dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental design a. Data In this study, we test the ability of the BFN-QG to dynamically interpolate altimetric observations by setting up twin experiments based on virtual observation data drawn from an ocean circulation model. First, we use a free model run that we consider as our ground truth [called Nature Run (NR)]. Second, the NR is used to generate synthetic altimetric observations from current (nadirs) and future (SWOT) observing systems. Then the synthetic observations are assimilated into the QG model by the BFN technique to produce gridded SSH maps that can be compared with the NR. The NR has been drawn from the NATL60 configuration (Molines 2018) of the Nucleus for European Modelling of the Ocean (NEMO) model. With its 1/608 horizontal resolution and 300 vertical levels over the North Atlantic basin, it is one of the most advanced basin-scale high-resolution simulation available today.
For a detailed study dealing with physical characteristics of NATL60, we refer the reader to the paper of  Ajayi et al. (2020). In short, these authors have shown that NATL60 exhibits a strong space-time variability in the size of small eddies (,100 km). The eddies are 2 times bigger in summer than in winter and their sizes vary geographical consistently with the latitudinal variation of the first Rossby radius of deformation. NATL60 has been used in several studies such as  Metref et al. (2019 Metref et al. ( , 2020))  The SSH field of this NR is subsampled by realistic synthetic satellite constellations, simulating both nadir (conventional) and SWOT altimetry. Existing simulator tools  (Gaultier et al. 2016 ) are used to generate the datasets. As a first baseline to represent current observing capabilities, we consider a constellation of four conventional nadir altimeters following the theoretical ground tracks of two interleaved Jason-like and Sentinel-3-like orbits.
The data are extracted with the SWOT simulator tool  (Gaultier et al. 2016 ) in its nadir mode, at an along-track resolution set to 6 km for each satellite. The data are considered perfect; i. e. , no source of measurement error is simulated. As a second main dataset of this study, we consider SWOTlike observations also generated with the SWOT simulator in its swath mode. The spatial resolutions are set to 2 km, both in the along-track and across-track directions, consistently with the anticipated resolution of the future SWOT products. The observations provided by the Nadir module (which will be part of the payload) are also simulated, at a resolution of 6 km. Here again, we do not consider any kind of measurement error. Yet it is clear that the measurement errors will considerably affect the quality of the signal.
c. Observational scenarios The mapping techniques (DUACS and BFN-QG) are run in the studied region, over three months, and testing three different observation scenarios: (i) four nadir altimeters (Nadirs), (ii) SWOT alone (SWOT) and (iii) nadir altimeters and SWOT concomitantly (Nadirs 1 SWOT). It is recalled that DUACS and BFN-QG algorithms are run with the same synthetic observations, presented in section 3a, to make the reconstructed fields comparable with each other. To anticipate the processing of the real SWOT data, possibly affected by strong spatially correlated errors, two other BFN-QG experiments are introduced where the SWOT data are assimilated by the relative vorticity component of PV. The SWOT correlated errors, to a large part specific to the satellite and instrument design  (Peral and Esteban-Fernandez 2018) , are particularly challenging for the mapping of altimetry because they can present significant amplitudes but cannot be easily filtered out using conventional filters.
5  to the SWOT-only scenario, and Fig. 6  to the joint nadir-SWOT scenario. As stated earlier, the two scenarios involving SWOT include two experiments that differ by the way SWOT is assimilated. In all five experiments, the BFN-QG performs better than DUACS, with one exception for the RMSE when only SWOT is assimilated with the relative vorticity formulation (Fig. 5 , top panels). The BFN-QG outperforms DUACS in the mapping of SSH from nadir altimetry only, and this outperformance is further increased when SWOT is introduced. For instance, the ER of the maps computed with nadir data (136. 7 km for DUACS and 101. 4 km for the BFN-QG) is improved by 26% with the BFN-QG in comparison to DUACS (Fig. 4 ) and this improvement reaches 33% when adding SWOT data (Fig. 6 ). This confirms the idea of  Ubelmann et al.
In the nadir 1 SWOT scenarios, the BFN-QG partially damps the time variations of the reconstruction errors in comparison with DUACS, although these variations remain significant. The time variations of the reconstruction errors are clearly visible on the RMSE plots. The increasing phases coincide with time periods without SWOT observations. During these periods, which last approximately 5 days, estimation errors grow due to imperfect dynamics (both QG for BFN-QG and linear interpolation for DUACS). In the SWOT-only scenarios, the error-variation amplitudes in DUACS and BFN-QG results are comparable. Figure  6  shows that the nadir data help to reduce this amplitude with the BFN-QG, more than with DUACS. The BFN-QG reconstructs structures at small scales with finer details than DUACS, as illustrated in Fig.
3. In particular, the discrepancies between DUACS and BFN-QG appear more significantly on j (depicting finer-scale structures) than on SSH. This difference in resolution is confirmed by the frequency-wavenumber spectral diagnostics. According to the FWPSDs, the BFN-QG can reconstruct smaller space and time scales than DUACS in general; in particular, when nadir and SWOT data are accounted for simultaneously, BFN-QG reaches scales of 80-100 km and 10 days. In all scenarios, the energy distribution of the DUACS reconstruction (upper-left panels of frequency-wavenumber PSD sections) is in agreement with the commonly known scales resolved by the maps  (Ballarotta et al. 2019) , around 120 km and 12 days. Above these scales (typically above 150 km and 15 days), the scores (right panels) indicate that all the reconstructions are reliable (blue color) with more signal than errors, except perhaps for the SWOT-only scenarios.
Conclusions This study aimed at testing the ability of a simple data assimilation technique [viz. , the back-and-forth nudging (BFN)] to map sea surface height (SSH) from realistic altimetric observations in the context of the future SWOT mission. The proposed mapping technique uses a 1. 5-layer QG model forced by observations of SSH. The model is gradually nudged toward the observations while running forward and backward within a temporal window in an iterative process so as to overcome the problem of time distribution of the observations. The performance of this mapping technique, dubbed the BFN-QG, has been evaluated and compared with the standard SSH mapping products DUACS, for different satellite constellations that include conventional nadir and SWOT altimetric data. The first conclusion of this work is that the BFN-QG is well suited to interpolate in space and time SSH provided by conventional nadir altimetric data.
This suggests that using a more complex dynamical model (e. g. , a two-layer QG model) could make this process even more efficient. Finally, if strong spatially correlated errors were to corrupt the SWOT measurement, the BFN-QG allows an alternative formulation that only nudges the relative vorticity derived from the SWOT data which should be less affected by correlated errors. In that context, the results show that this formulation produces relatively good quality SSH reconstructions under the crucial condition that the SWOT data must be combined with nadir data. Future works should expend this study by testing the BFN-QG with noisy synthetic SWOT data. Of course, many developments remain to be made before the efficient mapping of SSH from SWOT. Some have been suggested previously. The mapping methods must be tested with more realistic error-containing observations.
Even though, in many regions, IGWs should not be the dominating component of the signal at the scales reconstructed by the BFN-QG system, their impact on SSH reconstruction should be considered in further studies. For instance, an interesting idea would be to extend the BFN approach to IGWs by replacing the QG model with a gravity wavepermitting model. Another challenging development is how to extend dynamical mapping approaches such as BFN-QG to coastal and tropical regions. Once again, a possible development would be to replace the QG model with a model more appropriate to simulate the dynamics of these regions. One particular limitation of this task is that the employed reduced models have to be reversible to work with BFN. Zenodo platform with DOI 10. 5281/zenodo. 4045400 (https:// doi.
Data availability statement. The data used in this study can be accessed at the following URL: https://ige-meom-opendap. univ-grenoble-alpes. fr/thredds/catalog/meomopendap/extract/ LeGuillou/data_LeGuillou_2020/catalog. html. An SSH mapping data challenge, based on the experimental setup presented in this paper, is described here: https://github. com/ocean-datachallenges/2020a_SSH_mapping_NATL60 and identified on.
Data sharing benefits the researcher, the scientific community, and most importantly, the public by enabling more impactful analysis of data and greater transparency in scientific research. However, like many other scientists, the low-temperature geochemistry (LTG) community has generally not developed protocols and standards for publishing, citing, and versioning datasets. This paper is the product of a group of LTG and data scientists convened to strategize about the future management of LTG data. The group observed that the current landscape of sites for LTG -the data-scape --is a "street bazaar" of data repositories. This was deemed appropriate because LTG scientists target many different scientific questions and produce data with different structures and volumes described by copious and complex metadata.
Nonetheless, the group agreed that publication of LTG science must be accompanied by sharing of data in publicly accessible repositories. To enable this for sample-based data, samples should be registered with globally unique persistent identifiers. LTG scientists should be able to use both highly structured databases designed for specialized types of data or generalized, unstructured, and nontargeted data repositories. The group strategized that the overall data informatics paradigm should shift from "build data repository, data will come" to "publish data online, cybertools will find. " In other words, the most important need within the growing and complex data-scape is for increasingly powerful tools for searching and cross-referencing data across the proliferating data repositories. This strategy requires increasing emphasis on data science and data management for LTG professionals and students.
At the workshop, we recognized that some sub-sets of the LTG community have already selforganized their approaches to data management, sometimes initiating their own best practices for data management systems (e. g. Table  1 ). To enable conversation at the workshop between all the different sub-sets of the LTG community as well as the data informatics community, a short lexicon of terms was compiled (Table  2 ). We needed this lexicon because words were often used differently by domain scientists (geochemists) and information scientists, and often, words were used differently by different individuals within the information science community itself. Certainly, the lexicon was also helpful for communities who had yet to develop data management systems (e. g. , Table  3 ). This paper was written for both LTG practitioners and data scientists interested in helping in the management of LTG data.
The main questions at the workshop addressed data management and sharing from different perspectives. We focused on three areas. First, who are the different stakeholders interested in coordinated management of LTG data, and what does each of them want to achieve? To answer this question, we discussed what we perceive to be the characteristics of the optimal management system from the perspective of different stakeholders (e. g. , data producers, data users, modellers, funders, journal editors, government agencies, the public). Second, we asked, how can we best secure the longevity of data for the future given that a typical research project in LTG is only three years without possibility of renewal? In this regard we noted that data archived in older papers can still be read, while data in "aging" electronic peripheral devices such as floppy disks can only be read by specialty workers, emphasizing the importance of the type of media for storage and the resources available for data storage (e.
g. CHRISTENSEN et al. , 2009). Similarly, data stored within proprietary software may not be accessible in the future if the software changes or is not maintained. Finally, we looked at the question, what does the data life cycle look like today for LTG? We noted that many LTG practitioners only collect small volumes of data and publish it in papers, while others pursue meta-analysis of multiple datasets. This paper summarizes a roadmap for a future data-scape for LTG data, noting all these activities and usages.
Characteristics of LTG data Geochemical data are highly variable in terms of type, volume, structure, dimensionality, and character. The one trait that these data tend to share is that they provide chemical analysis or descriptions of features related to chemical makeup. But the structure of the data nonetheless varies from one dataset to another. For example, analyses can focus on the possible 100+ elements, the 200+ stable and radiogenic isotopes, 5000+ minerals, or the many thousands of fluid species (including organic molecules) that have been identified. In addition, new types of analyses are always being developed and refined. A schematic showing the variety in the different chemical analyses that might be made for one soil sample in a given landscape is shown in Figure  1. A few data characteristics are described below.
The volume of data associated with these datasets can be much, much larger than sample-based analytical data. Thus, whereas early datasets could be accommodated in a notebook, the larger data volumes can only be accommodated in online data systems (Figure  2 ). In contrast to sample-based data, LTG geochemists also collect time-series (sometimes referred to as longitudinal) or field-based measurements (taken without collecting a sample) of water, air, biota, and solids. Some of these time-series measurements are made by field workers, but increasingly, measurements are made with sensors (e. g. KIM et al. , 2017)  or remote sensing (e. g. BERATAN et al. , 1997). Temporal variations are measured in real-time or intermittently over long durations (e. g. BENSON et al. , 2010). Advances occurring in the technology of sensors and sensor networks are rapidly driving new types of data collection for water quality, soil and rock characteristics, air composition, and molecular biological properties.
Regardless of whether their measurements are sample-based, field measurement-based, or timeseries, LTG scientists place great stock in new types of analyses. The upshot of this is that many LTG papers summarize data that are purely research grade. As shown schematically in Figure  3 , these measurements are highly non-routine (one-of-a-kind or first-of-a-kind), in contrast to more established, routine measurements with accepted standards. Figure  3  emphasizes that, as innovation in the measurement protocol decreases from left to right, the ease of data management increases. Not all LTG observations will even be stored in a notebook, let alone in the sequentially more structured (higher) levels of data storage. Rather, only data that are scientifically useful will be stored in increasingly structured repositories.
Currently, LTG scientists need to store more data in online data repositories. Many of these will be generalized data repositories that provide flexibility in management of data and metadata. As datasets in the generalized data repositories grow in scientific importance or the demand for the data grows from users both internal or external to LTG, shared online relational databases may be developed and maintained for specific data types. Finally, in addition to these sample-, field-and time-based measurements, many geochemical "data" now increasingly consist of model outputs or calculations. One type of model output that is often thought of as data include measurements reported from instruments where manufacturers keep data processing protocols proprietary, leaving open access to raw data limited and sequestered behind a paywall limited to licensed users.
Other types of model output are also stored and used by geochemists. For example, global oceanic chemistry models used by oceanographers and geochemists can yield very large datasets of salinity or trace element content versus location. These models can include predicted data, so-called "re-analysis" data, model workflows, and model programs, and often the community wants to have access to all of these "data" sets  (KALNAY et al. , 1996). Given all of this variety in data types and model outputs, some LTG datasets are large in volume while others are very small. For example, model-related output "data" are commonly associated with very large "data" volumes, as are sensor or remote sensing data, both of which can provide high-spatio-temporal resolution. In contrast, many sample-based datasets may be relatively small in volume, at least partly because of the expense and time necessary to collect, prepare, sub-sample, and analyze (Figure  1 ).
Lack of best practices, standards, and harmonization for LTG data The variety of datasets and types of LTG research means that goals and workflows vary considerably from one team to the next. But the design of effective data repositories depends upon the goal of the data as well as the overall workflow for both data generation and data processing (e. g. RUEGG et al. ). As a result, even where many examples of a certain type of data have been collected, and even when they may be organized into online libraries, it is rare in LTG that there is a generally accepted standard for the data. For example, quantitative phase analysis of Earth materials, whether they are rocks, soils, sediments, or something else, is fundamental to LTG, and there are several libraries for such data (Table  1 ), but formats for sample preparation for X-ray diffraction, data collection, and meta-analysis have not been established within the community.
In another example, the team behind one NSFsupported geochemical data repository (EarthChem Library) emphasized the most common methods and sample types to develop templates for petrologists to submit rock chemical data. When the EarthChem team tried to use the same template for other communities beyond petrology, they were met with resistance because the non-petrologists preferred templates tailored to their own workflows. As a consequence of the many types of workflows, many practicing LTG scientists report they find data and metadata protocols from highly standardized data repositories to be difficult to implement for their own datasets. It is important to emphasize that the variety of workflows that characterize LTG is not just a consequence of competing egos or laboratories. The different workflows result from groups asking different questions.
For example, soil scientists and geologists collect and analyze soils to pursue questions within LTG. But the former analyze only the <2mm fraction (because it impacts soil fertility the most) while the latter use the entire sample for analysis (because they use the value in mass balance calculations). Thus, for routine analyses of many different types of soils, the National Cooperative Soil Survey (NCSS) database (N. R. C. S. , 2020) is useful because all the soils have been sieved in the same way before an analysis, but this database is not necessarily useful for mass balance calculations pursued by geologists (BRIMHALL AND DIETRICH, 1987). In another example, many in-vitro analytical methods have been developed to assess the health impact and bioaccessibility of contaminants in dust particles in the human lungs  (WISEMAN, 2015).
These protocols differ significantly from analyses aimed to understand leachability in environmental systems  (PICKERING, 1981). Another reason for the lack of agreement on standards and protocols of measurement and reporting data results from LTG practitioners' strong emphasis on development of new or nonstandardized technique -for example in sampling methodology, chemical extraction, analytical technique, and laboratory protocol. This emphasis results in a lack of data standards, difficulty in creating templates for data or metadata input, and ultimately, difficulty in comparing datasets within the LTG community. Here, data standards are defined as policies or protocols that determine how geochemical data and metadata should be formatted, reported, and documented. Many LTG scientists have not heard of nor used standards such as the Observations and Measurements Protocol of the International Organization for Standardization (ISO)  (COX, 2011).
Likewise, few LTG scientists are aware of the so-called 'Requirements for the Publication of Geochemical Data' which were agreed upon in 2014 by an editors' roundtable (a roundtable that included geochemists). These requirements explain how to report data and metadata in structured, standardized manners  (GOLDSTEIN et al. , 2014). Even where geochemical data are already compiled and accessible in one place such as the National Water Quality Portal (USGS/EPA), the data are not harmonized, i. e. , units, formats, analytical methods, detection limits, and other parameters are not presented consistently (e. g. SPRAGUE et al. , 2016; SHAUGHNESSY et al. , 2019). Apparently, data standards for agreed-upon units and measurement protocols have never emerged because i) communities have never felt enough need for or placed enough value on such standardization or ii) variations in protocols were simply necessary to answer the proposed research questions.
Neither have LTG scientists addressed, as a community, how to cite and reward or incentivize scientists who collate, curate, synthesize, and share published data for LTG or for other communities (data interoperability). The lack of standards has in turn hampered the development of automated flows of geochemical data into databases. For these and other reasons, geochemical data compilations have been growing slowly (LEHNERT AND ALBAREDE, 2019).
Current data management systems To date, a variety of data management systems have been used by LTG scientists, including storage in notebooks, offline data infrastructures (e. g. , individual computers), published works (e. g. , theses and journal publications and supplemental material), and online data infrastructures (e. g. , personal webpages, dedicated data repositories). A schematic showing the trend of data management is shown in Figure  2. As emphasized by the red-shaded arrow, from left to right the number of data values diminish because data are culled after quality control checks or data are not deemed important enough to save. The most structured form of data management system indicated on Figure  2  is a shared online relational database (upper right). Only a few of these are available for LTG data (see Supplementary Material for examples).