dataesr/scientific-paragraphs-categorization huggingface dataset:
Data sources will be updated simultaneously for the three datasets in future versions of HydroATLAS. preprocessing of attribute data. Before extracting their attribute information into LakeATLAS format, the original attribute datasets were preprocessed into a standardized grid format with the same geometric specifications as the HydroSHEDS 15 arc-second resolution grids. Standardization was applied to datasets in both raster (i. e. , representing data as a grid) and vector (i. e. , representing data as points, lines, or polygons) formats. The aim of this step was to ensure full spatial congruency between (preprocessed) attribute data and HydroSHEDS, thereby avoiding misalignments in the subsequent conversion processes. All original datasets had already been standardized for the extraction of attributes at the scale of sub-basins (for BasinATLAS) and river reaches (for RiverATLAS), so the same preprocessed grids were used for extracting attributes for LakeATLAS.
We provide a brief description of the general workflow below; a full description is available in Linke et al. 28. Additional details for each individual attribute, including on format and resolution, are provided in the Technical Documentation accompanying the LakeATLAS dataset. The production of standardized attribute grids involved: re-projecting original data sources to a geographic coordinate system with the horizontal datum of the World Geodetic System 1984 (GCS_WGS_1984); converting to grids those datasets that were originally in vector format; aggregating or resampling grids to 15 arc-seconds if needed; cropping or interpolating grids to address mismatches with HydroSHEDS along the coastlines (i. e. , layers showing some pixel values in the ocean or lacking pixels on land compared to HydroSHEDS); and filling voids to replace small areas lacking data with spatially interpolated values (though large areas encompassing entire regions or biomes without data were kept unaltered, e.
g. , if they covered all of Greenland or large deserts regions). Through these preprocessing steps, all attribute datasets were standardized to the following target grid specifications: a global extent of 180°W to 180°E in longitude and 84°N to 56°S in latitude; a cell size of 15 arc-seconds; a global projection defined by GCS_WGS_1984; and a land-ocean distribution of pixels following the land mask of HydroSHEDS.
Calculations were performed using the 'Zonal Statistics' tool of ESRI's ArcGIS 10. 4 software package  50  embedded in customized batch scripts. The zonal statistics tool produces spatial summary statistics, including mean, majority, sum, maximum, and minimum, by performing calculations on cells from a value grid (i. e. , the preprocessed hydro-environmental attribute grid) within the unique spatial units of a zone grid. These zones are defined by cells with the same value, i. e. , the unique lake identifier codes (IDs). In preparation for the zonal statistics calculations, three zone grids of lake IDs were produced: (i) a grid showing each lake's ID only in the grid cell that represents the lake's pour point; (ii) a grid showing each lake's ID in all grid cells that represent the lake's surface polygon; and (iii) a grid showing each lake's ID in all grid cells within a 3-km vicinity buffer around the lake (Fig.
Data records All hydro-environmental attributes available in version 1. 0 of the LakeATLAS dataset, as well as their sources, are listed in Table  2. Most attributes with a time component (i. e. , based on time series data) are provided as long-term annual averages in the attribute table of LakeATLAS, while some also include a monthly climatology (i. e. , long-term monthly averages). Each attribute offered in LakeATLAS is identified by a unique 10-character column name. This name is composed of a 3-digit key describing the name of the attribute, a 2-digit key describing the unit of the attribute, a 1-digit spatial aggregation key, and a 2-digit dimension key (plus two underscores). For example, the variable tmp_dc_lyr depicts air temperature (tmp) in degrees Celsius (dc) inside the lake polygon (l) as an annual average (yr), whereas the variable aet_mm_v06 depicts actual evapotranspiration (aet) in mm (mm) within a 3-km vicinity around the lake (v) as an average for the month of June (06), and the variable glc_pc_u16 depicts the global land cover extent (glc) in percent spatial coverage (pc) within the entire watershed upstream of the lake pour point (u) for class 16 (16) which represents 'cultivated and managed areas'.
All attributes refer to spatial averages across their respective aggregation unit unless explicitly stated otherwise. Full explanations and details on the syntax of the column names, the used abbreviations, and other specifications pertaining to each attribute and its associated data source are provided in the Technical Documentation that is part of the LakeATLAS dataset  27  (also available at https://www. hydrosheds. org/hydroatlas). In particular, the Technical Documentation includes a browsable catalog that provides an information sheet and overview map for every available variable.
Data format and distribution All derived hydro-environmental attributes are provided in attribute tables associated with either the lake polygon layer or the pour point layer of HydroLAKES. LakeATLAS data are publicly available for download at https://www. hydrosheds. org/hydroatlas and as a static copy at the figshare data repository  27. The LakeATLAS data layers are offered in ESRI Geodatabase and Shapefile formats. The data are projected using a geographic coordinate system with the horizontal datum of the World Geodetic System 1984 (GCS_WGS_1984). The attribute table can also be accessed as a stand-alone file in dBASE format which is included in the Shapefile format. All data are distributed with an accompanying Technical Documentation.
technical Validation Like the BasinATLAS and RiverATLAS datasets (Fig. 1 ), the data compendium of LakeATLAS does not create new data from scratch but rather re-formats existing source data. Unless specified otherwise, all source data are used "as is", i. e. , without modification except for preprocessing (described in the Methods section and by Linke et al. 28  ) and the accumulation of values along the drainage direction map of HydroSHEDS. Validation of the quality of original datasets remains with the source publications or documentations as cited in LakeATLAS.
HydroLaKeS. The reliability of LakeATLAS is largely driven by the quality and limitations of its geospatial foundation (i. e. , lake polygons from HydroLAKES), which is itself dependent on its component datasets (see Lake polygons and morphometric attributes in Methods section). HydroLAKES has two main limitations with respect to mapping lake surface areas: the incomplete representation of small lakes; and its inability to represent temporal fluctuations in lake extents. Given the characteristics of the underpinning source data, lakes with a length below 600 m but an area of 10 to ~25 ha (0. 10 to 0. 25 km 2 ) are susceptible to be incompletely portrayed in HydroLAKES for most areas worldwide, except in Canada. While Canadian lake polygons were provided by the high-resolution digital Canadian hydrographic dataset CanVec  38  , most other lake polygons, in particular those below 60°N, were generated from the SWBD dataset (see Methods).
Lake polygons from SWBD were created with orthorectified imagery of radar intensities at 1 arc-second resolution (~30 m at the equator) that was processed with semi-automated extraction protocols in combination with manual supervision and rule-based editing  14. The technicians used land cover water masks as well as existing maps and charts from 1:50,000 to 1:1 million scales as guidance and confirmation of the presence or absence of water. In this dataset, a discontinuity affects the representation of lakes below 25 ha because the minimum size threshold used by technicians for digitizing a waterbody was a length of 600 m (and a width of 90 m)  14. The largest lake missing due to this constraint is theoretically a round lake of 570 m in diameter spanning ~25 ha, and the proportion of omitted lakes increases with decreasing lake area.
This discontinuity is apparent when comparing the size distribution of lake polygons from HydroLAKES to polygons from the reference dataset in the contiguous United States, the U. S. National Hydrography Dataset Plus (NHDPlus) medium resolution  53  (version 2. 1; scale 1:100,000; Fig. 4 ). In Canada, no discontinuity in lake abundance exists below 25 ha because the Canadian reference dataset (CanVec) portrays lakes down to 0. 01 km 2 , an order of magnitude smaller than the surface area cut-off of 10 ha set for HydroLAKES. In Siberia, HydroLAKES polygons above 60°N latitude, i. e. , in areas where SWBD is not available, were generated from the MODerate resolution Imaging Spectro-radiometer (MODIS) MOD44W Collection 5 water mask, which provides a global coverage of waterbodies at 250-m resolution  54.
As explained by Carroll et al. 55  , it is likely that a considerable proportion of surface waterbodies between 10 and 25 ha (≤4 pixels) were not detected in this region due to the coarse pixel size of the MODIS instrument (each pixel is ~6. 25 ha in area) and were thus also missed in HydroLAKES. The second limitation of HydroLAKES resides in its lack of temporal resolution. In contrast to more recent remote sensing products depicting monthly surface water cover, like the European Joint Research Centre (JRC) Global Surface Water Dataset  15  , a large proportion of HydroLAKES polygons are a snapshot of lake size at a given time. For instance, the lake shorelines in SWBD (i. e. , below 60°N outside Canada) were delineated as they appeared at the time of the data collection in February 2000  14  ; lake polygons in Europe above 60°N from the European Catchments and RIvers Network System (ECRINS) represent lake sizes in 2006 as depicted by the Corine Land Cover data (CLC2006)  56.
HydroSHeDS. The quality and limitations of the underpinning hydrographic framework of drainage directions is discussed in the Technical Documentation of HydroSHEDS and related products (see https://www. hydrosheds. org). The choice of various specifications, such as the pixel resolution of 15 arc-seconds, is in alignment with previous global applications of the HydroSHEDS product  45, 57  to ensure compatibility of LakeATLAS with the rest of HydroATLAS and other existing studies, data, and results. The general aim of these choices is to provide data at high spatial resolution, yet without exceeding the limits of accuracy and reliability of the underpinning global datasets, while ensuring that users can process the global data without exceptional computing facilities. Discharge estimates from global hydrological model.
For many hydrological applications, the runoff and discharge estimates provided as part of the LakeATLAS dataset will be particularly important. Like all other attribute data in HydroATLAS, this information was provided by an existing source. Yet given its importance, we conducted a baseline evaluation of the discharge data. The long-term (1971-2000) mean discharge values provided in LakeATLAS were derived through geospatial downscaling  45  of the 0. 5-degree resolution runoff and discharge estimates of the global WaterGAP model 41 (version 2. 2 as of 2014). These estimates represent naturalized flow conditions without anthropogenic water use in the form of abstractions or impoundments  41. After downscaling, the global total river flow into all oceans matched the original flow as modeled in WaterGAP within an error margin of 0.
This selection resulted in a subset of stations that monitored both natural and artificial lakes, mostly clustered in North America and Europe. The sub-comparison confirmed that discharge estimates at lake outlets were about as accurate as those of other gauging stations (R 2 = 0. 95, SMAPE = 24%), with greater accuracy for larger lakes. preprocessing and aggregation of source data for LakeatLaS. To limit distortions and avoid the introduction of bias, the disaggregation and aggregation steps applied for the generation of LakeATLAS (see Preprocessing of attribute data in Methods section) refrain, as much as possible, from spatial interpolation methods. If original data needed to be re-projected or the resolution of original attribute datasets needed to be adjusted, the 'nearest neighbor' approach was applied to avoid modification of original values.
Comparison between LakeatLaS, LakeCat and GLCp. Given the importance of a lake's upstream drainage area on its water quality and functioning, it is crucial to correctly delineate lake watersheds. For LakeATLAS, we used network routing algorithms to determine the hydro-environmental characteristics of the entire area upstream of each lake's pour point based on the drainage direction grids of HydroSHEDS (see Calculation of 'upstream' statistics in Methods section). A similar approach was applied in LakeCAT to compute the characteristics of lake watersheds for the contiguous United States, albeit using drainage direction grids at a much higher resolution (1 arc-second for LakeCAT 21 vs. 15 arc-seconds for LakeATLAS). To compare LakeATLAS with LakeCAT, we spatially joined lake polygons from both datasets, matching lakes that overlapped for at least 90% of their respective area.
Based on this subset of lakes, we found that watershed areas delineated for LakeATLAS using lake pour points and HydroSHEDS drainage direction grids corresponded relatively well to watershed areas in LakeCAT (Fig. 6a ; log-log least-square regression R 2 = 0. 61, SMAPE = 51%, n = 3,975). The largest discrepancies were typically caused by different interpretations of lake pour points that were located near confluences between streams of substantially different size categories. In these situations, even a minor spatial mismatch can assign a lake to the small drainage basin of the tributary stream instead of the large basin of the mainstem river, or vice versa. Small lakes were more frequently affected by this issue. Like LakeATLAS, the GLCP database  24  relies on the lake polygons from HydroLAKES.
In contrast to LakeATLAS and LakeCAT, however, GLCP assigns climate and population statistics to 'lake basins' that were not strictly computed from the lakes' actual hydrologic watersheds (i. e. , the upstream areas that drain into each lake). Instead, GLCP uses a spatial proxy for each lake that is defined by the smallest sub-basin that encloses the lake in its entirety  24  , based on the sub-basin geometry provided in the HydroBASINS dataset  45. As HydroBASINS subdivides watersheds based on stream confluences rather than lake outlets, the spatial representativeness of the resulting 'lake basins' remains ambiguous. On the one hand, substantial parts of the associated basin may be located downstream of the lake; this issue is particularly important for headwater lakes with small drainage areas (note that even for the smallest lake size class, the median 'lake basin' size in GLCP is 156 km 2 ).
Differences between GLCP and LakeATLAS (Fig. 6c ; log-log least-square regression R 2 = 0. 02, SMAPE = 171%, n = 1,422,499; see ref. 24  for details on the 5,189 HydroLAKES polygons excluded from GLCP) only stem from differences in basin association methods as these two datasets both rely on HydroLAKES surface polygons. For both comparisons (Fig. 6b, c ), GLCP 'lake basins' tend to overstate actual lake drainage areas for lakes with watersheds smaller than ~100 km 2 , which reflects about the smallest average size of sub-basin units available in HydroBASINS. In contrast, GLCP tends to understate actual lake drainage areas for larger watersheds, as GLCP only picks the smallest sub-basin unit that entirely contains the lake rather than the entire drainage area. The differences in watershed delineation (or association) between the three datasets also have implications for the computation of watershed-level statistics.
This is for example the case for estimates of human population counts within the lake watershed (or 'lake basin') in 2010, an attribute that all three datasets share, although LakeCAT contains statistics from a different source of population data than LakeATLAS and GLCP. LakeCAT relies on the United States decennial census 60 while LakeATLAS and GLCP rely on the Gridded Population of the World (GPW) dataset  61  , version 4. Despite relying on a different data source, lake polygons, and resolution, LakeATLAS provides a relatively accurate estimate (as compared to LakeCAT) of the population count within lake watersheds (Fig. 6d ; log-log least square regression R 2 = 0. 66, SMAPE = 92%, n = 3,975). GLCP, within its 'lake basin' , overstates the population count within the actual hydrologic drainage area (i.
e. , upstream) compared to LakeCAT (Fig. 6e ) for 85% of lakes, particularly for lakes with smaller watersheds (log-log least square regression R 2 = 0. 35, SMAPE = 148%, n = 3,970). Similarly, despite relying on the same source of data as LakeATLAS, GLCP estimates of population counts in 'lake basins' are not comparable to LakeATLAS estimates in the lakes' actual drainage areas (Fig. 6f ). Fig. 6  Comparison of estimates of lake watershed area and human population count in lake watersheds (or 'lake basins') between LakeATLAS, LakeCAT and GLCP. Only lake polygons that overlap for at least 90% of their respective surface areas across datasets were included in the comparisons with LakeCAT (a,b, d,e; n = 3,975). Because LakeATLAS and GLCP both rely on lake polygons from HydroLAKES, we compared watershed area and population attributes for all lakes between these two datasets (c, f; n = 1,422,499).
The BasinATLAS and RiverATLAS vector datasets are both derived from the same underpinning hydrography of HydroSHEDS that was used for the identification of lake pour points. Therefore, all three components of HydroATLAS (BasinATLAS, RiverATLAS, and LakeATLAS) are mutually linkable via uniquely defined spatial relationships. For example, every river reach is associated with one or more lakes (one-to-many relationship), every lake's pour point is associated with a single river reach (one-to-one relationship), and every river reach or lake falls within a sub-basin (many-to-one relationship). Through these relationships, a lake can be associated with the river network characteristics of the river reach at its pour point, such as the distance from the upstream headwater source or from the final downstream pour point (i.
e. , at the ocean or at the most downstream pixel of an endorheic basin). Similarly, LakeATLAS is generally compatible with the growing list of other raster and vector datasets that are built from, or linked to, the hydrographic framework of HydroSHEDS. Examples of such datasets encompass a global assessment of the free-flowing status of rivers, including an estimate of their sediment transport  62  , and a range of aquatic species compilations, including continental maps produced by IUCN  63, 64. Other datasets that rely on HydroLAKES as a geospatial foundation, including GLCP, can also be linked to LakeATLAS via lake polygon IDs. Intensive efforts have been made to verify the licenses of the underpinning source datasets, and specific permissions were obtained from individual authors if needed, in order to release all derived attribute columns of LakeATLAS (version 1.
0) under either a Creative Commons Attribution 4. 0 International License (CC-BY 4. 0) or an Open Data Commons Open Database License (ODbL 1. 0), both permitting reuse of the data for any purpose including commercial. LakeATLAS users are requested to honor the individual reference suggestions of the source data providers; hence citations and acknowledgements should be made to both the original data source(s) and the LakeATLAS compendium. For example, the following template illustrates a reference to precipitation data sourced from LakeATLAS: "Precipitation data from the WorldClim v1. 4 database (Hijmans et  al. 2005) has been used in the spatial format of LakeATLAS v1. 0 (Lehner et al. 2022). " Detailed information regarding the license and reference(s) for each attribute column is provided in the Technical Documentation of LakeATLAS and in Table  2.
acknowledgements This work was financially supported by a  Discovery Grant  from the  Natural Sciences and Engineering Research Council of Canada  to B. L. ( NSERC-DG ;  RGPIN/341992 ) and by funding from the  Australian Research Council  to S. L. and B. L. ( DP210102575 ). The authors would like to thank numerous additional students in the research lab of B. L. who supported this project through various tasks. Particular thanks are due to the creators and providers of all the underpinning data sources as it is their efforts that ultimately allowed for the compilation of LakeATLAS.
Code availability All data processing steps were performed using native tools and/or customized batch processing within ESRI's ArcGIS 10. 4 software package  50  in a dedicated computing setup (64-bit processing). The two core tools applied were 'Zonal Statistics' and 'Flow Accumulation'. To support repetitive tasks of this work, a multitude of adjusted batch routines were developed as needed, mostly defining input and output path names for the standard tools and to handle internal object IDs. No stand-alone programming code was created that allows automatic processing of new data into the format of LakeATLAS. This is in alignment with the premise of our work, i. e. , to produce standardized data by applying tedious, individual, and customized GIS steps specific to every input dataset so that other users do not have to repeat these time-consuming manual iterations.
UoS/NEL The University of Strathclyde/TÜV SÜD National Engineering Laboratory nano-flow facility comprised of five  components: microfluidic chip, pressure system, fluorescent microscope, charged-coupled device (CCD) camera, and software interface. Fluids with a known concentration of fluorescent beads (1 µm diameter) were introduced into the microfluidic chip from a fluid reservoir under adjustable positive pressure (ranging from 50 to 200 mbar). The geometry of the microfluidic channel in the chip determined the resulting flow rate according to the pressure value applied. A fluorescent microscope was used to illuminate the flow behavior inside the chip. A camera (1-500 Hz acquisition rates) was used to record images of beads moving in the flow using a LabView interface. An in house developed software routine (Matlab) was then used to calculate the velocity of the beads and extrapolate the flow rate according to the geometry of the microfluidic channel used.
DTI The DTI flow facility had a dynamic gravimetric primary standard that measured the change in mass over time. The mass change, Δm, was based upon the measurements made with a high resolution, fast response weigh scale. The facility was installed on a granite table to ensure there were no vibrations influencing the measurements. A custombuilt isolation chamber was also used to reduce environmental factors. The DTI weigh scale can record data with a frequency of 10 Hz and can be used for measuring both dynamic and transient conditions. The flow was generated using syringe pumps across the range of 0. 283-166,667 nL/min with an associated measurement uncertainty of 5. 0% (k=2) to 0. 5% (k=2).
Acknowledgments: This work performed under  18 HLT08  MeDDII project has received funding from the  EMPIR  programme co-financed by the Participating States and from the  European Union 's  Horizon 2020 research and innovation programme. Research funding:  EMPIR  programme co-financed by the Participating States and from the  European Union 's  Horizon 2020 research and innovation programme. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.