim to educate a new generation of interdisciplinary researchers, offering classes and lab courses, and to introduce new curricula. A necessary requirement is to convince teachers, professors and other decision makers. The FAIRmat consortium will initiate and organize focused, crosscutting workshops together with, for example, colleagues from chemistry and biochemistry, astroparticle and elementary particle physics, mathematics and engineering. Some topics may be general, such as ontologies or data infrastructure, whereas others will be more specific, including particular experimental techniques or specific simulation methods. Hands-on training, schools and hackathons, as well as the usual online tutorials, will be part of our portfolio. Listening to the needs of small communities or groups will make sure that no one is left behind.

Although industry is very interested in the availability of data, the materials encyclopedia and the AI tools, most investigators hesitate to contribute their own data. Understandably, a company can survive only if they create products that are better or cheaper than those of their competitors. FAIRmat accepts these worries, for example, by allowing for an embargo of uploaded data (see above). The NOMAD Oasis (see also below), which is a key element of the federated FAIRmat infrastructure, can also be operated behind industrial firewalls as a stand-alone server with full functionality.

Science is an international, open activity. So, clearly, all the concepts and plans are and will be discussed, coordinated and implemented together with our colleagues worldwide. In fact, the first FAIR-DI Conference on a FAIR Data Infrastructure for Materials Genomics had 539 participants from all over the world ( https://th.fhi-berlin.mpg.de/meetings/fairdi2020/ ).

Let us end this section by noting that individual researchers already profit from the data infrastructure, even though we are at an early stage in progressing towards the next level of research. For example, countless CPU hours are being saved because computational results are well documented and accessible and do not need to be repeated. Consequently, human time is saved as well and scientists can concentrate on new studies. Students learn faster as they can access extensive reference data. Error or uncertainty estimates are possible and more robust when using well-documented databases. Further results not documented in publications are available in the uploaded data. Studies that were designed for a specific target can now be used for a different topic (repurposing). After receiving a digital object identifier (DOI), uploaded data become citable. This also applies to analytics tools. Although the full potential of FAIRmat will require a larger community to realize and join, the spirit of findable and AI-ready research data has already attracted substantial attention.

FAIR data infrastructure for materials science

FAIRmat will build a federated infrastructure of many domain-specific data-repository solutions: as few as possible but as many as are needed. In NOMAD 6 , such individual repositories are called ‘oases’ and support the different users’ local, domain-specific, individual needs to acquire, manage and analyse their data. An oasis is a stand-alone service typically connected to a central server, called the portal, but can also be run independently. As such, it is being tested at present as a building block of FAIRmat’s federated data infrastructure.

All participating groups or institutions will manage their data using the FAIRmat frame, a common compute, management and storage concept, with a central metadata repository. To enable 4V data processes, ‘federated data with centralized metadata’ will be the general principle. Selected data may also be stored centrally, if it is functionally beneficial for users or increases the availability of high-value datasets (see Fig. 2 ).

The left axis is the volume in bytes. As part of the research process, acquired data are filtered, analysed and refined. This generally produces smaller and smaller datasets of higher value. Owing to the expected data volume, the same level of availability for all data cannot be guaranteed. Therefore, data are categorized into four tiers with different levels of availability, from not shared at all (tier 0) towards published with a DOI (tier 3).

The portal will be the gateway for users to access all materials science data. Although popular search engines such as Google search for phrases in generic and mostly text-based properties (domain agnostic), we need to search for precise criteria in materials-science-specific metadata with their individual scientific notations and semantics. Thus, FAIRmat searches are domain-aware.

We will implement a common schema for all FAIRmat metadata and data. However, the data properties that are available for a given type of data differ from method to method and from domain to domain. There will be subsets of common properties for each subdomain, and these subsets form a hierarchy. For example, experiments and synthesis share a common notion of material, measurement or sample. This includes tagging samples with RFID (radio-frequency identification) or QR (quick response) code labels that are linked to every dataset acquired from them. On top of this hierarchy, and even outside the materials science domain, we will always have Dublin Core-style ( https://dublincore.org/ ) metadata about who, where and when.

This bottom-up harmonizing of metadata from different subdomains requires the development of data converters and a shared data schema. This will provide more flexibility when connecting many laboratories and new subdomains than top-down forced adoption of a new data format.

This hierarchy of common properties will also form the basis for exploring all materials science data. Similar to an online shop that allows customers to browse different categories of product, with varying criteria depending on the type of product, the central user interface will allow one to browse different subdomains of materials science on the basis of varying availabilities of data properties. On top of this, one may specify general properties, such as a material’s chemical composition and a scientific method. Then, more criteria will be made available. In this way we will design a common encyclopedia that supports the specific needs of the various materials science subdomains but will also provide more general information to non-experts.

Offering convenient tools for data analysis is an overall goal of FAIRmat. An example is the NOMAD Artificial Intelligence Toolkit ( https://nomad-lab.eu/AIToolkit ). At present, it provides several Jupyter Notebooks, some of them associated with a publication. It is recommended that researchers publish their AI analysis as well or modify or advance existing notebooks for their studies. Uploaded notebooks can obtain a DOI so that they are citable. As some data files will be huge, and may be distributed across several servers and cities, the analysis software will use the centralized metadata and extract the needed information from the (huge) data files. For the latter, we will bring the software to the data, avoiding the transfer of large files.

Other critical issues are long-term and 24/7 data availability (especially in a federated network), safety and security (especially when dealing with published versus unpublished data), data lifecycle (for example, from raw instrument readings to fully analysed and published datasets), linking data between domains, annotating data with a common user identity (for example, through ORCID; https://orcid.org ) and more.

FAIR, reproducible synthesis

Synthesizing materials with well-defined properties in a reproducible fashion is of utmost importance to materials science. Unfortunately, this desire is not always fulfilled because it requires control of a large number of experimental details, and the full entirety of the relevant parameters is typically not known. The concept of data-centric science and the development of AI tools promise to model synthesis more reliably and to identify the relevant set of descriptive parameters and their mutual interdependencies, or at least their correlations. Linking synthesis data to data from experimental materials science and theory using common metadata schemas and ontologies will create a new level of the science of materials synthesis.

Publicly accessible databases such as Landolt-Börnstein/Springer Materials ( https://materials.springer.com ) and the Inorganic Crystal Structure Database ( https://icsd.products.fiz-karlsruhe.de ) contain huge numbers of entries on the properties of crystalline materials but they lack information on their synthesis. Recently, work on the basis of machine-learning and natural-language-processing techniques has started to codify materials synthesis conditions and parameters that are published in journal articles 23 . The auto-generated open-source dataset at Berkeley ( https://ceder.berkeley.edu/text-mined-synthesis ) consisted of 19,744 chemical reactions retrieved from 53,538 solid-state synthesis paragraphs by March 2021. However, typically, this information is incomplete, and published information is biased towards reports of successful studies, omitting failed attempts.

This unsatisfactory situation is rooted in the complexity of the synthesis processes, including elaborate workflows and a large diversity of instruments for characterization. In the realm of FAIRmat, we follow the relevant phase transformations that occur during synthesis from the melt, from the gas phase, from solid phases and from solution. Synthesis by assembly complements these classical approaches. The nature of the assembly method is very different, as collective behaviour gives rise to new properties, such as the formation of aggregates or self-assembly. Figure 3 depicts the variety of crystal growth methods. Even though Czochralski, Bridgman, metal flux growth and optical floating zone are all melt-growth techniques—that is, they belong to the same type of phase transition—they are distinguished by the contact of the melt with the crucible, by the seeding of the single crystal and by thermal gradients, with great influence on crystallinity and impurity content. But even fine details matter. For example, the geometry of the reactors, fluctuations in the impurity content of the source material, the flow of precursors in the reactor or the miscut and pretreatment of substrates in epitaxial growth may have detrimental effects. At this point, synthesis is often based on experience and tricks, which are not readily shared with others. Obviously, this makes the development of metadata schemas and ontologies a formidable task and—with respect to the four Vs—synthesis struggles mainly with variety and veracity.

Simplified sketches of the five most common crystal growth methods from the melt (Czochralski, Bridgman, optical floating zone and metal flux growth) and the gas phase (chemical vapour transport).

We started to establish metadata and ontologies following the above-mentioned phase transformations. To connect to the other experimental disciplines (for example, sample characterization) we aim at a common ELN scheme and laboratory information management system (LIMS) and uniquely identify the samples, as noted in the infrastructure section. Thereby, we link the measured physical and chemical properties of a specimen to the synthesis workflow. The ELN and LIMS data are automatically fed into a prototype repository that is now being developed at the Leibniz Institute for Crystal Growth ( https://ikz-berlin.de/ ).

Once a structured database on synthesis has been established, this will allow computer-aided development of synthesis recipes to fabricate as yet unknown materials with tailored properties. Moreover, it will enable comparison of different synthesis methods for the same material in terms of generalized physical and chemical parameters, also linking them to theoretical predictions.

FAIR data in experimental disciplines

Experimental materials science is concerned with the characterization of the atomic and electronic structures of compounds, as well as with determining their electrical, optical, magnetic, thermal or mechanical properties. Typically, terabytes (sometimes petabytes) of data from one study result in a few plots in a publication. Only FAIR data management of all results, both successful and failed, makes experimental studies reproducible and obviates the necessity to repeat the experiments for a different but related project. In addition, by making all these data available to the community, everyone will benefit from statistically more reliable quantification of measurement errors and calibrations.

In experimental materials science, the variety of characterization methods is very diverse, and each class of methods has its own equipment and workflows for generating data. The diversity in data formats specific to vendors, labs, instruments, communities and operators presents a substantial challenge with regard to integrating this information into a FAIR infrastructure. For the initial period, we concentrate on five experimental techniques (see Fig. 4 ) with very different frontiers in terms of the 4V challenges, and largely disjunct and differently structured communities. These are electron microscopy and spectroscopy, angle-resolved photoemission spectroscopy, core-level photoemission spectroscopy, optical spectroscopy and atom-probe tomography. The amount of generated data ranges from a few kilobytes to terabytes per dataset, and the data rates and data structures also differ substantially. With some modern detectors delivering several gigabytes of data per second, the volume and velocity challenge is to preprocess, compress and evaluate or visualize these data. This becomes a more severe velocity issue in time-resolved experiments, for which the duration may not even be fixed but being decided during the observation. Disturbingly, overall, we observe a lack of efficient and reliabe recording of metadata in a digital form, posing a severe data-veracity challenge.

Optical spectroscopy, atom-probe tomography, angle-resolved photoelectron spectroscopy, electron microscopy and X-ray photoemission spectroscopy are noted explicitly, and the large number of other experimental methods is indicated by the empty ‘perspectives’.

Analogously to establishing FAIR data in synthesis, a strong focus is the customization of inter-operating ELNs and LIMSs, their integration into experimental workflows and their direct connection to the data repository.

In each of the five selected experimental techniques, activities have also started to define domain-specific metadata catalogues and ontologies. In some labs (transmission electron microscopy and spectroscopy), a first, rudimentary prototype of a NOMAD Oasis has recently been installed, with the aim of exploring how it should be further developed towards the requirements of the different subdomains. Integration of ELNs into the experimental workflow is at different stages of development, ranging from first implementation concepts in atom-probe tomography to a working integration of an ELN database with the data acquisition software in some transmission electron microscopy labs. This also includes the tagging of samples with QR labels and automated linking of sample IDs with links to experimental data and time-stamped notes generated by the data acquisition software. Several angle-resolved photoemission spectroscopy groups are reorganizing their labs at present, switching from paper lab books to ELNs. In this context, we note that, in a joint effort of different labs, we were able to make vendors of complex equipment reconsider their previously restrictive and closed data-format policies.

FAIR theory and computations

Materials modelling, in particular, including digital twins, is enjoying ever-growing attention thanks to a timely combination of hardware and algorithmic developments 24 . The NOMAD Laboratory 6 has already implemented a materials data infrastructure for quantum-mechanical ground-state calculations and ab initio molecular dynamics (see the summary in the section on ‘Data-centric materials science’). However, materials modelling also requires force fields and particle-based methods, to capture larger length scales and longer timescales (see Fig. 5 ). The implementation of such multiscale materials data infrastructure faces several outstanding challenges 25 , 26 . By considering trajectories, we need to account for both instantaneous and ensemble properties. Also, the heterogeneity of simulation setups, solvers, force fields and observables requires an ambitious and coherent strategy to make multiscale modelling FAIR. The development of metadata for this field has only just started.

Approaches range from excited states, ground-state density functional theory and force-field-based molecular dynamics to continuum. The data infrastructure has to meet the needs of the different methods. These are outlined by the equations at the bottom.

Another crucial task is the response of matter to external stimuli. The physical objects of interest obtained from theory are excitation energies and lifetimes, electronic band gaps, dielectric tensors, various excitation spectra and ionization potentials, all of which have experimental counterparts. The leading methodologies 27 comprise time-dependent density functional theory, Green function techniques and dynamical mean-field theory, implemented in a huge number of different computer codes. The predicted FAIRmat infrastructure will foster the often incomplete documentation at present and facilitate benchmarking and curation of results.

Concerning the four Vs, the area of theory and computation is severely affected by variety (that is, the heterogeneity of the meaning of the produced data). This refers to the fact that there are many physical equations, even more algorithms and yet more approximations that are implemented in the numerous, very different software packages. Although ab initio computational materials science has largely assumed a common nomenclature, for example, for the several hundred exchange-correlation approximations (see, for example, Libxc, a library of exchange-correlation and kinetic energy functionals for density functional theory; https://tddft.org/programs/libxc/ ), this is not yet the case for force fields, dynamical mean-field theory or calculations of fluid dynamics, and so on.

Related to the variety challenge is veracity. Note that we differentiate between accuracy and precision; the latter can be checked by comparing results from different software that addresses the same equations and uses the same approximations. Although for ab initio computational materials science the first important steps have been made 28 , for other theoretical approaches, such efforts are still missing. Accuracy, in turn, refers to the equations and basic approximations (for example, the exchange-correlation functional used or the force field). Here, error bars are largely missing so far, but for interoperability with experimental results, such error estimates need to be developed. Concerning the data volume, for molecular dynamics calculations, it is hardly possible to store all the information—that is, the detailed time evolution of the positions of all the atoms (if these are several thousand in number, as in force field studies) or the electronic charge density (in ab initio studies). Here, selection and compression strategies will be developed.

Making the data revolution happen

Fourteen years after Jim Gray’s explication on data-intensive scientific discovery 29 , materials science is still dominated by the first three research paradigms: experiment, theory and numerical simulations 2 . However, there is now wide consensus that data-centric research and the fourth paradigm (data mining, new ways of analysis (largely by AI) and visualization) will change, if not revolutionize, the sciences. We stress that the fourth paradigm represents a new way of thinking 2 . It complements but does not replace the previous concepts and approaches. Implementation of this paradigm not only creates new opportunities but also enhances the traditional approaches through efficient data exchange, better documentation and a more detailed understanding of what other groups are doing. This will open new horizons for research in the basic and engineering sciences, reaching out to industry and society.

So, let us summarize what we need to make the data revolution happen in materials science:

Hardware for data storage and handling, advanced analytics and high-speed networks. The availability of appropriate hardware is the basic prerequisite for building the described data infrastructure. We also need middleware, for example, for the efficient exchange of data that are created in or by different digital environments. In addition, efficient, near-real-time data analytics will also require advanced hardware, as well as software and hardware co-design.

Development and support of software tools. New tools are already being invented; for example, for fitting data, removing noise from data, learning rules that are behind patterns in data and identifying ‘statistically exceptional’ data groups 16 . With such rules, one will also identify ‘materials genes’—physical parameters that are related to the processes that trigger, facilitate or hinder a certain materials property or function. FAIRmat will foster the international coordination of the development of such tools in the wider materials science community.

The development of ELNs and LIMSs. Such necessary changes of current scientific procedures seem minor if one accepts that it is good scientific practice to document the experimental (or computational) conditions and the results in full detail, so that studies are reproducible. Thus, data collection (including the comprehensive characterization of the experimental setup) should become as automatic as possible. This sounds like an outdated request, but it has not been executed properly so far and, for data-centric science, it is essential. Unfortunately, for some—maybe many—studies, an immediate realization is not fully possible and even the first approximation requires a ‘phase transition’. Owing to the complexity of the field, there is no one-size-fits-all solution.

Close collaboration between experts from data science, IT infrastructure, software engineering and the materials science domain as equal partners. In FAIRmat, this will be realized by a centralized hub of specialists at the Physics Department of the Humboldt-Universität in Berlin.

Changing the publication culture and advancing digital libraries. As noted above, the basic scientific requirement of reproducibility of experimental work is often lacking. This is rooted in the complexity and intricacy of materials synthesis. FAIRmat will change this situation. The concept of ‘clean data’—that is, data that are comprehensively annotated—is being developed (see ref. 30 and references therein). This is much more elaborate than it sounds, and publications that ‘just’ present and describe such data comprehensively should be appreciated by the community as much as a standard publication in a high-impact journal.

Digital libraries have been built and advanced over the past decade, and this work continues. Although there have been ample developments in the field of life sciences, the situation in materials science is less advanced. However, it is improving (for example, at https://tib.eu/ or https://openaire.eu/ ) and, in this field, metadata catalogues are typically too unspecific to allow the identification of suitable datasets (for example, for AI analysis).

The German National Research Data Infrastructure project ( https://nfdi.de ) promotes all of the points discussed above, with the exception of the necessary hardware. Although a national effort, it is obviously part of an international activity, and FAIRmat has established respective collaborations already. We will support scientists and confirm them in their responsible handling of research data, and we will strive to educate the next generation of researchers and engineers to actively engage in order to achieve these goals in a timely manner.

The field is changing and the research community seems mostly convinced about the direction of this change, but it is still mostly in the role of an observer. If active scientists don’t sign on, the infrastructure will develop without them. Then, in a few years, they will need to accept what is there, and it may—unfortunately—not fully serve their needs. The consequences of the whole endeavour may be summarized as follows: the predicted changes brought about by a FAIR data infrastructure will not replace scientists, but scientists who use such an infrastructure may replace those who don’t.

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Acknowledgements

This work received funding from the Max Planck Research Network BiGmax, the Humboldt-Universität zu Berlin and the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 951786, the NOMAD CoE. We thank S. Auer for his feedback on digital libraries, V. Coors for her thoughtful work on the figures and the entire FAIRmat team for shaping the concept and first steps towards its implementation. FAIRmat is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project 460197019.

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Matthias Scheffler, Christoph T. Koch, Markus Scheidgen & Claudia Draxl

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Matthias Scheffler & Claudia Draxl

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Martin Aeschlimann

Leibniz-Institut für Kristallzüchtung, Berlin, Germany

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Max-Planck-Institut für Polymerforschung, Mainz, Germany

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Contributions

C.D. (chairperson of FAIRmat) and M. Scheffler (co-chair) created the general concept, led the writing and edited the whole paper, in close discussion with all the other authors. They also wrote the abstract, introduction, ‘Data-centric materials science’ and ‘Making the data revolution happen’ sections. M. Albrecht and C.F. took the lead on the section ‘FAIR, reproducible synthesis’ (Area A of FAIRmat); M.G. and C.T.K. on the section ‘FAIR data in experimental disciplines’ (Area B of FAIRmat); M. Scheffler., K.K. and T.B. on the section ‘FAIR theory and computations’ (Area C of FAIRmat); and H.-J.B., M. Scheidgen and W.E.N. on the section ‘FAIR data infrastructure for materials science’ (Area D of FAIRmat). C.D., together with C.W. and A.G. (Area E of FAIRmat) and M. Scheffler and M. Aeschlimann (Area F of FAIRmat), created the section ‘Preparing the research of tomorrow’. All authors carefully read and edited the whole paper.

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Scheffler, M., Aeschlimann, M., Albrecht, M. et al. FAIR data enabling new horizons for materials research. Nature 604 , 635–642 (2022). https://doi.org/10.1038/s41586-022-04501-x

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News | July 14, 2022

Great exploration revisited: new horizons at pluto and charon.

July 14, 2022 -- Seven years ago today, NASA's New Horizons spacecraft made history with the first up-close exploration of the Pluto system – providing breathtaking views and detailed data on Pluto and its largest moon, Charon, revealing the surfaces of these distant, mysterious worlds at the outer reaches of our solar system.

These simulated flights over Pluto and Charon include some of the sharpest images and topographic data that New Horizons gathered during its historic flyby on July 14, 2015. These are the first “movies" of Pluto and Charon made from the highest-resolution black-and-white image strips, taken by New Horizons' Long Range Reconnaissance Imager (LORRI), as the spacecraft zipped by at more than 30,000 miles per hour.

“These new high-resolution flyover videos are incredible," said New Horizons Principal Investigator Alan Stern, of the Southwest Research Institute. “They aren't just scientifically valuable, but they are also engaging, which is why we want to share them with the public. Enjoy flying over a planet named Pluto and its giant moon Charon, both more than three billion miles from Earth!"

“These new high-resolution flyover videos are incredible. They aren't just scientifically valuable, but they are also engaging, which is why we want to share them with the public." - Alan Stern, New Horizons Principal Investigator

Features as small as about 230 feet (70 meters) are visible on Pluto's icy, rocky surface. Moviemaker and New Horizons science team member Paul Schenk, from the Lunar and Planetary Institute, used high-resolution topographic mapping analysis to show surface relief in the nitrogen-laden ice sheet in the Sputnik Planitia impact basin – half of Pluto's famous “heart" feature.

This simulated flight starts near the center of the ice sheet and ends on the rugged ice-carved southeastern rim of the basin 300 miles (500 kilometers) away, where the difference between the highest and lowest points is more than 2 miles (3.5 kilometers). Also prominently visible are the small pits that cover the surface of the otherwise low-relief ice sheet. Schenk also added color data from New Horizons' Multispectral Visible Imaging Camera (MVIC) to bring out the reddish hues in Pluto's highlands.

The simulated Charon flyover starts in the low-lying, icy volcanic plains of Vulcan Planitia and ends in fractured northern plains some 300 miles (500 kilometers) away. Prominently visible are several mountains that rise about 1.5-2.5 miles (3-4 kilometers) above the volcanic plains. The images in this narrow strip show surface details as small as about 450 feet (140 meters) across.

The Johns Hopkins Applied Physics Laboratory in Laurel, Maryland, designed, built and operates the New Horizons spacecraft, and manages the mission for NASA's Science Mission Directorate. Southwest Research Institute, in San Antonio and Boulder, Colorado, directs the mission via Principal Investigator Stern, and leads the science team, payload operations and encounter science planning. New Horizons is part of the New Frontiers Program managed by NASA's Marshall Space Flight Center in Huntsville, Alabama. The Lunar and Planetary Institute in Houston is operated by USRA under a cooperative agreement with NASA's Science Mission Directorate.

- Story by the New Horizons team, Johns Hopkins University Applied Physics Laboratory

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Far beyond Pluto: What's next for NASA's New Horizons probe?

Seven years after its epic Pluto flyby, New Horizons is still going strong.

NASA's New Horizons spacecraft flew by Pluto seven years ago, but the probe's work is far from done.

New Horizons is still on duty in extended mission mode, diving ever deeper into the Kuiper Belt to examine ancient, icy mini-worlds in that vast region beyond the orbit of Neptune.

New Horizons launched in January 2006 and carried out a reconnaissance study of Pluto and its moons in the summer of 2015, culminating in a close flyby of the dwarf planet on July 14, 2015. That encounter revealed Pluto to be an incredibly diverse world, complete with towering water-ice mountains and huge plains of exotic nitrogen ice.

But the nuclear-powered probe kept its eyes open even after Pluto was in the rear-view mirror.

Destination Pluto: NASA's New Horizons mission in pictures

Primitive object

New Horizons next flew by Arrokoth , a small Kuiper Belt object (KBO), on Jan. 1, 2019. Arrokoth, which the New Horizons science team discovered in 2014 using the Hubble Space Telescope , is the most distant and most primitive object ever explored up close by a spacecraft.

And there could be another flyby in New Horizons' future as well.

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At a meeting of NASA's Outer Planets Assessment Group (OPAG) in June, New Horizons principal investigator Alan Stern, of the Southwest Research Institute (SwRI) in Colorado, related that both the spacecraft and its scientific payload are entirely healthy. The probe's lifetime is presently limited only by its nuclear fuel supply, which is likely sufficient to keep New Horizons flying through 2040.

And NASA recently granted another mission extension for New Horizons, which will keep the spacecraft going through 2025.

"I am very excited about this second extended mission," Stern told Space.com. NASA and the New Horizons team are discussing budget numbers for fiscal year 2025, he added.

Main action items

Now on the New Horizons agenda are a trio of main action items, as approved by NASA. One involves looking for another flyby target "and also more KBOs that we can study, not up close, but in the distance," Stern said.

In addition, New Horizons is still transmitting the last bytes of data gathered during the Arrokoth flyby in 2019. 

"We got delayed in that, mostly because the Deep Space Network had some upgrades. They took antennas down, and one was down for a year," Stern said. "We've got roughly 90% of the Arrokoth flyby [data] on the ground, but we want everything, and that takes time. So that's a significant activity."

Then there's the centerpiece of New Horizons' second extended mission — a diversity of observations across a variety of fields.

"While we are flying across the Kuiper Belt ," added Stern, "we are going to be doing a very interdisciplinary mission in all the space sciences — astrophysics, planetary science and heliophysics. We're going to use this spacecraft to do things that really cannot be done except if you have a spacecraft out there. There's really never been anything like this … We're doing all three different space sciences by making New Horizons an observatory for all three purposes."

For example, in heliophysics, the spacecraft will study "pickup ions." These charged particles dominate the pressure of the outer heliosphere — the huge bubble of magnetic fields and particles that the sun blows around itself — and control where the boundary with the interstellar medium is situated.

In astrophysics, New Horizons will study the cosmic optical and ultraviolet background, getting a nice view beyond the obscuring dust and other scattered light sources of the solar system's inner regions. New Horizons has already produced the most sensitive measurements of these backgrounds to date, with "deep implications for cosmology," Stern noted.

In the planetary science column, the probe is slated to study Uranus and Neptune from unique "high phase angle" geometries, shedding light on the important energy balances of those planets.

"There's never been anything really deeply interdisciplinary like New Horizons is going to become for this next three years in extended mission," said Stern.

Related: NASA's New Horizons Pluto spacecraft is still exploring, 50 AU from the sun

Machine learning

The New Horizons team also plans to obtain time on ground-based telescopes such as Keck and Subaru to find new KBOs to study as the probe zips by them, "or if you get lucky and pass one that's close enough we could get to, we'll have a close flyby," Stern said.

The New Horizons team is employing machine learning to hunt for new KBOs using such scopes. "It turns out it is faster, more accurate, more reliable," said Stern. Weighed against human sleuthing, machine learning "is better and finds more KBOs. So that's a breakthrough and saves us a lot of work and turns out a better product."

Ground-based observations have shown that there are different classes of KBOs that have different colors and compositions. "So we know there's a lot of heterogeneity among the KBOs," Stern said. "If we had a flyby of a second KBO, I would not expect the same thing at all. It would be a completely different place than Arrokoth."

— New Horizons: Exploring Pluto and beyond — NASA celebrates New Horizons' historic 2015 Pluto flyby with awesome new videos — Solar system planets, order and formation: A guide

Building blocks

KBOs teach scientists about planetesimals, the building blocks of planets thought to exist in protoplanetary disks and debris disks.

The Arrokoth KBO flyby yielded a breakthrough result, Stern said: That at least some planetesimals formed very gently, in a process called a local cloud-collapse phenomenon. The New Horizons team would love to study another KBO up close, to see if its formation and evolution match what was observed at Arrokoth.

"We're turning this into a machine that does good for astrophysics and heliophysics while it's doing good for planetary science," Stern said about New Horizons and its second extended mission. "They are equal partners in science, and that is a first for a planetary science mission."

Leonard David is author of the book "Moon Rush: The New Space Race," published by National Geographic in May 2019. A longtime writer for Space.com, David has been reporting on the space industry for more than five decades. Follow us on Twitter @Spacedotcom or on Facebook .  

Join our Space Forums to keep talking space on the latest missions, night sky and more! And if you have a news tip, correction or comment, let us know at: [email protected].

Leonard David is an award-winning space journalist who has been reporting on space activities for more than 50 years. Currently writing as Space.com's Space Insider Columnist among his other projects, Leonard has authored numerous books on space exploration, Mars missions and more, with his latest being "Moon Rush: The New Space Race" published in 2019 by National Geographic. He also wrote "Mars: Our Future on the Red Planet" released in 2016 by National Geographic. Leonard  has served as a correspondent for SpaceNews, Scientific American and Aerospace America for the AIAA. He has received many awards, including the first Ordway Award for Sustained Excellence in Spaceflight History in 2015 at the AAS Wernher von Braun Memorial Symposium. You can find out Leonard's latest project at his website and on Twitter.

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Human cardiac and other organoids have recently emerged as a groundbreaking tool for advancing our understanding the developmental biology of human organs. A recent paper from Sasha Mendjan’s laboratory published in the journal Cell on December 7, 2023, reported the generation of multi-chamber cardioids from human pluripotent stem cells, a transformative technology in the field of cardiology. In this short highlight paper, we summarize their findings. Their cardioids remarkably recapitulate the complexity of the human embryonic heart, including tissue architecture, cellular diversity, and functionality providing an excellent in vitro model for investigation of human heart development, disease modeling, precision medicine, and regenerative medicine. Thus, generating cardioids is an important step forward for understanding human heart development and developing potential therapies for heart diseases.

In the realm of medical research, scientists are constantly pushing the boundaries of innovation to unlock a deeper understanding of complex organs and their associated diseases. Thanks to the revolutionary understanding of embryonic development and tissue homeostatic maintenance, scientists are taught to culture and replicate the architecture and function of human organs at a small scale in vitro, which are termed organoids (Lancaster and Knoblich 2014 ). These tiny, self-organized three-dimensional structures not only mimic the architecture, cellular composition, and some functions of an organ but also offer an unprecedented opportunity to reveal the intricacies of organ development, disease progression, and organ regeneration.

So far, the emerging organoids vary from brain organoids that mimic the complexity of the human brain's neural networks to gut organoids that replicate the structure and functionality of the intestines. Similarly, liver organoids offer insight into liver diseases and drug toxicity testing, while kidney organoids provide a unique model for understanding renal development and disease (Lancaster and Huch 2019 ). In spite of these advances in different organoids, cardiac organoids, which resemble both the structure and regular pacing waves and contractions of the heart, have only been reported in recent years (Hofbauer et al. 2021 ; Schmidt et al. 2023 ).

Mendjan and colleagues recently established a human cardioid which contained the atrium, left ventricle (LV), right ventricle (RV), outflow tract (OFT), and atrioventricular canal (AVC) (Schmidt et al. 2023 ). This all-in-one cardioid recapitulates, for the first time, the structures of each heart chamber, mirrors cardiac contractions and calcium transients in vivo. Briefly, the authors described their remarkable experimental design, starting from culturing different subtypes of cardioids, and then fusing them together into a final multi-chamber cardioid. But generating these cardioids was not simple. During heart development, the first heart field (FHF) primarily gives rise to the LV, the anterior second heart field gives rise to the RV and most of the OFT, and the posterior second heart field gives rise to most of the atrium and a portion of the AVC. By temporally modulating several key cardiogenic signaling pathways such as ACTIVIN, bone morphogenic protein, fibroblast growth factor, retinoic acid, insulin, transforming growth factor-b, and WNT (wingless-related integration site), they first developed three main cardiac progenitor lineages and then differentiated them into five subtypes of cardioids that were enriched in mature cardiomyocytes (Fig.  1 A). Transcriptomic analysis of these cardioids revealed gene signatures that corresponded to the LV (IRX3, IRX4, HEY2 and MYL2), RV (ISL1, IRX1, HEY2 and RFTN1), atrium (TBX5, NR2F2 and NR2F1), OFT (WNT5A, ISL1, HAND2 and RSPO3), and AVC (TBX2 and TBX3). In the further maturation and functional specification progress, the LV/RV cardioids elevated ventricular structure protein MYL2, chamber marker NPPA and NPPB, and the MYH7/MYH6 ratio resulting in paralleled sarcomere structures and higher contraction amplitude. Moreover, the OFT cardioids displayed more efficient smooth muscle cell (SMC) differentiation propensity and AVC cardioids comprised a few PECAM + or COL1A1 + cells. These physical traits accurately reflected functional capabilities. And in their 3D differentiation protocol, they particularly noted that precise cell counting before patterning-1 aggregation was essential for robust cardioid formation since a higher cell density always led to inefficient cardiomyocyte differentiation and neural marker expression within the organoid core.

Schematic of experimental design in differentiation and fusion of cardioids. A Differentiation protocol of cardioids subtypes. B Experimental design of multi-chamber cardioid generation. C Experimental design of shared-lumen multi-chamber cardioid generation

To determine the similarity of cellular and molecular characteristics of these cardioids to human cardiogenesis in vivo, the authors carried out single-cell RNA sequencing (scRNA-seq) of these cardioids and made a comparative analysis with published scRNA-seq datasets from human embryonic hearts. They found remarkable similarities of cardioids to human ventricular and atrial cardiomyocytes. Functionally, cardioid automaticity had a greater extent of contractions in the LV, atrium and AVC, while the RV and OFT had a weaker capacity to spontaneously contract. Moreover, this automaticity decreased in the LV, RV and OFT cardioids by a loss of HCN4 K + /Na + channel expression. In addition, Ca ++ transients and action potentials of each cardioid revealed a distinct identity. The LV cardioids had a prolonged Ca ++ transient and a more homogeneous signal propagation field potential spread compared with the atrial and AVC cardioids as reported in vivo. Patch-clamp analysis of single cardiomyocytes revealed that the action potential duration (APD) in the atrial/AVC cardioids was shorter than in the RV cardiomyocytes, which was consistent with primary human cardiomyocytes. Overall, the fetal-like electrochemical signaling of cardioids recapitulates well the human heart biology.

To generate a multi-chamber cardioid, the authors applied both cardiac progenitor sorting and co-developing cardioid strategies (Fig.  1 B). The former strategy enabled the self-sorting of different cardiomyocytes but did not result in a multi-chamber cardioid. By co-developing cardioids in the latter strategy, they were able to generate an all-in-one cardioid, which achieved structural connections and maintained distinct identities and compartments of each cardioid. Interestingly, they noted that cardioids co-develop only when combined at day 3.5, were electrochemically coupled, and contracted in a coordinated manner by day 6.5. Remarkably, if they substituted all the cavity structures with respective cardiac progenitors on the second day of culture (LV at day 1.5; RV at day 3.5; atrium at day 3.5), they surprisingly generated cardioids with a shared lumen (Fig.  1 C). The Ca ++ propagation in these cardioids was not unidirectional at the beginning, but it became gradually dominated from the atria in pacing after 3 days in culturing. Thus, this multi-chamber cardioid provides a great platform for mimicking human heart development, disease modeling, and drug screening. For example, they generated cardioids from each of the mutants ISL1, TBX5 or FOXF1 hiPSCs, and found similar cardiac chamber-specific defects as those in clinical congenital heart diseases. In addition, they observed the expected effects of ion channel drugs on either single or multi-chamber cardioids. These results made this multi-chamber cardioid a powerful tool when combined with genome editing to dissect cellular mechanisms of congenital heart diseases and carry out high-throughput drug screening for clinical translations. Together, these data suggest that cardioids generated in this work faithfully recapitulate human heart structures and physiology, which serve well for studying both basic cardiogenesis research and clinical translation in the future.

Conclusions

This work reports the first multi-chamber cardioids with fetal-like identity of cardiomyocytes and have distinct features of cardiomyocytes from different chambers. These cardioids have beating waves and unidirectional Ca ++ transients initiated from the atria as in the normal human heart. Their molecular markers and ion channel genes of cardiomyocytes in these cardioids have striking similarity to human cardiogenesis in vivo. Therefore, this cardioid offers a credible human development platform for genome editing, disease modeling, therapeutic drug screening, and regenerative medicine. While having these remarkable advances, current protocols for generating cardioids are quite complex and warrant further investigations, particularly from different laboratories. Cell-type diversity and advanced cardiac modeling such as trabeculation, septation, circulation, ballooning, and coronary vasculature are the next milestones to achieve. Nevertheless, like other organoids, generating cardioids in this work represents a great in vitro model for understanding both human heart development and improving cardiovascular healthcare in the future.

Availability of data and materials

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Hofbauer P, Jahnel SM, Papai N, Giesshammer M, Deyett A, Schmidt C, Penc M, Tavernini K, Grdseloff N, Meledeth C, et al. Cardioids reveal self-organizing principles of human cardiogenesis. Cell. 2021;184(12):3299-3317 e3222. https://doi.org/10.1016/j.cell.2021.04.034 .

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Acknowledgements

The authors thank Prof. Iain Bruce (Visiting Professor at Peking University) for his comments on this manuscript and funding supports from the National Key R&D Program of China (2018YFA0800501) and the National Natural Science Foundation of China (32230032).

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Junjie Hou, Ye-Guang Chen & Jing-Wei Xiong

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Hou, J., Chen, YG. & Xiong, JW. Unveiling new horizons in heart research: the promise of multi-chamber cardiac organoids. Cell Regen 13 , 10 (2024). https://doi.org/10.1186/s13619-024-00193-y

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Scientists push new paradigm of animal consciousness, saying even insects may be sentient

Bees play by rolling wooden balls — apparently for fun . The cleaner wrasse fish appears to recognize its own visage in an underwater mirror . Octopuses seem to react to anesthetic drugs and will avoid settings where they likely experienced past pain.

All three of these discoveries came in the last five years — indications that the more scientists test animals, the more they find that many species may have inner lives and be sentient. A surprising range of creatures have shown evidence of conscious thought or experience, including insects, fish and some crustaceans.

That has prompted a group of top researchers on animal cognition to publish a new pronouncement that they hope will transform how scientists and society view — and care — for animals.

Nearly 40 researchers signed “ The New York Declaration on Animal Consciousness ,” which was first presented at a conference at New York University on Friday morning. It marks a pivotal moment, as a flood of research on animal cognition collides with debates over how various species ought to be treated.

The declaration says there is “strong scientific support” that birds and mammals have conscious experience, and a “realistic possibility” of consciousness for all vertebrates — including reptiles, amphibians and fish. That possibility extends to many creatures without backbones, it adds, such as insects, decapod crustaceans (including crabs and lobsters) and cephalopod mollusks, like squid, octopus and cuttlefish.

“When there is a realistic possibility of conscious experience in an animal, it is irresponsible to ignore that possibility in decisions affecting that animal,” the declaration says. “We should consider welfare risks and use the evidence to inform our responses to these risks.”

Jonathan Birch, a professor of philosophy at the London School of Economics and a principal investigator on the Foundations of Animal Sentience project, is among the declaration’s signatories. Whereas many scientists in the past assumed that questions about animal consciousness were unanswerable, he said, the declaration shows his field is moving in a new direction.

“This has been a very exciting 10 years for the study of animal minds,” Birch said. “People are daring to go there in a way they didn’t before and to entertain the possibility that animals like bees and octopuses and cuttlefish might have some form of conscious experience.”

From 'automata' to sentient

There is not a standard definition for animal sentience or consciousness, but generally the terms denote an ability to have subjective experiences: to sense and map the outside world, to have capacity for feelings like joy or pain. In some cases, it can mean that animals possess a level of self-awareness.

In that sense, the new declaration bucks years of historical science orthodoxy. In the 17th century, the French philosopher René Descartes argued that animals were merely “material automata” — lacking souls or consciousness.

Descartes believed that animals “can’t feel or can’t suffer,” said Rajesh Reddy, an assistant professor and director of the animal law program at Lewis & Clark College. “To feel compassion for them, or empathy for them, was somewhat silly or anthropomorphizing.”

In the early 20th century, prominent behavioral psychologists promoted the idea that science should only study observable behavior in animals, rather than emotions or subjective experiences . But beginning in the 1960s, scientists started to reconsider. Research began to focus on animal cognition, primarily among other primates.

Birch said the new declaration attempts to “crystallize a new emerging consensus that rejects the view of 100 years ago that we have no way of studying these questions scientifically.”

Indeed, a surge of recent findings underpin the new declaration. Scientists are developing new cognition tests and trying pre-existing tests on a wider range of species, with some surprises.

Take, for example, the mirror-mark test, which scientists sometimes use to see if an animal recognizes itself.

In a series of studies, the cleaner wrasse fish seemed to pass the test .

The fish were placed in a tank with a covered mirror, to which they exhibited no unusual reaction. But after the cover was lifted, seven of 10 fish launched attacks toward the mirror, signaling they likely interpreted the image as a rival fish.

After several days, the fish settled down and tried odd behaviors in front of the mirror, like swimming upside down, which had not been observed in the species before. Later, some appeared to spend an unusual amount of time in front of the mirror, examining their bodies. Researchers then marked the fish with a brown splotch under the skin, intended to resemble a parasite. Some fish tried to rub the mark off.

“The sequence of steps that you would only ever have imagined seeing with an incredibly intelligent animal like a chimpanzee or a dolphin, they see in the cleaner wrasse,” Birch said. “No one in a million years would have expected tiny fish to pass this test.”

In other studies, researchers found that zebrafish showed signs of curiosity when new objects were introduced into their tanks and that cuttlefish could remember things they saw or smelled . One experiment created stress for crayfish by electrically shocking them , then gave them anti-anxiety drugs used in humans. The drugs appeared to restore their usual behavior.

Birch said these experiments are part of an expansion of animal consciousness research over the past 10 to 15 years. “We can have this much broader canvas where we’re studying it in a very wide range of animals and not just mammals and birds, but also invertebrates like octopuses, cuttlefish,” he said. “And even increasingly, people are talking about this idea in relation to insects.”

As more and more species show these types of signs, Reddy said, researchers might soon need to reframe their line of inquiry altogether: “Scientists are being forced to reckon with this larger question — not which animals are sentient, but which animals aren’t?”

New legal horizons

Scientists’ changing understanding of animal sentience could have implications for U.S. law, which does not classify animals as sentient on a federal level, according to Reddy. Instead, laws pertaining to animals focus primarily on conservation, agriculture or their treatment by zoos, research laboratories and pet retailers.

“The law is a very slow moving vehicle and it really follows societal views on a lot of these issues,” Reddy said. “This declaration, and other means of getting the public to appreciate that animals are not just biological automatons, can create a groundswell of support for raising protections.”

State laws vary widely. A decade ago, Oregon passed a law recognizing animals as sentient and capable of feeling pain, stress and fear, which Reddy said has formed the bedrock of progressive judicial opinions in the state.

Meanwhile, Washington and California are among several states where lawmakers this year have considered bans on octopus farming, a species for which scientists have found strong evidence of sentience.

British law was recently amended to consider octopuses sentient beings — along with crabs and lobsters .

“Once you recognize animals as sentient, the concept of humane slaughter starts to matter, and you need to make sure that the sort of methods you’re using on them are humane,” Birch said. “In the case of crabs and lobsters, there are pretty inhumane methods, like dropping them into pans of boiling water, that are very commonly used.”

This article was originally published on NBCNews.com

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Arts & Science Researchers Awarded New NYU Grants to Pursue Advances in Human Health

Seven Arts & Science researchers received grant funding as part of NYU's newly-established Discovery Research Fund for Human Health . This inaugural grant awarded $1.2 million to 16 projects across the University in three distinct categories: planning, early stage research, and technology acceleration and commercialization. The Discovery Research Fund for Human Health received 56 grant proposals across 11 NYU schools and awarded funding to four Arts & Science initiatives. With these grants, A&S faculty are exploring neural disease, extensive nerve damage, insecticide resistance, concussion assessment, and epilepsy diagnosis with the goal of finding advanced, affordable, and accessible treatment methods.

An Early Stage Research Award was granted to “mRNA Regulations at the Single-Cell Level: the Case of FMRP,” which will investigate the role of mRNA in brain development and disorders to better understand neural diseases. The PI on this project is Silver Professor of Biology and Neural Science at NYU Arts & Science Claude Desplan , whose other research examines stochastic choices, the brain's optic lobe, and the neural pathway for motion vision. Co-PIs on this project include Director of the Center for Neural Science and Professor of Neural Science Eric Klann and Postdoctoral Fellow in the Desplan Lab, Ryan Loker .

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UCR getting ready to launch a new kind of research center

Researchers will receive stable funding and support to pursue ambitious projects

UC Riverside aims to offer researchers a chance to collaborate and pursue ambitious projects with centralized funding and administrative support through the first Campus Interdisciplinary Research Center.

Provost and Executive Vice Chancellor Elizabeth Watkins and Rodolfo Torres, vice chancellor for Research and Economic Development, announced the creation of the new center in February, inviting applications with a May 1 deadline.

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Meanwhile, there’s more news about Pluto.

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The Annual Humanities Horizons Lecture was established in 2013 to provide a significant contribution to reflection on and advocacy for the Arts and Humanities.

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Main features and contamination of sealed soils in the east of Moscow city

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• Published: 27 October 2021
• Volume 44 , pages 1697–1711, ( 2022 )

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• Elena M. Nikiforova   ORCID: orcid.org/0000-0003-2377-9072 1 ,
• Nikolay S. Kasimov   ORCID: orcid.org/0000-0002-1106-1808 1 ,
• Natalia E. Kosheleva   ORCID: orcid.org/0000-0002-7107-5718 1 &
• Ivan V. Timofeev   ORCID: orcid.org/0000-0001-8817-1231 1  

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The aim of this paper is to characterize the main properties and level of pollution of sealed soils in different land use zones of the Eastern administrative district (EAD) of Moscow. In 2016–2017 overall, 47 samples were taken from 35 soil pits. The list of soil properties analyzed included actual acidity, organic carbon content, particle-size distribution, and degree of salinity. Pollution of sealed soils with petroleum products (PPs), benzo[a]pyrene (BaP) and heavy metals and metalloids (HMMs) was evaluated. Sealed soils are characterized by the medium organic matter content (2.24%), alkaline reaction (pH 8.0), sandy loamy texture, and the absence of soluble salts in the upper part of the profile. The pronounced technogenic anomalies of hydrocarbons are mainly formed in the sealed soils of the industrial and traffic land use zones. The mean content of BaP in the sealed soils is 56 times higher than that in the background soils, it exceeds MPC by 9.5 times. The concentrations of most HMMs in the sealed soils exceed the background level by two–four times. The most intense accumulation of As, Ba, Cr, Cu, Ni, Pb, Sb, and Sn takes place in the industrial zone with the high degree of sealing. The hygienic standards for BaP and PPs contents approved in the Russian Federation in the sealed soils of EAO are exceeded by almost ten times. Maximum permissible concentrations are also exceeded for a large group of HMMs. The high contamination of the sealed soils can create dangerous ecological situation in the EAD if road covering will be removed and pollutants begin to migrate.

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Influence of the soil sealing on the geoaccumulation index of heavy metals and various pollution factors.

Acknowledgements

The authors are grateful to undergraduates of the Geographical Faculty of Moscow State University E.V. Shestova and A.G. Tsykhman for their participation in field studies and chemical analyses of the samples and an assistant professor of the Faculty of Geography M.A. Smirnova for a consultation. This study was supported by the Russian Science Foundation. Field and analytical works were performed within the framework of the project no. 14-27-00083, data analysis and interpretation – within the Project No. 19-77-30004.

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Nikiforova, E.M., Kasimov, N.S., Kosheleva, N.E. et al. Main features and contamination of sealed soils in the east of Moscow city. Environ Geochem Health 44 , 1697–1711 (2022). https://doi.org/10.1007/s10653-021-01132-5

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EdUHK Signs MoU with Moscow City University to Expand Global Academic and Research Footprints

The Education University of Hong Kong (EdUHK) recently signed a memorandum of understanding (MoU) with Moscow City University (MCU), marking a significant milestone for both universities in expanding their global academic and research footprints, primarily in the fields of education and science.

Presiding over the signing ceremony were EdUHK President Professor John Lee Chi-Kin and Rector of MCU Dr Igor Remorenko .    Both universities will develop joint fundamental and applied research, and interact on innovation. The new agreement will also enable joint research initiatives and the development of collaborative educational programmes, through the establishment of network alliances. Academic staff and students will have the opportunity to participate in exchange programmes, allowing them to broaden their horizons and contribute to a vibrant academic environment on both campuses. The agreement also includes regular exchange of academic publications and journals, as well as joint academic events, with a view to fostering intellectual exchange and facilitating a global academic dialogue.

In looking forward to this new chapter of collaboration, Professor Lee said, “I envisage that the partnership will bring mutual benefits to students and staff from EdUHK and MCU, as well as the wider academic community.”

Dr Remorenko added, “Signing this MoU marks a significant milestone in the pursuit of deepened collaboration between the two universities, as we pave the way to synergise our expertise and resources.”

EdUHK signs MoU with Moscow City University

EdUHK President Professor John Lee Chi-Kin (third from left) with Rector of MCU Dr Igor Remorenko (third from right)

EdUHK Establishes Global Research Institute for Finnish Education

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UGA breaks ground on new medical education and research building

The groundbreaking was a "transformational moment at the University of Georgia"

The University of Georgia broke ground Friday on a new medical education and research building that will significantly expand teaching and research capabilities at the university’s future School of Medicine .

Located on UGA’s Health Sciences campus, preliminary plans for the building include medical simulation suites, standardized patient rooms, clinical skills labs, a gross anatomy lab, and a medical library. The building will also feature student support spaces like conference rooms, study spaces, lounges, and faculty and staff offices dedicated to student support.

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Gov. Brian Kemp speaks at the groundbreaking ceremony for the Medical School Building on the Health Sciences Campus. (Andrew Davis Tucker/UGA)

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“Today is an exciting and transformational moment at the University of Georgia,” said UGA President Jere W. Morehead. “As a land-grant university and Georgia’s flagship research institution, the University of Georgia is uniquely positioned to address the health care needs of our state through world-class medical education, research and community outreach.”

Following the recommendation of Governor Brian Kemp, the Georgia General Assembly passed a fiscal year 2024 amended budget that includes $50 million in funding for a new University of Georgia School of Medicine facility.

President Jere W. Morehead speaks along with USG Chancellor Sonny Perdue and Gov. Brian Kemp at the groundbreaking ceremony for the Medical School Building on the Health Sciences Campus. (Andrew Davis Tucker/UGA)

The $50 million in state funding will be matched by private contributions to fund the $100 million medical education and research building.

The University System of Georgia Board of Regents authorized the University of Georgia to establish a new independent School of Medicine in Athens in February.

In March, Dr. Shelley Nuss was named founding dean of the UGA School of Medicine. She previously served as an associate professor of internal medicine and psychiatry in the Augusta University/University of Georgia Medical Partnership. In 2016, she was named campus dean of the Medical Partnership, which has been educating physicians in Athens since 2010.

“The fact is, Georgia needs more doctors, and we need them now,” said Nuss. “The new UGA School of Medicine will increase the number of medical students in the state, translating to more practicing physicians to help address Georgia’s greatest health care challenges.”

The creation of the UGA School of Medicine marks the natural evolution of the longest-serving medical partnership in the United States. Similar programs founded around the same time have already transitioned to independent medical schools.

USG Chancellor Sonny Perdue speaks from the podium along with Gov. Brian Kemp at the groundbreaking ceremony for the Medical School Building on the Health Sciences Campus. (Andrew Davis Tucker/UGA)

UGA will continue to work closely with the Medical College of Georgia to ensure a smooth transition for current medical students as UGA seeks accreditation from the Liaison Committee on Medical Education (LCME).

The development of a new public school of medicine at UGA promises to help address a significant shortage of medical professionals. Georgia’s growing population tops approximately 11 million residents, straining the state’s existing medical infrastructure.

Now the nation’s eighth largest state, Georgia is forecasted to experience further population growth in the coming years, and nearly one-third of the state’s physicians are nearing retirement.

“Georgia is growing,” said Sonny Perdue, chancellor of the University System of Georgia. “We may only be only eighth today, but in just a few short years Georgia could be the fifth largest state. And that means we are going to need more health care, and people are going to get it here and across the state.”

Founding Dean of the School of Medicine Shelley Nuss, middle, is surrounded by medical students at the groundbreaking ceremony for the Medical School Building. (Andrew Davis Tucker/UGA)

Georgia currently ranks No. 40 among U.S. states for the number of active patient care physicians per capita, according to the Association of American Medical Colleges (AAMC), while it ranks No. 41 for the number of primary care physicians and No. 44 for the number of general surgeons per capita. The shortage of medical providers is particularly acute in rural and underserved areas, where access is even more limited.

UGA faculty are already engaged in human health research, and the establishment of a school of medicine will bolster their efforts.

“Our flagship institution, the University of Georgia, is tasked with the vital mission of educating and preparing the next generation of leaders,” said Gov. Brian Kemp. “To that end, one of our top priorities is building a strong health care workforce pipeline. This UGA facility will be an essential part of those efforts.”

Alongside funding from state government, strong private support will fortify efforts to create a School of Medicine at UGA. Donors have demonstrated robust support for UGA initiatives in recent years. In fiscal year 2023, UGA raised over $240 million in gifts and pledges from alumni, friends and foundation and industry partners. The university’s three-year rolling fundraising average is now a record $235 million per year, with annual contributions exceeding $200 million for the past six consecutive years.

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