dataesr/scientific-paragraphs-categorization huggingface dataset:
Such databases represent the most structured and demanding management systems, but also provide the easiest data discovery and use for meta-analysis. These also promote the easiest collaboration. Some of the data repositories that have a track record of success for data types of interest to LTG (time-series water data, rock chemistry, atmospheric radiation measurements, CO2 flux, etc. ) are summarized in Table  1. Some of these online data infrastructures are maintained and used as libraries (e. g. , for spectra, electron micrographs, or diffraction patterns). Such libraries are not true data repositories, but rather databases that do not generate DOIs for the data provider and may only retain a limited number of examples for each measured entity. An instructive example for mineralogy is the International Centre for Diffraction Data (ICDD) that offers a detailed (paywalled) library of mineral structure data (both experimental and theoretical), which serves as a reference for identification and quantification of minerals.
Other open-source databases for mineral structures are also available (e. g. , Mineralogical Society of America Crystal Structure database). Given that only a few highly structured targeted databases for LTG data are available, and that libraries are not true data repositories, many other LTG data types lack appropriate repositories (a few examples are listed in Table  3 ). For these "orphaned" data types, scientists either publish their data in a journal article or its supplement, leave it unpublished on their computer or within a thesis, publish it online on their personal website, or use generalized and unstructured data repositories that can accommodate any type of data file and can assign a DOI to the dataset. These generalized data repositories provide only minimal curation of metadata and do not police data quality.
E. T. L. , 2020). Other such generalized data repositories are also becoming available through publishers, universities, federal agencies, and private entities. Two examples that are used by some NSF-funded geochemists are EarthChem Library and CUAHSI's HydroShare (see Supplemental Material). To our knowledge, there is no known portal that links to all the many data repositories used by LTG scientists. Despite the examples in Table  1 , we sense that most LTG scientists do not use data repositories. Thus, even for those parts of LTG science for which data management systems have been developed, many practitioners of LTG do not understand the repositories, how to use them, how to manage their data efficiently to prepare to ingest data into the repository, nor what kind of science they could enable.
Lesson 1. Improving the accessibility of geochemical data promotes better science and better societal decision-making. The value of scientific data increases to other scientists and to the public when data are published online and can be accessed even after a given program or project is terminated  (BALL et al. , 2004; CHRISTENSEN et al. , 2009). As an example, background soil chemistry data from decades in the past can be used to assess pollution impacts or health risks for activities that are ongoing today (e. g. BRECKENRIDGE AND CROCKETT, 1998; U. S. NATIONAL ACADEMY OF SCIENCES ENGINEERING AND MEDICINE, 2017). In this respect, soil and water quality data are often prized by decision-makers and the scientific community alike. On the other hand, if a decision-maker or scientist or member of the public must peruse multiple publications and web pages to pull together a dataset, or to adjust the units of a dataset  (SHAUGHNESSY et al.
, 2019) , the time needed for such discovery and harmonization can preclude time for deep analysis  (LIU et al. , 2020). Publicly accessible geochemical databases also accelerate collaboration among scientists and across disciplines and help to promote dialogue with the public  (CHRISTENSEN et al. , 2009; BRANTLEY et al. , 2018). Without compiled datasets, very little coordinated design of data gathering strategies occurs, leaving gaps in geochemical understanding. Publication of data into accessible venues makes the information usable by communities outside of the original audience. Lesson 2. Although the data enterprise from measurement to meta-analysis is complex and fraught with opportunities for error, systematic management of data leads to improvements in data quality and promotes identification of large-scale trends.
Few individuals understand the entire trajectory from sample collection or sensor deployment to measurement or observation to online publication and data interpretation in LTG. In addition, only a very few people within this complex data enterprise can assure the quality of the data, and these personnel tend to be those who made the measurements in the first place or who were responsible for use of reference standards, methodologies, instrumentation upkeep, and quality assurance measures. In addition, as data are moved from the laboratory notebook to compiled datasets and shared data repositories (see, for example, Figure  2 ), new opportunities for errors arise. Simply put, it is impossible to maintain and manage data systems without errors. Nonetheless, during the complex trajectory from measurement to interpretation of geochemical data, increasingly systematic data management helps scientists to find issues related to data quality as well as to find largescale trends and patterns in the data, through the use of meta-analysis.
Lesson 3. Highly structured relational databases may be useful for LTG data where geochemical measurement protocols are reproduced identically in many locations on many samples, but less structured data repositories are needed for measurements that are implemented differently from sample to sample or place to place, or where protocols are developing or non-standardized. Some geochemical sampling and analytical strategies are routine while many are under development or implemented differently based on the particular nature of the media or the question being asked. The result is that "routine" data are relatively easy to standardize and manage in structured repositories while non-routine data are not (Figure  3 ). An example of "routine" data are measurements of solute concentrations, pH, alkalinity, and other parameters completed on water samples by the U.
Even when geochemical datasets are small in volume they generally require complex metadata to make them of lasting usefulness (a point also made for ecological data  (MICHENER, 2006) ). This is partly because interpretation of chemical analyses requires understanding the different methods of sub-sampling, extractions, or density separations before analysis (Figure  1 ). Even after preparation, a range of measurements with a variety of instruments are possible: analyses of target elements, mineral species, aqueous species, isotopes, location-specific isotopes in molecules, microbiological characteristics, chemical bonding, photographs, diffractograms, spectra, and other operationally useful parameters (Figure  1 ). The result of all this complexity is that geochemical datasets are characterized by a wide variety of structures, metadata requirements, and formatting.
Lesson 6. The data-scape for LTG must encompass i) flexible management systems for datasets where measurement methods are less routine or still under development, ii) highly structured and managed data systems for datasets with established standards for measurement, and iii) a process whereby data systems can sometimes evolve from mode (i) to mode (ii). This finding is largely a corollary of the lessons explained above in that it can be difficult and time-consuming to format and input large volumes of metadata into structured data management systems even when they are designed specifically for an individual dataset; likewise, such data input simply does not make sense for less routine data. Such structured data systems need only be built for very large and important datasets where the measurements are more or less routine and the community agrees upon the need for and utility of the database.
Two examples discussed previously manifest this finding: namely the development of a highly structured database for rock chemistry (PetDB) and the development of a highly structured database for water chemistry (CUAHSI HIS). These communities had rough measurement standards and protocols already, and agreed on the utility of the data, and so they self-organized and developed standardized data management systems. Without such agreed-upon formats and goals, other communities need data management systems that allow data to be stored in less structured systems. The benefits of these less structured systems are that they are often more intuitive to subject-matter experts, can be easier to archive in some data repositories, and are easy to re-structure  (CHRISTENSEN et al. , 2009). Of course, by definition, this type of data storage is not as useful to some data users (Figure  2 ) because datasets are compiled with different structures and other characteristics.
But it is important to emphasize that one reason that less structured data systems emerged from the rock and water communities was that there was resistance to the time commitment needed for uploading of data and metadata into the highly structured databases. Therefore, even after the highly structured databases emerged (e. g. PetDB and CUAHSI HIS), the need for less structured data systems that would allow easier collations of data without the time-consuming input and metadata format requirements became apparent (see Supplementary Material). These two highly disparate communities (petrologists and water scientists) both discovered that i) some datasets and communities need structured data management systems, and some need less structured systems, and ii) many data providers resist timeintensive data uploading protocols.
Lesson 7. Standards for data and meta-data are only developed when the community or the science demands it. Some communities have successfully brokered data sharing agreements (e. g. , climate, biological oceanography, seismology); likewise, best practices have been endorsed for data publication and data citation that apply across some domains (e. g. Lehnert & Hsu 2015 , ESIP 2019; (DATA CITATION SYNTHESIS GROUP, 2014; STALL et al. , 2019; COPDESS, 2020). However, low-temperature geochemists so far have not agreed upon standards for data formatting nor for data storage for many of their datasets. It is likely that this is because the community has not yet seen enough value in data standards or in data harmonization relative to the time required to implement them. For example, if most LTG data are not intended for integration with other groups' or other disciplines' datasets, or if this integration is not valued, then the hard work of data standardization will not occur.
As Earth system models are developed and improved and interoperability of datasets is increasingly valued, however, LTG practitioners will eventually develop ways to standardize and share some if not all of their data. One impetus that is already encouraging a demand for standards on the part of LTG scientists is new mandates from journal editors and granting agencies. Lesson 8. LTG scientists need both archived (unchanging) and versioned (modified and updatable) datasets. Some LTG datasets must be maintained as non-changing entities (long-term archives) while others are continuously updated or corrected over time (self-described longitudinal or versioned datasets). For example, water chemistry data has been used to investigate the impact of hydraulic fracturing on groundwater (Shale Network, Table  1 ).
When meta-analyses are published  (WEN et al. , 2019) , the data are referenced both as a growing dataset site hosted by the CUAHSI Hydrologic Information Systems (HIS) (doi:10. 4211/his-data-shalenetwork), but also as a separately archived version of the dataset sampled at the time of analysis (doi:10. 26208/8ag3-b743). To archive the data as a versioned dataset was not possible in the CUAHSI HIS, and so the scientists published it in their university data repository. That repository allowed archiving of a long-term copy of the data, whereas the other site showed the entire, growing dataset. From the perspective of data producers, it is particularly important to archive the dataset analyzed in publications to ensure the reproducibility of the relevant research work. On the other hand, scientists also need to update datasets and attach version numbers to evolving data.
Thus, data curation is needed that tracks provenance, provides versioning capabilities, and allows citations (e. g. , DOIs). Such utilities could be found in different data management systems or within one system. Lesson 9. Where geochemical databases have been successful, they have been funded over long periods of time, organized by groups of dedicated scientists, and focused on specific data types. A few entities have built very focused databases for geochemical data. For example, PetDB and Geochemistry of Rocks of the Oceans and Continents (GEOROC) are successful synthesis databases for petrologic data, as is the CUAHSI HIS for time series water quality data (see Supplementary Material). The first two databases exclude large sectors of rocks and minerals of interest to LTG while the second database is built for time series but is not as easy to use for depth profiles of soil porewater, for example.
A similarly successful data repository in another field is the USGS Produced Water Database which provides water chemistry for samples collected from oil and gas reservoirs nationwide (Table  1 ). These databases and other long-term repositories (Table  1 ) share some attributes. First, they target only a subset of data as defined by their mission or funding: PetDB, for example, was funded by NSF's RIDGE Program to provide a complete collection of the geochemistry of igneous and metamorphic rocks of the ocean floor. By definition, the databases do not include the geochemistry of all rock types. Nonetheless, the databases have grown to accommodate similar geochemical data of other materials. For example, PetDB includes data for mantle xenoliths, ophiolites, marine sediments, volcanic gases, and astromaterials.
Second, successful databases tend to receive consistent funding over many years from government agencies, private foundations, libraries, or universities. Third, some data systems are successful at least partly because developers are a small but dedicated group of scientists (<12) who attract and work with a much larger group of scientists contributing data. Lesson 10. The science community must prioritize which datasets will be managed online as shared relational databases because development and maintenance of such databases is very time-and resource-consuming. Building cyberinfrastructure that facilitates access to geochemical data along the trend shown in Figure  2  is expensive, skill-requiring, and time-consuming. The exact cost of both building and maintaining datasets or data repositories depends upon the type of database.
For example, although relational databases are more powerful than flat files, they are also more difficult to maintain over time. They are also less intuitive for subject-matter experts, and require more planning and documentation  (CHRISTENSEN et al. , 2009). In actual dollars, the cost of maintaining EarthChem's PetDB (Table  2 ) annually is $250,000/year, and this does not include resources for new developments to keep up with changing technology demands. For large, multi-investigator projects, data management can cost 20-25% of the cost of the measurements themselves  (BALL et al. , 2004). The costs of maintenance are at least partly related to the ongoing evolution of computer hardware and software and the need to maintain the databases on the evolving platforms. Investments in funding, time, and personnel for updating, maintenance, and upgrades are therefore significant.
A part of the problem is that research datasets are ever-changing, but very little money is typically available for changing data management structures or new metadata fields, etc. It is of course always possible to write code to migrate data from one system to the next. However, this also costs time and money. All of these issues are amplified because of the large number of skillsets needed at the same time in a data management team -skillsets that are generally not found in the same small set of individuals. For example, information technology researchers that have developed new cyberinfrastructure are generally less interested in maintaining old infrastructure. Furthermore, personnel managing data cyberinfrastructures must not only support the software and hardware but must also provide help to the community of users.
Training modules for such data management have been established online that could be useful to LTG scientists (U. S. G. S. , 2020b). If data and metadata are managed well, the job need only be done "once" and data presentation and publication will become easier for journals and online databases. Researchers should plan for data management in advance of their research, and be afforded additional funding within grant budgets for personnel time, hardware, or software. For larger projects, data management team members could be embedded into science teams to promote improved data and metadata publication. To enable all of this improved data management, LTG scientists suggest that funders should provide adequate funds for time and infrastructure, while protecting resources for the science itself.
Data scientists at the workshop pointed out that the use of consistent data templates pulled from existing resources or customized for a laboratory can be a simple but cost-effective way of lowering the burden of collecting consistent metadata. This is because once all the data are standardized into one format, it is easier to convert to some other metadata or data format if needed for ultimate publication in a data repository. Some pointed out that geochemical workflows could be supported and automatically recorded by intelligent software such as Laboratory Information Management Systems. At the same time, however, such systems can be expensive and time intensive to implement and are usually only implemented in large laboratories or for very large datasets, both of which tend to plot to the right on the schematic in Figure  3.
Choice of data repository. The choice of appropriate data repository must be made by consultation with the scientific community, editors, managers, and funders. The file format should be chosen so that it meets the standard for long-term preservation. In other words, storing the data file as a specific spreadsheet format rather than a CSV file might limit users' ability to use the data in the future if proprietary spreadsheet format conventions are changed. Using non-proprietary data formats make the most sense. At a minimum, if the data are deposited in a repository, the dataset should be given a DOI and this should be called out in the journal publication. Appropriate repositories could include sites run by a scientific organization, publisher, government agency, or university. Some repositories will be hosted on a single server while others might be distributed data management systems (e.
They should also be the preferred location for data publication in their respective sub-disciplines. In addition to promoting these specialized, targeted, highly structured databases with a strong track record for growth and security, workshop participants argued that funding agencies should promote development of less-structured, generalized long-term data repositories to provide homes for LTG data that do not at present have one (e. g. Table  3 ). These repositories can host almost any kind of dataset, without any requirements with respect to data structure. These generalized data repositories are not organized around a research question and thus are useful even as the science itself changes. They are instead organized by an entity (a library or university or country or funding agency, for example) or are associated with a broad scientific target topic  (water, climate, etc.
). Good examples that have been funded by U. S. federal agencies are CUAHSI HydroShare, EarthChem Library (described in Supplementary Material), the NASA DAACs (https://earthdata. nasa. gov/eosdis/daacs), the USGS's Sciencebase (https://www. sciencebase. gov/catalog/), and the DOE's ESS-DIVE  (VARADHARAJAN et al. , 2019). These generalized data repositories are not as rigid in their metadata requirements, do not provide rigorous data curation, and are simpler and more intuitive to use. Workshop participants agreed that in addition to the specialized, targeted, and highly structured databases for specific types of data (e. g. PetDB, CUAHSI HIS, etc. ) and the generalized and unstructured data repositories (EarthChem Library, HydroShare, ESS-DIVE), there must also be a path by which some datasets within the generalized repositories can grow and eventually nucleate into a targeted, structured, specialized database.
Such a transition might organically occur when the volume of data reaches a critical or threshold value, when the need for the data becomes critical, or when the user base becomes large  (BALL et al. , 2004). At that point, presumably, funding for the transition would become defensible. Not every dataset or data type will follow this trajectory nor receive such resources, but for a small number of datasets, funding could be made available on a competitive basis within the standard proposal format. The data systems that move all the way to the upper right on Figure  2  will likely answer specific, important, and compelling questions that enable meta-analysis for broad, enduring problems. The data management ecosystem. The workshop group also considered two generalized scenarios within the overall trend of development of the ecosystem of data repositories for LTG.
First, LTG datasets are already distributed among many repositories across the world and even within the U. S. A. and many data are stored in sites managed by non-US and non-NSF scientists (for example, see Table  1 ). Likewise, some already-functioning specialized data management systems (Table  1 ) could be better places for LTG data publication than a generalized NSF-branded or LTG-branded repository. Furthermore, some datasets might be well-managed in different ways by different scientific communities in different data management systems with different data measurement protocols, promoting different types of science. Likewise, LTG data from a single study might include multiple types of data that might be better spread across multiple repositories (the opposite might be true as well). Hence, multiple data repositories must be expected and should be encouraged, and a street bazaar of data management systems, scenario two, is inevitable and, in many ways, desirable.
For example, as more repositories become available, competition will likely drive improvement in capabilities. Perhaps data providers will begin to choose data repositories in the same way and for the same reasons that they choose journals for their publications today. This led to a discussion of the need for better search tools to navigate this bazaar of data efficiently. To enable this, LTG scientists must work directly with data specialists to develop better search tools and better ways to tag datasets so they can be easily discovered. Tagging, of course, requires development of controlled vocabularies by LTG scientists themselves. Data could be tagged either during or after uploading, and tagging could either be required by funders or journal editors, or it could be voluntary. Workshop participants argued that funding should be prioritized for cybertools to find the data that have been placed online in trusted secure data repositories and to cross-reference these data for samples with unique identifiers.