dataesr/scientific-paragraphs-categorization huggingface dataset:
1) Interpretation Whereas principle F1 enables unambiguous identification of resources of interest, principle F2 speaks to the ability to discover a resource of interest through, for example, search or filtering. Digital resources FAIR Principles: Interpretations and Implementation Considerations must be described with rich metadata -descriptors of the content of the resource referred to by that identifier. It is hard to generally define the minimally required "richness" of this metadata, except that the more generous it is, both for humans and computers, the more specifically findable it becomes in refined searches. While other principles speak to the specific kinds of metadata that should be included, principle F2 simply says that a digital resource that is not well-described cannot be accurately discovered.
2) Implementation considerations It is a challenge for each domain-specific community to define their own metadata descriptors necessary for optimizing findability. The minimal "richness" of the metadata should be defined so that it serves its intended purpose and should also be guided by the requirements of the other FAIR principles. This then poses a challenge to each community to create machine-actionable templates that facilitate capturing uniform and harmonized metadata about similar data resources among all community stakeholders, and to provide a means to ensure that this metadata is updated and curated  [17]. Examples of metadata schemata can be found in FAIRsharing   [18] [19] and include for instance the Data Documentation Initiative (DDI)  , the HCLS Dataset Descriptors  , and many domain-specific "minimal information" models that have been invented.
1) Interpretation Principle F3 states that any description of a digital resource must contain the identifier of that resource being described. For instance, the description of a computational workflow, should explicitly contain the identifier for that workflow in a manner that is unambiguous. This is especially important where the resource and its metadata are stored independently, but persistently linked, which is generally considered good practice in FAIR. The purpose of this principle is twofold. First, it is perhaps trivial to say that a descriptor should explicitly say what object it is describing; however, there is a second, less-obvious reason for this principle. Many digital objects (such as workflows, as mentioned above) have well-defined structures that may disallow the addition of new fields, including fields that could point to the metadata about that digital object.
2) Implementation considerations Current challenges are numerous, significantly limiting, and largely outside of the control of the average data provider. First, there is no single-source for search that currently indexes all possible metadata fields in all domains. Second, there is no uniform way to execute a search, and thus every search tool must be accessed with tool-specific software. Finally, many search engines forbid automated searches, precluding their use by FAIR-enabled software. Various initiatives are emerging that attempt to address this, at least in part, by providing a well-defined, machine-accessible search interface over indexed metadata. Nevertheless, to our knowledge, none of these currently index all possible metadata properties, nor do they span all possible domains/communities; rather, they focus on specific metadata schemas such as schema.
org, at the expense of other well-established metadata formats such as DCAT, and/or are limited to specific communities such as biotechnology, astronomy, law, or government/administration. Current choices are for each community to choose, and publicly declare, what search engine to use for their own purposes, general or field-specific, and should at a minimum provide metadata following the standard that is indexed by the search engine of choice. They should also provide a machine-readable interface definition that would allow an automated search without human intervention. An example of a generic searchable resource that supports manual exploration is Google Dataset Search  ; however, this suffers from several of the problems mentioned above, in particular, that it indexes only certain types of metadata (schema.
Principle A1: (meta)data are retrievable by their identifier using a standardized communications protocol 1) Interpretation A primary purpose of identifying a digital resource is to simultaneously provide the ability to retrieve the record of that digital resource, in some format, using some clearly-defined mechanism: hence the retrievability is a facet of FAIR Accessibility. Here, the emphasis is on "ability": there should be no additional barrier retrieval of the record by some agent when its access protocol (A1. 1) results in permitted access to that record. Note that the agent may be a machine working behind a firewall, if that agent has been permitted access. For fully mechanized access, this requires that the identifier (F1) follows a globally-accepted schema that is tied to a standardized, high-level communication protocol.
1). A requester can be a human or a machine agent. In the latter case it is probably a proxy for a human or an organization to which the authentication and authorization protocol should be applied, in which case, the machine should be expected to present the appropriate credentials. The principle requires that a FAIR resource must provide such a protocol, but the protocol itself is not further specified. In practice, an Internet of FAIR Data and Services cannot function without implementing Authentication and Authorization Infrastructure (AAI, see also  [22] ).
FAIR Principles: Interpretations and Implementation Considerations humans), but also means that machines cannot easily make use of digital resources, which is the primary goal of FAIR. For example, when a machine visits two data files in which a field "temperature" is present, then it will need more contextual descriptions to distinguish between weather data in one file and body temperature measurements in another. Achieving a "common understanding" of digital resources through a globally understood "language" for machines is the purpose of principle I1, with an emphasis on "knowledge" and "knowledge representation". This becomes critical when many differently formatted resources need to be visited or combined across organizations and countries and is especially challenging for interdisciplinary studies or for meta-analyses, where results from independent organizations, pertaining to the same topic, must be combined.
In this context, the principle says that producers of digital resources are required to use a language (i. e. , a representation of data/knowledge) that has a defined mechanism for mechanized interpretation -a machine-readable "grammar" -where, for example, the difference between an entity, as well as any relevant relationship between entities, is defined in the structure of the language itself. This allows machines to consume the information with at least a basic "understanding" of its content. It is a step towards a common understanding of digital resources by machines, which is a prerequisite for a functional Internet of FAIR Data and Services. Several technologies can be chosen for principle I1.
2) Implementation considerations Communities will have to choose an available technology or decide how they will otherwise deal with multiple representations and languages. In any case, they will have to make sure that each data item that is the same in multiple resources is interpreted in exactly the same way by every agent (human and computer), and that how items across resources relate to one another can be unambiguously understood by all agents  [25]. The key consideration in this regard is that FAIR speaks to the ability of data to be reused by a generic agent, rather than a community-specific agent. This is most easily accomplished by making the knowledge available in the most widely used format(s), even if this means duplication of the information in the community-specific format. The most widely-accepted choice to adhere to this principle, at the present time, is the Resource Description Framework (RDF) which is the W3C's recommendation for how to represent knowledge on the Web in a machine-accessible format  14.
2) Implementation considerations Current considerations are for communities to ensure that terminology systems and, for instance, the units of measure, classifications, and relationship definitions are themselves FAIR. Thesauri that are proprietary and not universally accessible should be avoided wherever possible, because machines (and indeed particular countries, regions or communities as a whole) may not have the authority to access their definitions, such that even data that is accessible after authentication via A1. 2 may not be useful to an agent that has no authority to access the concept definitions used within that data. Ontologies defined in the "Web Ontology Language" (OWL) and shared via a publicly accessible registry (e. g. BioPortal for life science ontologies  15 ) are examples of formally represented, accessible, mapped, and shared knowledge representations in a broadly applicable language for knowledge representation, that are also compliant with the Findability requirements of FAIR, since BioPortal provides a machine-accessible search interface.
1) Interpretation An important aspect of FAIR is that data or metadata, generally speaking, does not exist in a silo -we must do what is necessary to ensure that the knowledge representing a resource is connected to that of other resources to create a meaningfully interlinked network of data and services. A "qualified reference" is a reference to another resource (i. e. , referencing that external resource's persistent identifier), in which the nature of the relationship is also clearly specified. For instance, when multiple versions of a metadata file are available, it may be useful to provide links to prior or next versions using a named relation such as "prior version" or "next version" (preferably using an appropriate community standard relationship that itself conforms to the FAIR principles).
Such templates also reduce the burden on community members to deeply understand the highly structured PROV ontology, and the well-defined data structures that emerge from its use -that is to say, PROV should not be treated as a simple vocabulary from which terms can be selected, but rather as a model that constrains how those terms must be used in relation to one another. Several early tools are under development to make the construction of FAIR metadata easier, including for instance CEDAR  20 , CASTOR  21 and the knowledge models in the Data Stewardship Wizard  22  [24].
DISCUSSION The high level foundational principles of Findability, Accessibility (under well defined conditions), Interoperability (also across prior silos), which together enable the ultimate aim to enable trusted, effective and sustained Reuse of research resources are widely endorsed. However, the examples given in this paper already demonstrate that interpretation of the derived guiding principles for implementation is far from straightforward. For some implementation considerations there are already existing solutions, so communities can choose to reuse such solutions. The prerequisite is of course that these solutions are themselves FAIR, so that people (and machines) first of all know about them and can reuse them as solutions in their own implementations. In some cases, however, implementation of a component of the Internet of FAIR Data and Services has not been addressed before within a particular setting, and solutions developed in other settings may not (fully) suffice.
We therefore highly commend the efforts of communities and consortia such as the ESFRI scheme in Europe, the Innovative Medicines initiative, but also international organizations such as RDA, CODATA and GO FAIR to gently guide convergence based on community-emerging best practices. No-one ever said FAIR was easy, but we have to go through the hardship of making our resources FAIR to enable better science together. It benefits everyone to make it as easy as possible for communities to make steps in the direction of optimally achievable FAIRness in their domain. This obviously critically includes reuse of each other's solutions where possible. Initiatives such as FAIRsharing  [18][19]  are examples of attempts to support stakeholder communities in sharing and reusing FAIR solutions. Eventually, agreement of the FAIR implementation choices between different communities should lead to convergence  [4]  However, the question remains: convergence to what? This process will not lead to the ultimate goal of FAIR (optimal Reuse) unless we at  24 At the publication date of this article the original paper  [1]  had close to 1600 citations counted in Google Scholar.
Using "stick" based compliance incentives (e. g. , government health ministries or funding agencies that create FAIR certifications or requirements for funding) could prove a strong driving force towards convergence. However, this process needs to be guided and will not always occur spontaneously; not so much because communities do not want to reach convergence and hence interoperability, but because they are "too busy minding their own business". International coordination and a platform to address exactly that convergence process is needed. In actual practice, implementation choices and challenges should be known and will be implemented mainly by FAIR-aware data stewards, who ultimately work in the institutes or projects alongside those who are generating the data and metadata. Their choices should constitute a large part of the Data Stewardship Plans of researchers  [24].
ACKNOWLEDGEMENTS The work of  A. Jacobsen ,  C. Evelo ,  M. Thompson ,  R. Cornet ,  R. Kaliyaperuma  and  M. Roos  is supported by funding from the  European Union 's  Horizon 2020 research and innovation program  under the  EJP RD COFUND-EJP N° 825575. The work of  A. Jacobsen ,  C. Evelo ,  C. Goble ,  M. Thompson ,  N. Juty ,  R. Hooft ,  M. Roos ,  S-A. Sansone ,  P. McQuilton ,  P. Rocca-Serra  and  D. Batista  is supported by funding from  ELIXIR EXCELERATE ,  H2020  grant agreement number  676559. R. Hooft  was further funded by  NL NWO NRGWI. obrug. 2018. 009. N. Juty  and  C. Goble  were funded by  CORBEL  ( H2020  grant agreement  654248 ). N. Juty ,  C. Goble ,  S-A. Sansone ,  P. McQuilton ,  P. Rocca-Serra  and  D. Batista  were funded by  FAIRplus  ( IMI grant  agreement  802750 ).
N. Juty ,  C. Goble ,  M. Thompson ,  M. Roos ,  S-A. Sansone ,  P. McQuilton ,  P. Rocca-Serra  and  D. Batista  were funded by  EOSClife H2020-EU  (grant agreement number  824087 ). C. Goble was funded by  DMMCore  ( BBSRC BB/M013189 /). M. Thompson ,  M. Roos  received funding from  NWO  ( VWData 400. 17. 605 ). S-A. Sansone,  P. McQuilton ,  P. Rocca-Serra  and  D. Batista  have been funded by grants awarded to  S-A. Sansone  from the  UK BBSRC and Research Councils  ( BB/L024101/1 ;  BB/L005069/1 ),  EU  ( H2020-EU 634107 ;  H2020-EU 654241 ,  IMI  ( IMPRiND 116060 ),  NIH Data Common Fund , and from the  Wellcome.
FAIR Principles: Interpretations and Implementation Considerations Trust (ISA-InterMine  212930/Z/18/Z ;  FAIRsharing 208381/A/17/Z ). The work of  A. Waagmeester  has been funded by grant award number  GM089820  from the  National Institutes of Health. M. Kersloot was funded by the  European Regional Development Fund  ( KVW-00163 ). The work of N. Meyers was funded by the  National Science Foundation  ( OAC 1839030 ). The work of  M. D. Wilkinson  is funded by  Isaac Peral/Marie Curie  cofund with the  Universidad Politécnica de Madrid  and the  Ministerio de Economía y Competitividad  grant number  TIN2014-55993-RM. The work of  B. Magagna ,  E. Schultes ,  L. da Silva Santos  and  K. Jeffery  is funded by the  H2020-EU 824068. The work of  B. Magagna ,  E. Schultes  and  L. da Silva Santos  is funded by the  GO FAIR ISCO  grant of the  Dutch Ministry of Science and Culture.
The work of  G. Guizzardi  is supported by the  OCEAN  Project (FUB). M. Courtot received funding from the  Innovative Medicines Initiative 2 Joint Undertaking  under grant agreement No. 802750. R. Cornet was further funded by  FAIR4Health  ( H2020-EU  grant agreement number  824666 ). K. Jeffery received funding from  EPOS-IP   H2020-EU agreement 676564  and ENVRIplus  H2020-EU agreement 654182.
Sampling depths were 1,000-600, 600-300, 300-200, 200-100, and 100-0 m depth during all cruises. Temperature, salinity, chlorophylla and oxygen concentration were measured during concomitant profiles of a Seabird SBE 11plus CTD (conductivity, temperature, depth) equipped with dual oxygen sensors (calibrated during the cruises with discrete samples) and a fluorescence probe. Additionally, an Underwater Vision Profiler 5 (UVP5;  Picheral et al. , 2010)  was mounted on the CTD to measure the particle abundance and size distribution as well as the Trichodesmium sp. abundance. Nitrate was analyzed either on board or after storage at -20 • C after  Grasshoff et al. (2009). Furthermore, we analyze backscatter data from the vessel mounted 38 kHz Acoustic Doppler Current Profiler (ADCP). The regions targeted in this work are the area of the Cape Verde Ocean Observatory.
ADCP Data Analysis During the cruises M105, M106, M119, and M130 a 38 kHz RDI Ocean Surveyor (OS38) was mounted in the ship's sea chest and worked continuously. Depending on the region and sea state, the range covered by the instruments is around 1,000 m. The minimum size of particles that influences the sound scattering of the OS38 are 10-20 mm therefore, large copepods, euphausiids and small pelagic fishes contribute most to the backscatter amplitude recorded by the OS38. To investigate the vertical migration of the zooplankton the echo amplitude of the OS38 was transformed into volumetric backscatter Sv (dB)  (Mullison, 2017)  to correct the depth dependency of the data. Furthermore, Sv between 10:00 to 14:00 and 22:00 to 2:00 o'clock local time were selected for every 24 h and averaged in daytime and nighttime profiles.
At cOMZ particle flux between 400 and 600 m (about the target depth of DVM) derived from UVP5 data is simulated best by b = 0. 858; again, this value leads to an overestimate of oxygen between 400 and 600 m of about 20 mmol m -3. Only the model with a very steep particle flux profile (b = 0. 6435) matches the trap fluxes observed by  Engel et al. In this case, the model overestimates deep oxygen by ≈ 50 mmol m -3 at 400 m. Finally, at 5N observed particle flux is matched best by b defined by a range between 0. 858 and 1. 0725. The lowest value of b (comparable to faster settling particles) results in an overestimate of particle flux. Yet, as shown above this experiment could still produce a reasonable oxygen profile.
Applying such calculations is nevertheless necessary to convert the abundance and size estimates to biomass, fluxes and rates in SI units, which allows for comparison with other studies. The only parameter for which we could not find an allometric relationship is the gut flux. Here, we used data from  Schnetzer and Steinberg (2002)  to estimate gut flux at 1% of the migrating biomass. A general factor of 1% is not very satisfactory, as gut flux might vary according to composition and size distribution of the migrating community. The cited study was conducted at the Bermuda Atlantic Time Series Station, where the community composition may be different from the one we observed. We estimate the particulate matter supply via mortality of migrating organisms at depth by combining biomass and mortality estimates.
As we know the carbon flux into and out of the 300-600 m depth layer and the POC content (derived from the UVP5 data i. e. , for the particle size range 0. 14-26. 8 mm) of this layer, we can derive the needed active supply of particulate matter to maintain the IPM and counter the particle remineralization. The active flux needed can be calculated as remineralisation rate * POC content + Flux out -Flux in [see also Extended data Figure  9  from  Kiko et al. (2017) ]. The active flux we observe at cOMZ would fully support the IPM if the remineralization rate would be 2. 6% per day. Iversen and Ploug (2013)  find individual particle remineralization rates of about 0. 5-6% per day at 4 • C. Temperatures at 300-600 m depth in the ETNA are approximately twice as high, which should increase the remineralization rate by about a factor of 1.
, 2019)  our analysis suggests that parameterization of zooplankton and nekton diel vertical migration, its organic matter supply to the deep ocean and deep oxygen consumption can affect global and regional model performance. Comparison to our observations suggests that the NEMO/PISCES/APECOSM model still underestimates the effects, whereas the inclusion of DVM could help to improve the performance of MOPS in this region, and possibly elsewhere. Our comprehensive data set that includes zooplankton as well as particle concentrations and derived fluxes should help to further constrain such modeling efforts in terms of model parameterization, optimization, and evaluation.
) A first comparison of our POC-flux data to global biogeochemical model simulations indicates that the overall POC-flux estimates coincide but the contribution of DVMmediated fluxes seems to be lacking or to be underestimated. More appropriate parameterizations might improve the representation of the biological carbon pump and the global oxygen distribution. furthermore supported by the projects TRIATLAS and iAtlantic, which have received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 817578 and 818123, respectively. RK furthermore received a Make Our Planet Great Again grant of the French National Research Agency within the Programme d'Investissements d'Avenir; reference ANR-19-MPGA-0012.
ACKNOWLEDGMENTS We are grateful to the crew and chief scientists of the research vessels  Maria S. Merian  and Meteor as well as the colleagues collecting nutrient data on the various cruises. Pieter Vandromme obtained Multinet samples and UVP5 data during FS Meteor cruise M119. Furthermore, we would like to thank all our colleagues of the SFB754 who supported us, provided interesting feedback regarding our work and generated a motivating work environment. Lars Stemman  and  Marc Picheral  ( Laboratoire d'Océanographie de Villefranche-sur-Mer ) provided a UVP5 for cruises MSM22 and  M97  and supported the optical analysis. Technical support by Hydroptic for UVP5 deployments is greatly appreciated. A special thank you also goes to the GEOMAR data management team ( Hela Mehrtens ,  Carsten Schirnick  and colleagues) and  Christiane Schelten , the coordinator of the SFB754.
Single-crystal X-ray diffraction measurements The single crystal X-ray diffraction experiments were carried out at 100 K on a SuperNova dual wavelength microfocus diffractometer, equipped with a 135 mm Atlas CCD detector, using Mo-Kα radiation (λ=0. 71093 Å) for 1 and 3 and Cu-Kα for 2 and 4 (λ=1. 54184 Å). The data collection, reduction, and analytical absorption corrections were performed with the CrysAlis program suite  [25]. The crystals structures for the title compounds have been solved by direct methods and successive Fourier difference syntheses and refined by weighted fullmatrix least squares method against F 2 using SHELX suite  [26]. The corresponding structures are monoclinic and are described respectively in the P21/c (for 1), P21/n (for 2 and 3) and C2/c (for 4) spaces groups.