dataesr/scientific-paragraphs-categorization huggingface dataset:
ODATIS has a dedicated website 4  launched in December 2017 that already includes more than 120 data collections. The portal and associated services have a mirror version in French and English, the latter of which allows data reuse and interoperability. The ODATIS portal already offers several data access tools: a search service with selection filters, a data discovery service (with two options: "Preview" and "Complete"), a visualization service, and the possibility to download data directly or via local partner portals.
Governance of the French Ocean Cluster To fulfill its mission, ODATIS is organized to efficiently interact with partner institutions and the overarching Research Infrastructure (Data Terra) regarding strategic aspects, and with its DSCs and users regarding operational aspects (Figure  2 ). The ODATIS executive board is the interface between these two levels. It is composed of a management team and a representative of each DSC. The management team consists of the director, two assistant directors (scientific and technical), in situ and satellite technical officers, ad hoc project officers, and an editorial manager. This team has continuous informal interactions as well as three formal meetings a year of the executive board. There are three strategic levels: the Scientific Council (SC), the Data Terra executive board, and the Inter-Institution Steering Committee (IISC).
The Scientific Council, under the supervision of the scientific director, has the role of a scientific advisory board. Its members, appointed by the steering committee, are experts in marine sciences and representative of the user communities. The tasks of the SC are to help the executive board identify the scientific community's needs, check the scientific quality of the data and products generated by the DSCs, and assess the progress of the scientific consortia in order to propose a prioritization for the prototyping and production phases. If necessary, the SC formulates recommendations to the steering committee. The second level is the Data Terra executive board. Data Terra relies on thematic clusters that need to be harmonized to permit data interoperability and combination. It is essential that common editorial and technical choices are defined collegially.
ODATIS is also placed under the responsibility of the Inter-Institution Steering Committee, which is composed of one representative from each of the partner institutions. Lastly, the main prerogatives of the IISC are to define the strategy of the ODATIS cluster in order to achieve its objectives, particularly in terms of scientific policy and European and international positioning, to ensure that the needs expressed by the user community are taken into account, and to mobilize the human and financial resources necessary to develop ODATIS. At the operational level, ODATIS is based on a network of geographically distributed DSCs (see section "Data and Service Centres") operated by major French oceanic research organizations (CNRS, CNES, Ifremer, IRD, SHOM, Marine Universities). To coordinate the DSCs, ODATIS organizes two to three technical workshops every year to define the technical orientations of the cluster, or runs practical training sessions, for example, on handling and testing data visualization tools.
These workshops are mandatory for DSCs but are open to anyone who requests them within the limit of a reasonable number of participants, which is typically 15-20. Finally, ODATIS relies on scientific consortia in order to promote and develop innovative processing methods and products for space, airborne, or in situ observations of the ocean and its interfaces (atmosphere, coastline) with the other thematic clusters. A scientific consortium is a group of public or private experts that conduct research or develop innovative methods for mobilizing observation data, producing prototypes of products, or operating these prototypes to produce specific data on coastal and open ocean issues around thematic fields (physical, chemical, and biological processes, ecosystems, ocean/atmosphere exchange, global approaches, resources, etc.
) to meet the societal and environmental challenges of our time. The first consortium, the Dissolved Oxygen Scientific Expertise Consortium 5  , was implemented in 2019 in order to network and integrate scientific stakeholders at national and even international scales around the theme of deoxygenation of the offshore and coastal ocean and establish an exhaustive national database on oceanic dissolved oxygen. This work will be France's contribution to the international effort led by the IOC-UNESCO GO2NE and IOCCP network. Scientific consortia concerning other essential oceanic variables (carbon, salinity, color) and techniques (flow cytometry) are currently being implemented.
Data and Service Centers At present, there are nine DSCs, two of which are dedicated to satellite data and seven of which are dedicated to in situ datasets (Figure  2 ). In situ DSCs collect data from observations made via ships (or measured on samples in laboratories after the cruise), drifting, or moored systems. The tasks of a DSC are to record data, process data, control data quality, and provide routine access to marine data. Members of the ODATIS executive board and DSCs are involved with the coordination of several initiatives for marine data management from regional to international scales, including: • regional and national programs, in particular the long-term national observation services of the CNRS. This concerns, for example, coastal ocean and nearshore observations conducted under the umbrella of the French research Infrastructure for littoral and coastal observation services, named ILICO  (Cocquempot et al.
Finally, ODATIS, through its data centers, contributes to the Environmental Research Infrastructures (ENVRI) community, which includes all European Earth Observation infrastructures, both for observation and data management. ENVRI defines guidelines to be implemented for compliance with FAIR principles, in collaboration with working groups of the Research Data Alliance (RDA) (e. g. , metadata and catalogs, permanent identifiers such as DOI, FAIR controlled vocabularies). ODATIS benefits from all these collaborations. Common vocabularies (ontologies), metadata and data formats, and interoperability protocols have been adopted and adapted to this thematic field and should be implemented by all DSCs. In addition, as marine data are environmental data with geo-references, used in public policies such as the EU Marine Strategy Framework Directive (MSFD), the technical requirements of the INSPIRE directive must be applied  (INSPIRE, 2007).
IMPLEMENTATION OF FAIR AND TRUST PRINCIPLES The implementation of FAIR principles is heterogeneous across DSCs. Several DSCs are responsible for datasets that contribute to key international data networks and supra-national infrastructures (e. g. , ARGO, AVISO + (Archiving, Validation and Interpretation of Satellite Oceanographic data), SOCAT (Surface Ocean CO2 ATlas), GOSUD (Global Ocean Surface Underway Data), SeaDataNet (a pan-European infrastructure to ease the access to marine data measured by the countries bordering the European seas). These DSCs already meet many of the FAIR criteria. However, the large majority of DSCs do not meet FAIR data criteria with regards to their data handling, formatting, and compliance. This is related to the genesis of ODATIS, which was built on existing DSCs and can be extremely complex in terms of managed databases and services provided.
A diagnostic is that most observation datasets are archived and organized in databases deployed by the different DSCs; however, the coordination and harmonization of all ocean databases still needs to be implemented at the national level by ODATIS. At present, there is still huge heterogeneity in the databases between DSCs, mostly for those concerned with in situ data, which is a major obstacle for interoperability. In order to obtain support for upgrading in situ DSCs, the ODATIS management team replied to a recent national call for projects and was awarded the ANR COPiLOtE project (Toward the Certification of the Data and Service Centres of the Ocean Data Cluster 2020-2022), which began in April 2020. The main objective of COPiLOtE is to harmonize the implementation of FAIR principles within ODATIS in situ DSCs according to the tasks identified in Table  2.
The first step is an individual self-evaluation of each DSC using a questionnaire, with the help of the executive office, to assess its level of implementation of FAIR principles. Then, the approach will be implemented in two volunteer in situ DSCs. At the end of the project, the implementation guidelines will be transposable to all in situ DSCs of ODATIS, that is, those not involved in the COPiLOtE project and future DSCs that will emerge from the community. The second objective is to guide in situ DSCs toward certification in order to meet the Core Trustworthy Data Repositories requirements of the RDA CoreTrustSeal 9. The TRUST Principles (i. e. , transparency, responsibility, user focus, sustainability, and technology) provide a common framework to implement and maintain digital repositories  (Lin et al.
, 2020). Certification is important for ensuring the reliability and durability of data repositories, and hence the potential for sharing data over a long period of time, for both users and their funders. We do not expect all DSCs to obtain certification at the end of the project. For illustration, there is currently only a single French marine data repository that is currently certified (IFREMER-SISMER, see the list of certified repositories at the link: www. coretrustseal. org/why-certification/certifiedrepositories/). However, our aim is to motivate DSCs to engage in the process, based on the premise that certification will soon become mandatory in order to obtain the support of funding institutions.
DEVELOPING A CLASSIFICATION SYSTEM OF DATA CENTERS As described above, ODATIS relies on a network of nine existing DSCs. These centers are not evenly distributed across the French territory and are mostly located in West France (Brittany, South-West). There is a rising demand from all French laboratories involved in marine sciences to have local data centers. For example, the Mediterranean Institute of Oceanography (MIO) on the Mediterranean coast is one of the most important French oceanography centers, which could justify having a DSC. However, is it reasonable to increase the number of existing 9 www. coretrustseal. org/ DSCs? There is a limit to the number of centers manageable by ODATIS, in terms of the efficiency of management of a distributed structure, staff requirements, and IT and software investments.
Another difficulty involves the capacity to aggregate heterogeneous data (different volumes and storage centers) and therefore the ability to make data interoperable and FAIR at all levels. The challenge for ODATIS is now to develop a network that can cover the need to be close to the data producers, while providing cross-analytical tools for data from all Earth compartments for the end user. For this purpose, ODATIS and Data Terra are currently building a common strategy based on a classification system of data centers (Figure  3 ). The first category is the data assembly center (DAC). DACs are small laboratory-or institution-based data units working closely with the data providers and focusing on making their data FAIR. It is indeed a critical point for data quality to have proximity with the producer.
DACs will archive and enable access to the dataset through a persistent identifier (PI or PID), metadata, and common vocabulary. DAC repositories will be certified (CoreTrustSeal, ISO) to meet the repository criteria of scientific journals. DACs will also produce statistics on data usage. Such centers do not need large informatic resources and could be fairly widespread. However, there is a need to harmonize tools to optimize this service and ensure interoperability and Web harvesting. The second category is the DSCs themselves, which are larger national data hubs that aggregate data for larger collections and offer services specialized around at least one type of thematic data. A DSC has different levels of activities, assembles data such as DAC, is located as close as possible to the producer, stores data at least at the national level by theme (by aggregating, for example, thematic data from different DACs), and is a multi-thematic data center.
g. , ocean, land, atmosphere, biosphere, solid earth). The objective is to offer virtual research environments (VRE) with dedicated statistical and geostatistical tools, cartography, machine learning, or development environments (e. g. , Jupyter Notebooks, Pangeo suite). Such DIAS centers require substantial resources (e. g. , informatics, team), which are only conceivable at the level of the Data Terra research infrastructure. To date, most data available through the ODATIS portal are related to long-term observation networks, which have an automatic data workflow to acquire data directly from.
Structure level Executive office Data and services centers Step sensors (e. g. , Argo). It is even mandatory for accredited French observation networks to share data and metadata  (Cocquempot et al. , 2019). On the contrary, it is still complicated to collect data from a specific project or a cruise because of the diversity of data and acquisition delays. Some data are measured directly on board, but most samples are analyzed back at the laboratory. The need to help data producers share their data has already been mentioned; this task must be performed in addition to data acquisition. To make this task easier, it is possible to access the SEA scieNtific Open data Edition (SEANOE) 10 service through.
CONCLUSION The aim of this study was to provide information on the structure of ODATIS, the Data Terra cluster dedicated to French open ocean and coastal data, which could serve as a guideline for future national or thematic data repositories. The first challenge for ODATIS is to provide easier and wider access to FAIR marine data. This issue is crucial for addressing ongoing and future climate and environmental changes, particularly in coastal regions directly affected by human activities. Repositories already exist all over the world; some have abundant experience but many do not fully comply with FAIR data principles. A second challenge is to develop data analysis and interpretation services. Here, we have highlighted the different issues to consider when developing a strategy to guide DSCs toward certification and for defining a DSC classification system for optimizing human and IT costs with respect to the services offered to users.
The implementation of such e-infrastructure requires significant resources and time to develop the tools necessary for their implementation. Substantial personnel and IT resources are also required. Assistance to data producers is essential because it is at this first stage that the metadata associated with the datasets are defined, ensuring the development of FAIR repositories. ODATIS and its reference infrastructure Data Terra should not only organize the data life cycle based on the classification of DSCs, but should also consider the users. Such access to data from multiple sources (in situ, satellite, post-sampling analysis) and from different compartments is fairly new, and most colleagues are not yet accustomed to searching and manipulating such datasets, let alone remotely. In parallel to the informatic development of the ocean cluster of Data Terra, there is a need for further communication and training.
10 www. seanoe. org the ODATIS portal. SEANOE offers the possibility to publish scientific data in the field of marine sciences free of charge. Data are published as open access for a maximum of 2 years, for example, to restrict access to data of an article under review. The producer sets the terms and conditions for the use of the data by selecting one of seven Creative Commons licenses. SEANOE recommends the use of perennial data files (e. g. , CSV instead of Excel) and requests a description of the dataset (metadata). If the dataset matches the quality criteria and the theme (marine science), SEANOE provides a DOI within two working days and will monitor future dataset citations in articles, with download statistics reported to authors once a year. Data published in SEANOE are also automatically duplicated to the EMODnet Data Ingestion portal and, depending on their interest, may also be published via European data portals such as SeaDataNet or a thematic EMODnet portal.
Precision on primordial abundances, deduced from observations, have now reached the percent level for 4 He and deuterium. Precision on big bang nucleosynthesis (BBN) predictions should, hence, reach the same level. The uncertainty on the 4 He mass fraction is strongly affected by theoretical uncertainties on the weak reaction rates that interconvert neutrons with protons. All these corrections have been calculated in a self-consistent manner and implemented in a new, and public, Mathematica code PRIMAT [1], together with an extensive data base of reaction rates. Both can be obtained at.
The PRIMAT code We had developed a Fortran77 code (EZ BBN)  [7]  together with a network of reactions, including n, p, d, t, 3 He and α captures on nuclei up to the CNO region, with the most up to date nuclear input  [8] [9] [10] [11]. It has been adapted to investigate, beyond the standard model physics, like modified gravity  [12, 13] , variation of fundamental constants  [14] , or exotic neutron sources  [15] [16] [17]. We have recently developed a new code, PRIMAT (for PRImordial MATter),  [1]  in a Mathematica platform to more Table  I. Predictions compared to observations. Y p D/H×10 5  3 He/H×10 5  7 Li/H×10 10 Observations 0. 2449±0. 0040 [2]  2. 527±0. 030 [3]  <(0. 9-1. 3) [18] 1. 58±0. 31 [19]  EZ BBN  [9]  0. 2484±0. 0002 2. 45±0. 05 1. 07±0. 03 5. 61±0. 26 PRIMAT  [1]  0. 24709±0.
Reaction rates The PRIMAT package includes, besides a few reaction rates represented by analytical expressions within the code, a file where the other reaction rates are given in tabular format: BBNRatesAC2019. dat. Each carries also its reverse ratio so that the reverse reaction is also systematically included in the network. The file can be easily edited to accommodate alternative rates or even new reactions. It can also be used as a benchmark to compare different codes as most differences in BBN results come from the use of different reaction rates. The file was finalized at the end of 2017 and is expected to be updated by the end of 2019, in particular to take into account the following evolutions. • The D(p,γ) 3 He reaction is crucial for the calculation of the deuterium abundance as theoretical predictions of its rate differ significantly  [23, 24] , without sufficient experimental data to discriminate between them.
Ultrasound Stimulation of the ACC and BF Modulates Time to Act If the effect of contextual factors on actTime is mediated by the BF and ACC, then causal manipulation of these areas should modulate the relationship between contextual factors and act-Time. We collected a new dataset outside of the scanner, but this time we used TUS to modulate ACC and BF activity immediately before animals performed the task (STAR Methods). Each animal underwent four stimulation conditions, including two conditions of interest and two control conditions (Figure  7C ). Our two primary conditions were bilateral ACC TUS and bilateral BF TUS. We also chose two control conditions: passive bilateral sham control with the transducer positioned on the skull but without sonication and one active control with TUS targeted at the bilateral parietal operculum (POp), a region distinct from and anterior to intraparietal areas linked to sensorimotor decision-making  (Shadlen and Kiani, 2013) , in which activity was unmodulated by our contrast of interest or any other task event.
Cox regression model To estimate the deterministic component of actTime we used a specific class of survival models called the Cox proportional hazard model  (Murakami et al. , 2017). The model predicts time-to-event (actTime) on the current trial from present and past contextual factors. Specifically, the predictors (covariates) included reward magnitude, dot speed and ITI of the current trial, and the actual reward and actTime on the past 10 trials. Importantly, actTime in trials that monkeys decided not to respond are labeled as 'censored' data because in those trials monkeys might have responded if the trials were to continue for longer. The model is described as: lðtÞ = l 0 ðtÞ:expðbxÞ; where lðtÞrepresents a hazard function (hazard rate of responding), l 0 ðtÞ represents a baseline hazard function, that is a hazard function when all the covariates are 0, b is a row vector with 23 elements (3 present contextual factors + 10 past rewards + 10 past actTimes) representing Cox coefficients for each covariate and x is a 23 element column vector representing covariates, present contextual factors and contextual factors of the past 10 trials.
The coefficients were estimated for each testing session by using the 'coxphfit' function in MATLAB. We also investigated an alternative model where we introduced 'trial number' as a covariate to account for a potential effect of satiety on actTime; however, the Cox coefficient for trial number was not significantly different from zero. Moreover, its inclusion had a negligible impact on the proportion of variance in actTime explained by the other factors. This may partly reflect the relatively short duration of each testing session. A detailed method for obtaining Cox coefficients has been previously described  (Murakami et al. , 2017). The estimated Cox coefficients ð b bÞ from present and past contextual factors (only coefficients from the immediately preceding trial were used, as only these had been significant for both past reward outcome and past actTime) were used to obtain the expected actTime from the given predictors by the following method: The cumulative hazard function of each trial was then used to estimate the survival function of each trial, S(t): b S x ðtÞ = exp À À b L x ðtÞ Á ; The deterministic actTime is estimated by: ½actTime = Z N 0 b S x ðtÞ; We also separately assessed the contribution of past and present context to deterministic actTime.
Imaging data acquisition Awake-animals were head-fixed in a sphinx position in an MRI-compatible chair (Rogue Research, MTL, CA). MRI was collected using a 3T horizontal bore MRI clinical scanner and a four-channel phased array receive coil in conjunction with a radial transmission coil (Windmiller Kolster Scientific Fresno, CA). Each loop of the coil had an 8cm diameter which ensures a good coverage of the animal's head. Similar coils have been previously used for awake fMRI studies in primates  (Chau et al. , 2015; Kolster et al. , 2014; Papageorgiou et al. , 2017). The chair was positioned on the sliding bed of the scanner. The receiver coils were placed on the side of the animal's head with the transmitter placed on top. The touch sensors and the juice delivery system were the same as the one used in the mock scanner.
An MRI-compatible screen (MRC, Cambridge) was placed 30cm in front of the animal and the image was projected on the screen by a LX400 projector (Christie Digital Systems). Functional data were acquired using a gradient-echo T2* echo planar imaging (EPI) sequence with a 1. 5 3 1. 5 3 1. 5 mm resolution, repetition time (TR) 2. 28 s, echo time (TE) 30 ms and flip angle 90. At the end of each session, proton-density-weighted images were acquired using a gradient-refocused echo (GRE) sequence with a 1. 5 3 1. 5 3 1. 5 mm resolution, TR 10 ms, TE 2. 52 ms, and flip angle 25. These images were later used for offline MRI reconstruction. T1-weighted MP-RAGE images with a resolution of 0. 5 3 0. 5 3 0. 5 mm, TR 2. 5 s, TE 4. 04 ms, inversion pulse time (TI) 1. 1 s, and flip angle 8 , were acquired in separate sessions under general anesthesia.