dataesr/scientific-paragraphs-categorization huggingface dataset:
This is managed in part by running seasonal projects (1-3 month duration). In order to extend the time series for these projects, research teams may occasionally leave instrumentation in the field to continue sampling until the teams arrive the following year and begin a new (though usually similar) project  (Bartsch et al. , 2022). While this has proven to be an effective strategy for optimising the available fieldwork time in the Arctic, it effectively creates many short time series with a range of interoperability issues. There are now many well-funded international projects and research institutions that are working to close knowledge gaps in Arctic systems, but they tend to continue to produce these smaller datasets. Therefore, a primary consideration during the creation of an Arctic fjord dataset designed to allow for the investigation of the full socio-ecological system within fjords is how to combine many spatially and temporally disjointed datasets when they may not have the same units of measurement or have otherwise not been sampled with comparable methodologies.
To begin to address this issue, a panel of experts in a range of natural and social science fields identified the most relevant aspects of Arctic fjord socio-ecological systems  (Schlegel et al. , 2023). The structure of these systems was organised as follows: category → driver → variable. For example, the proportion of sea ice cover within a fjord is a variable of the driver sea ice, which is in the category of the cryosphere. This structure was used to guide the collection of data and to organise how the many small yet very important datasets in the Arctic were amalgamated. This structuring of the available data also allowed for better management and conversion of the different units and methodologies into a project-wide standard. This structure did, however, prevent the inclusion of some data types that cannot be effectively stored as a column of values, such as glacier shapefiles and photographic transects.
Different again from the Svalbard fjords are those found in Greenland, with those on the east coast (e. g. Young Sound) currently Table  1. Categories and drivers into which all data points in this dataset are classified. The categories are as follows: cryo refers to cryosphere, phys refers to physics, chem refers to chemistry, bio refers to biology, and soc refers to social. The drivers are as follows: sea ice refers to sea ice cover, glacier refers to glacier mass balance, runoff refers to terrestrial runoff, sea temp refers to seawater temperature, light refers to spectral radiation (PAR (photosynthetically available radiation) + UV-B (ultraviolet B)), carb refers to carbonate system, nutrients refers to nutrients, prim prod refers to primary production, biomass refers to biomass, spp rich refers to species richness, gov refers to governance, tourism refers to tourism, and fisheries refers to fisheries.
being less influenced by warming coastal waters than the fjord systems on the west coast (e. g. Qeqertarsuup Tunua and Nuup Kangerlua). For these reasons, it was determined that the seven sites provide a scientifically useful basis for socioecological investigations, which is why a broader amalgamation of all possible data for all EU Arctic fjords was not performed. Using the names of these seven sites (accounting for various different spellings) and also their geographical coordinates, the databases detailed below were queried to create individual data collections per site. The main city and research station were also used in queries for data at Kongsfjorden (Ny-Alesund/Ny Alesund/Ny-Ålesund), Isfjorden (Longyearbyen), Young Sound (Zackenberg), Qeqertarsuup Tunua (Qeqertarsuup), and Nuup Kangerlua (Nuuk).
Categories → drivers → variables Due to the diverse range of avenues of inquiry one must consider when amalgamating data across the scope of a socioecological system, it was necessary to establish a consistent terminology. Each individual variable of measurement of the natural and social world (e. g. the presence of ice, tourist arrivals, or nitrate concentration; Table  A1  in the Appendix) was characterised into 1 of 14 drivers (sensu  Möller et al. , 2022) , with each of these being grouped into one of five categories (Table  1 ). The list of 14 drivers was not initially evident nor was there a consensus on them from the start. At the outset of the project, a long list of relevant variables was agreed upon, and links to the necessary datasets were provided when possible. When no link was provided, a series of data sources (Sect.
2. 3) were queried using keywords or units of measure (e. g. sea water or degrees Celsius) from the list of variables. While all of the data originally identified were aggregated, a literature review performed for this same project revealed that the important interactions within socio-ecological systems would be better expressed as broad drivers rather than individual variables  (Schlegel et al. , 2023). In reaction to this, further pruning of the dataset outlined here revealed that many of the variables from the initial list had little to no available data. After a couple rounds of editing, the final list of 5 categories, 14 drivers, and the variables therein were established. Thanks to the companion review paper  (Schlegel et al. , 2023) , it was also possible to determine which relationships between drivers are the most important and what the directions of those relationships are.
It is these important dependent relationships that are used to demonstrate the utility of the product (Sect. 4). Finally, it should be noted that not all variables have equally accessible amounts of data, and the collection of data was heavily skewed in favour of the well-sampled variables of seawater temperature and salinity (Fig. 2 ). A total of 7 564 441 data points was collected, with nearly half being seawater temperature (3 606 138) and the other half being salinity (3 482 342). Of the 1565 datasets that have been collected, 880 contain seawater temperature data. Of the 107 different variables, 81 of them are only found in a single dataset, with these primarily being variables for biological, cryospheric, and social drivers (Fig. 2 ). Finally, it must be noted that all of the data points presented here represent either daily, monthly, or annual values.
Data sources The vast majority of the data aggregated for this dataset were publicly available and accessed via four data repositories: (1) PANGAEA, (2) the Norwegian Polar Data Centre (NPDC), (3) the Norwegian Marine Data Centre (NMDC), and (4) Greenland Ecosystem Monitoring (GEM) (Table  2 ). A description of these repositories and how the datasets within each were accessed is detailed in the following subsections. Other notable databases that provided important access to data are also mentioned below.
PANGAEA The PANGAEA data portal (https://pangaea. de, last access: 17 August 2023) hosts a very large collection of datasets produced primarily via Earth system research. It is an openaccess portal with only some datasets under passwordprotected embargo while the authors await a corresponding research article to finish the publication process. The administrators of this portal provide an application programming interface (API) through which one may programmatically interrogate the entire database of a few hundred thousand datasets using Boolean search operators and keywords. While it must be noted that this data portal does not specialise in Arctic data, it is possible to filter data within a long-lat https://doi. org/10. 5194/essd-15-3733-2023 Earth Syst. Sci. Data, 15, 3733-3746, 2023   range, ensuring that the search results remain relevant.
For this particular project, the R package pangaeaR (Chamberlain et al. , 2021) was used. Through the initial search process, 14 063 datasets were identified as potentially being within the scope of the search for the key drivers and study sites. This first filter was based primarily on which datasets were geolocated within the bounding boxes covering the seven sites (Fig. 1 ), as well as on filtering out datasets that were specifically bathymetric, terrestrial, or aerial in nature. After downloading the datasets and amalgamating them, the list of parameters for the PANGAEA data was consulted, and those applying to the 14 drivers determined for this dataset were used as a second filter on the downloaded data. Through this process it was determined that 840 of the PANGAEA datasets would be aggregated, at least in part, with the final dataset.
NPDC The Norwegian Polar Data Centre (NPDC; https://data. npolar. no, last access: 17 August 2023) is designed to cater to the needs of the Arctic research community and specifically hosts datasets related to research conducted for, or funded by, Norwegian institutions. This portal provides a more classic user interface in which one has a search bar that understands Boolean logic. Because the indexing of datasets on this website is tailored to Arctic research, it was not necessary to use the keywords for the drivers in searches. Rather, it was sufficient to search just for the names of the study sites. A total of 11 datasets were downloaded from this website, and all of them were aggregated into the final dataset.
NMDC The Norwegian Marine Data Centre (NMDC; https://www. nmdc. no/, last access: 17 August 2023) is also designed to host Norwegian data but focuses on the marine realm and not necessarily the Arctic. This is not, however, an issue as the database interface combines a keyword search bar, common categories that can be excluded or included via radio buttons, and an interactive map that allows one to filter via spatial domain. In this way, one can rapidly and accurately search for datasets containing drivers of interest within the predetermined spatial domains of the seven sites chosen for this project. A total of 44 datasets were downloaded from this portal, all of which were included in the final dataset.
GEM The Greenland Ecosystem Monitoring Database (GEM; https://data. g-e-m. dk/, last access: 17 August 2023) focuses on the management and dissemination of data relevant specifically to the three Greenland study sites identified for this project. This database is therefore oriented around inquiries into one of these three sites, and while a search bar is available, it is generally more direct to follow the links provided for the individual sites and to use the file structures listed therein to find datasets of interest. Overall, 31 datasets were downloaded from this portal; however, due to https://doi. org/10. 5194/essd-15-3733-2023 Earth Syst. Sci. Data, 15, 3733-3746, 2023  the data portal requirements for acknowledging the use of each unique download, it is not possible to include these datasets in the final dataset presented in this paper.
Additional sources of note The Svalbard Integrated Arctic Earth Observing System (SIOS; https://sios-svalbard. org/, last access: 17 August 2023) is effectively a meta-search database of other Arctic databases that contain datasets specifically of interest to researchers on Svalbard. This data portal provides an advanced user interface, similarly to the NMDC, in which a range of criteria may be imputed in some way in order to limit the resulting output. After searching through the NPDC and NMDC databases, SIOS was used to perform a metasearch of many additional databases to catch anything that was not hosted on the two primary Norwegian sites. Several datasets were discovered through this method, all of which were included in the final dataset. Another database with a strictly Arctic focus that provides publicly accessible data is the Environmental Monitoring of Svalbard and Jan Mayen (MOSJ; https://mosj.
no/en/, last access: 17 August 2023). This database has a drop-down tab menu that allows users to select broad categories like climate → ocean. Therein, one can select from several variables such as sea ice extent or sea level. One then directly downloads these data as. csv files. This website was of particular importance for direct and useful glacier mass balance data. But it also provides a full range of variables from the cryosphere, physics, and biology categories. In total, two datasets were downloaded here, both of which were included in the database. Governance data were provided exclusively via the national statistics websites of Norway (https://www. ssb. no/en, last access: 17 August 2023) and Greenland (https://bank. stat. gl/pxweb/en/Greenland/, last access: 17 August 2023). The Norwegian statistics website focuses more on the national concerns of an economy oriented around more developed service industries and therefore has fewer resources available for dedicated inquiry into the human impact on the marine realm.
The Greenland national statistics website, in contrast, focuses more on the importance of the marine realm to the economy and therefore has a deeper range of available statistics of interest for the effect of governance on other drivers in Arctic fjord socio-ecological systems. Overall, 8 datasets were downloaded for Norway, and 16 were downloaded for Greenland, and all were included in the database. It must be noted that the spatial scale of these data is much greater than for the other categories. For example, the site of collection for a national statistic is usually an entire province and not a single fjord. Where possible, the national-scale sites are associated with their local-scale fjord (e. g. Nuup Kangerlua is within the Sermersooq Municipality). There are also a few very large datasets of interest to this project which are themselves an amalgamation of smaller existing datasets.
The first of these is the UNIS database  (Skogseth et al. , 2019) , which is a compilation of all of the temperature and salinity profiles (collected via ship transects and moorings) found around Svalbard (75-83 • N and 0-34 • E), which partially explains the dominance of these two variables in the dataset (Fig. 2b ). The other two datasets, SOCAT (Surface Ocean CO 2 Atlas;  Bakker et al. , 2016)  and GLODAP (The Global Ocean Data Analysis Project;  Lauvset et al. , 2022) , focus more on the carbonate system of the ocean. These are global products, but only the Arctic region encompassing the seven study sites (∼ 60 to 90 • N, ∼ 60 • W to 60 • E) has been amalgamated into the dataset for this project. Finally, the Norwegian Meteorological Institute (https://www. met. no/en, last access: 17 August 2023) provided 13 very long and high-quality multi-variable atmospheric time series generated by long-running MET stations; however, the final dataset for this project was limited to focusing directly on the marine realm, not the atmosphere, so these time series were not amalgamated.
Data assembly Once the data portals had been thoroughly interrogated and the files downloaded and saved according to their data sharing permissions, they were combined into a single data product. Each of the data portals outlined above have their own requirements for the data they host, with some portals being more strict than others. Much of the aggregation of the hundreds of different datasets was aided by the very strict quality controlling for data hosted on PANGAEA. All of these datasets were first aggregated during the download process into a PANGAEA-specific format, which was close to the final project-wide standard. The other data portals allow for a wider variety in which the raw data within the dataset, as well as the different file types within which those data are stored, may be oriented.
Using the R language (R Core Team, 2023), a series of scripts were written to create a pipeline that first loaded all of the PANGAEA files and then the individual files from the other data portals before combining them into a single shared project-wide standard based on tidy data principles  (Wickham, 2014). Each datum in the dataset therefore has the same corresponding columns of meta-data: (1) download date, (2) URL, (3) citation, (4) type of data (e. g. in situ or remotely sensed), (  5 ) site (mostly one of the seven study sites; Fig. 1 ), (6) category, (7) driver, (8) variable (9) longitude, (10) latitude, (11) date of sampling, and (12) depth of sampling. Where possible, the URL provided for the data is the link to its digital object identifier (DOI) page. Importantly, all values in the dataset are numeric so that they can be listed in one single column that extends along millions of rows of data.
Quality control Because all of the data aggregated for this dataset were taken from published sources, it was determined that they should not require the application of rigorous quality control (QC). Therefore the primary function of QC for this process was to ensure that the data aggregated into the final product could be classified into one of the 14 drivers identified above. This was necessary in part because many of the datasets listed at the outset of the project contained data for drivers outside of the final 14, requiring that they be filtered during the amalgamation process. Even though this dataset is composed almost entirely of published data, it was noted while performing the example analyses below (Sect. 4) that some issues persisted. When the issue was simply an anomalous data point (e.
4 ). Seawater temperature and salinity are once again the most well sampled of the drivers, with data starting in 1876. Somewhat surprisingly though, nutrient data have been sampled since 1934. After that began the consistent measurement of carbonate chemistry, terrestrial runoff, and glacier mass balance data in 1957, 1957, and 1967 respectively (Fig. 4 ). The measurement of the rest of the drivers tends to begin in the 1990s to 2000s. We may also see that data for all of the drivers tend not to be present at all seven study sites at the same time (Fig. 4 ). The data available for the 14 drivers within each of the five categories are summarised in the following sub-sections, and any specific filtering or unit conversions made are detailed.
Cryosphere drivers Cryosphere data are readily available throughout the Arctic, though usually not at a daily resolution. Measurements of winter ice cover are generally available for all but the western Greenland sites. The glaciers for Svalbard and eastern Greenland have annual measurements available in August-September for Svalbard and April-May for eastern Greenland. Conspicuously, there are no measurements of sea ice across sites using the same methods or units. Comparisons of in situ sea ice cover across the Arctic are thus not currently possible. The Global Runoff Data Centre (GRDC) provides a global database of river discharge values, though their data-sharing restrictions prevent us from aggregating the data here. Likewise, there are many discharge values available for Greenland via GEM.
The terrestrial runoff data that were subset from the larger GRDC database were determined by spatially filtering the time series whose long-lat values fell within the bounding boxes of the study sites (Fig. 1 ). The number of datasets providing cryosphere data is relatively low compared to those for chemistry or physics data but higher than for biology and social drivers (Table  2 ). Due to its broad importance for the understanding of change within Arctic fjord socio-ecological systems, sea ice cover is one of the two drivers in this data product for which remotely sensed data were included. Pixels were extracted from the MASIE 4 km resolution ice cover product  (NSIDC, 2022)  for the seven study sites (Fig. 1 ), and average daily sea ice cover values were created from 2006 to 2021. This created only one additional time series per site, thereby avoiding overrepresentation of remotely sensed data in this data product, which aims to be a collection of primarily in situ data.
While a 1 km product is available, it only starts in 2014, which is too short to be useful for trend analysis. A comparison was made between the 4 and 1 km product for years of overlapping data in Kongsfjorden, and the values were found to be very similar. For this reason, it was decided that the lower-resolution, longer time series was preferable. While sea ice thickness rather than cover would be preferable for inclusion in this amalgamated dataset, at the time of this writing, the authors were not aware of any such product on an Arctic scale. https://doi. org/10. 5194/essd-15-3733-2023 Earth Syst. Sci. Data, 15, 3733-3746, 2023.
Chemistry drivers The chemical composition of seawater is generally well sampled in the Arctic, with the study sites having data available for the carbonate system for all months of the year (Fig. 3 ). Daily carbonate system data are available for several years in Kongsfjorden and to a lesser extent in Nuup Kangerlua. Nutrient data availability is greatest during the ice-free months (Fig. 3 ), particularly in Kongsfjorden and Young Sound. While less frequent, the western Greenland sites also have data available for much of the year. Datasets providing chemistry data are the second most numerous after physical data (Table  2 ). The filtering and grouping of variables for the chemistry drivers required more consideration than the previously described categories because each of these drivers contained variables that were notably different from one another.
For example, while sea ice cover data might be in units of percentage (%) or square kilometres (km 2 ), one can still filter through datasets for any reference or variable name containing "ice". However, the carbonate system encompasses the partial pressure of CO 2 (pCO 2 ) in seawater, total alkalinity (TA or A T ), dissolved inorganic carbon (DIC or C T ), pH, and the saturation state of calcium carbonate. Likewise, nutrients contain nitrate (NO 3 ), nitrite (NO 2 ), ammonium (NH 4 ), phosphate (PO 4 ), and silicate (SiO 4 ). All of the variables for these two drivers tend to come in a variety of units of measurement so it was necessary to choose a standard unit and convert data as necessary. For the carbonate system this was either in units of micromoles per kilogram (µmol kg -1 ) (TA, DIC), standard atmosphere (µatm) (pCO 2 ), and total scale (pH T ) when possible (unfortunately, many pH values have an unknown scale, which is noted in the units for the variable).
For the nutrients, all values are in micromoles per litre (µmol L -1 ). While this does not match the established best practice of using micromoles per kilogram (µmol kg -1 ) for nutrient measurements  (Jiang et al. , 2022) , one must have seawater temperature and salinity values to convert from litres to kilograms  (Becker et al. , 2020) , and these data tend to be missing from datasets that report nutrients in units of litres, making a conversion impossible. R. W. Schlegel and J. -P. Gattuso: A dataset for investigating socio-ecological changes in Arctic fjords.
Biology drivers Datasets providing data for biology drivers are not numerous (Table  2 ). Indeed, no FAIR datasets were identified for Storfjorden or Porsangerfjorden, with only primary production data in August available at Qeqertarsuup Tunua. Nuup Kangerlua is the only site with data available for all of the biology drivers for all months of the year, with Young Sound having all drivers during some ice-free months. Isfjorden has primary production data available over the full calendar year, with some daily datasets available. Note that Seabird data exist for much of Svalbard (e. g. https://data. npolar. no/dataset/ 0ea572cd-1e4c-47a3-b2a5-5d7cc75aaeb4, last access: 17 August 2023), but these were considered to be outside of the scope of the marine data collected for this dataset. It is important to note that, while the rate of primary production is known to be a very important driver in Arctic fjords, data for the direct measurement of this driver are almost non-existent, with the exception of data from Young Sound  (Holding et al.