dataesr/scientific-paragraphs-categorization huggingface dataset:
3) were queried using keywords or units of measure (e. g. sea water OR °C) 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. , in review). 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 and the final list of five categories, 14 drivers, and the variables therein were established. Thanks to the companion review paper  (Schlegel et al. , in review) , it was also possible to determine which relationships between drivers are the most important, and what the direction of those relationships are.
It is these important dependent relationships that are used to demonstrate the utility of the product (see Section 4 and 5). 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 have been collected, with nearly half being seawater temperature  (3, 606, 138) , and the other half salinity  (3, 482, 342). Of the 1,565 datasets that have been collected, 880 contain seawater temperature data. Of the 107 different variables, 81 of them come from a single dataset. These primarily being variables for biology, cryosphere, and social drivers (Fig. 2 ). If the number of data points for the variables constituting a driver are not numerous enough, no label is plotted.
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 are 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) hosts a very large collection of datasets produced primarily via earth system research. It is an open access portal with only some datasets under password protected embargo while the authors are waiting for a corresponding research article to finish the publication process. The administrators of this portal provide an 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 lon/lat 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 within the scope of the search for the key drivers and study sites. This first process was based primarily on which datasets were geolocated within the bounding boxes covering the seven sites (Fig. 1 ), as well as filtering out datasets that were specifically bathymetric, terrestrial, or aerial in nature. After downloading the datasets and amalgamating them, the list of parameters for PANGAEA data were 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) 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. 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/) is also designed to host Norwegian data, but focuses on the marine realm, and not necessarily Arctic. This is not however an issue as the database interface combines a keyword search bar, common categories that can be ex/included via radio buttons, as well as 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. 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/) focusses 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 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/) 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, similar 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 Environmental Monitoring of Svalbard and Jan Mayen (MOSJ).
This database has a drop-down tab menu that allows users to select broad categories like Climate -> Ocean. And 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 2 datasets were downloaded here, all of which were included in the database. Governance data were provided exclusively via the national statistics websites of Norway (https://www. ssb. no/en) and Greenland (https://bank. stat. gl/pxweb/en/Greenland/). 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, 16 for Greenland, and all were included in the database. It must be noted that the spatial scale of these data is much greater than the other categories. For example, the 'site' of collection for a statistics is usually an entire province, 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 who are themselves an amalgamation of existing smaller datasets. The first of these is the UNIS database, which is a collection of all of the moorings found throughout western Svalbard and consists primarily of salinity and temperature measurements, which partially explains the dominance of these two variables in the dataset (Fig. 2B ). The other two datasets, SOCAT (Surface Ocean CO Atlas;  Bakker et al. , 2016)  and ₂ Atlas; 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°W to 60°E, ~60°N to 90°N) has been amalgamated into the dataset for this project.
Data assembly Once the data portals had been thoroughly interrogated and the files had been 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 control 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 may be oriented, as well as the different file types within which those data are stored.
Using the R language (R Core Team, 2022), a series of scripts were written to create a pipeline that loaded first all of the PANGAEA files, 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 as being particularly relevant to Arctic fjord socio-ecological systems. 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. So any data falling outside of this narrowed scope were filtered during the amalgamation process. This was relatively rare. Even though this dataset is composed almost entirely of published data, it was noted while performing the example analyses below (Sections 4 and 5) that some issues persisted.
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   measurement of the rest of the drivers tends to begin in the 1990's to 2000's. 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. 12.
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/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 lon/lat values fell within the bounding boxes of the study sites (Fig. 1 ). The number of datasets providing cryosphere data are 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 2006-2021. This created only one additional time series per site, thereby avoiding to overrepresent remotely sensed data in this data product, which aims to be a collection of primarily in situ data.
Physics drivers Data measuring the physical properties of the EU Arctic are the most readily available (Fig. 2 , Table  2 ). Seawater temperature measurements are available at daily to monthly resolution for all sites, with the most frequent taken at Kongsfjorden, and the least at Young Sound, where there are no values during the winter months. Salinity data are often sampled alongside seawater temperature, and so their availability largely matches the former. Light data, while important, are much more difficult to come by. There are many daily values available at Kongsfjorden, but only for one or two years. There have been a few years of summer light measurement in Young Sound, and the western Greenland sites have enough data to create a rough monthly climatology. No data have yet been sourced for northern Norway.
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 % or km2, one can still filter through datasets for any reference or variable name containing 'ice'. However, the carbonate system encompasses the partial pressure of CO2 (pCO2) in seawater, total alkalinity (TA or AT), dissolved inorganic carbon (DIC or CT), pH, and the saturation state of calcium carbonate. Likewise, nutrients contain nitrate (NO3), nitrite (NO2), ammonium (NH4), phosphate (PO4), and silicate (SiO4). 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 µmol kg-1 (TA, DIC), µatm (pCO2), and total scale (pHT) when possible (unfortunately many pH values have an unknown scale, which is noted in the units for the variable).
Biology drivers Datasets providing data for biology drivers are not numerous (Table  2 ). Indeed, no 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), 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 Young Sound, thanks to a recently published study (e.
Social drivers Of all the categories of drivers identified in this study, datasets for social drivers were the most difficult to source. This is primarily due to the fact that there are no applicable social datasets on PANGAEA, which is by far the largest provider of data in this project (Table  2 ). Of the hundreds of datasets sourced, only 28 of them provide social data. Three provide monthly tourism values over the past several years and for Kongsfjorden and/or Isfjorden (Table  2 , Fig. 3 ), one for ship AIS data in western Svalbard, 10 for monthly governance statistics for Greenland, and another 8 for Norway. Fisheries are a very well quantified driver, with a very well established body of statistical analyses for comparison with the natural world. Six such datasets were sourced via national statistics websites for Greenland and three for Norway.
There are 19 fisheries datasets available via the IMR site (https://gbif. imr. no/ipt/), three of which have been amalgamated. More so than with the other categories of data, there is overlap in the variables for the drivers within this category. For example, boat traffic within the fjords is an important social consideration, but must be classified either into the tourism or fisheries drivers depending on the ship in question. The variable names are otherwise the same, which required that the reference in question be consulted in order to accurately rename them (i. e. 'vessels -tourism [n]' vs 'vessels -commercial [n]'). The division and regrouping of these variables was by far the most time consuming of all of the categories due to how many small exceptions there were. In order to illustrate the potential uses of this dataset, a comparison of the different drivers is outlined below.
This is not an exhaustive comparison, nor is it meant to be proscriptive on the use of these data. That would depend on the question(s) being asked by a given researcher. It must also be noted that the values presented below do not necessarily reflect the changes that exist within the fjord as large aggregations of the data have been made in the interest of simplifying the analysis in order to perform it across all of the categories/drivers of data. It would also not be productive or useful to compare every driver in this dataset to each other. Rather it is necessary to follow a guiding principle for which drivers are compared and why. This is found in Fig. 2  of  Schlegel et al. (in review) , which shows the key relationships between drivers and the direction of their interactions, as determined from the literature.
Because the aim of this dataset is to allow for investigations of the interactions between drivers within a given Arctic fjord socio-ecological system, we did not compare data across sites, but we did compare the interactions found within each site to those in other sites. In order to maximise the amount of comparisons that can be made for drivers within fjords, the data have been binned into a few consistent depth ranges, and averaged into monthly means. Time series with only annual values were not used as these created skewed comparisons against other drivers for just the months of January or December accordingly. When data were available for two different comparisons at multiple different depth ranges (e. g. surface sea ice, and seawater temperature at 50 m) all of the possible depth range comparisons were made.
Generally speaking, comparisons of data at the same depth will be the most interesting, but there are exceptions to this that are noted below. Comparisons were only made when there were at least three months of overlapping data available, and only data from 1982 to 2020 were used as this is the period available for the CCI SST product. It must be noted that even though the methodology used for data comparison is coarse, there are still many drivers with either no overlapping monthly values, or only a couple of sites that have overlap. For many sites there are almost no drivers that overlap with anything other than seawater temperature (Table  3 ). Of the 217 relationships that exist between the variables within drivers, only one was able to be quantified across all of the seven sites contained within this dataset (Table  3 ).
18 comparisons could be made between just two different sites, seven between three sites, and seven more comparisons could be made between four or more sites. The relationship between seawater temperature and sea ice cover was somewhat consistent across all sites (except Young Sound), with an increase of 1° C equating to a mean change (± standard deviation) in annual sea ice cover of -12% ± 0. 22% (Fig. 5 ). This relationship allows us to project this value into the future given different emissions pathways (Section 5). Another robust comparison available within this dataset is seawater temperature and species count, which shows a positive trend for most sites, with Young Sound again providing outliers (Fig. 5 ). 18 410 Figure  5 : Boxplots showing the values for the slope of linear models that compare an independent and dependent variable within multiple sites, as shown in Table  3.
Therefore, one must note that the future projections of the driver relationships detailed below are generally for data over the shelf mouth of the fjords, and not for the inner fjord processes. The Arctic model contains six overlapping variables with the data product detailed here: seawater temperature, salinity, pCO2, nitrate (NO3), phosphate (PO4), and silicate (SiO4). As a first step to see how similar the data between the model and the amalgamated dataset are, the RMSE for monthly data were made between sites at the same depths (Fig. 6 ). We then looked at the differences in the trends of the data where possible (Table  4 ). Even though the model data may be warmer than the in situ data for most sites (Fig. 6 ), the positive decadal trends in the remotely sensed data tend to be steeper than the RCP 8.