dataesr/scientific-paragraphs-categorization huggingface dataset:
Retrieving reservoir boundaries Reservoir polygons of the georeferenced dam points were retrieved as thoroughly as possible from three global water body datasets: GRanD reservoirs  (Lehner et al. , 2011) , Hy-droLAKES v1. 0  (Messager et al. , 2016) , and UCLA Circa 2015 Lake Inventory  (Sheng et al. , 2016). These three water body datasets exhibit an increasing spatial resolution: from 7000+ polygons in GRanD reservoirs provided exclusively for GRanD's dam points to millions of water body polygons, including both natural lakes and reservoirs, in the other two datasets. While HydroLAKES documents 1. 4 million water bodies larger than 0. 1 km 2 (10 ha), the Landsat-based UCLA Circa 2015 Lake Inventory further reduced the minimum size to only 0. 004 km 2 (0. 4 ha), resulting in another 7. 7 million water bodies on the global continental surface.
Accordingly, we implemented a hierarchical procedure, where the three water body datasets were applied in ascending order of spatial resolution to retrieve the reservoir boundaries with an overall decreasing size. Specifically, GRanD v1. 3 provides 7162 valid reservoir polygons for the 7303 dam points (after coordinate corrections) used for harmonization. These GRanD polygons were first assigned to their associated dam points in GeoDAR v1. 1 through GRanD IDs. Reservoirs of the remaining 17 556 dam points in GeoDAR v1. 1, including the 76 GRanD dams with corrected locations (Table  S5  in the Supplement), were next retrieved from HydroLAKES when possible. To avoid duplicates in the reservoirs retrieved from different data sources, we only used the subset of HydroLAKES that is spatially independent from (i.
If this led to multiple dams associated with one reservoir again, only the dam with the closest proximity to the reservoir was kept. Through experimentation, we opted to implement this three-iteration procedure twice, first using a conservative 500 m tolerance to maximize the accuracy for most associations and then a 1 km tolerance to further minimize the number of orphan dams. This multi-iteration procedure retrieved roughly 7600 reservoir polygons from HydroLAKES. For the remaining dam points left unpaired, we applied the same association procedure to continue retrieving their reservoirs from the high-resolution UCLA Circa 2015 Lake Inventory. Similarly, only the subset that does not intersect with the retrieved Hy-droLAKES polygons was considered in order to avoid duplicates in the retrieved reservoirs from different datasets.
The total storage capacity of the 133 largest dams from  Wada et al. (2017) , despite being limited in number, reaches 3900 km 3 or 61 % of the cumulative storage capacity in GeoDAR v1. 0, and the other ∼ 40 % capacity was split almost equally between the remaining 22 000+ geo-matched and geocoded dams. Although the registers used for geo-matching are regional, the dams in GeoDAR v1. 0, as shown in Fig. 6b , are distributed in 151 out of the 165 countries or territories in the WRD, largely owing to our geocoding efforts through Google Maps API. Since the production of v1. 0 was largely independent of other global dam datasets such as GRanD, it can also be used to cross-compare, supplement, and potentially improve other dam datasets. Validation of our georeferencing accuracy for v1. 0 is provided in Sect.
The numbers of reservoir polygons retrieved from each of the three water body datasets are comparable (about 7100-7200 each), but the total reservoir storage capacity and area generally decrease with the increasing spatial resolution of the water body datasets (Table  1 ). As a result, the mean reservoir polygon size decreased from 63 km 2 for those retrieved from GRanD to 2 km 2 from HydroLAKES and 5 km 2 from the UCLA Circa 2015 Lake Inventory. This result is overall consistent with the design of our hierarchical procedure (Sect. 2. 5), where smaller reservoirs were successively retrieved with the help of finer water masks. It is important to note that the retrieved polygons do not always represent the maximum water extents of the reservoirs because water boundaries in the retrieval sources were not necessarily mapped in the maximum inundation periods.
Attributes and usage The GeoDAR dataset, including dam points for v1. 0 and both dam points and reservoir polygons for v1. 1, is provided as three separate shapefiles. For user convenience, we also duplicated the two dam point shapefiles in the comma-separated values (csv) format. The file names and attributes are explained in Table  3. Although most of our dam points were georeferenced using WRD records, our published GeoDAR complies with the legal codes of ICOLD and does not directly release any attribute from the WRD. The attributes we provide in GeoDAR, as listed in Table  3 , are limited to our georeferencing methods, QA/QC, validation, and other information (such as spatial coordinates and part of the reservoir storage capacities) that is already open-source or has been permitted for use by the original producers.
3 reservoirs", "HydroLAKES v1. 0", and "UCLA Circa 2015 Lakes". Refer to Table  1  for more details. plg_a_km2 Area of the retrieved reservoir polygon in square kilometers (calculated using the cylindrical equal area projection on WGS 1984). All other attributes in v1. 1 dams. Note: missing or inapplicable values are flagged by "-999" for numeric-type attributes. proprietary attributes in the WRD. This is also based on the premise that the user needs to acquire the WRD attribute data from ICOLD themselves and that the user agrees not to release the GeoDAR-WRD association or the WRD attributes to the public. Alternatively, since we imposed no usage restrictions on our spatial features (dam points and reservoir polygons), users are free to integrate them with other datasets and tools, such as remote sensing observations and modeling, to acquire the needed attributes, particularly those not yet documented in the ICOLD WRD.
Comparisons with existing global datasets To better understand the improvements and potential applications of GeoDAR, we compare it with three major global dam and reservoir datasets: the complete ICOLD WRD, GRanD (v1. 3), and GOODD (V1). To recap the pros and cons of each dataset, ICOLD WRD documents over 56 000 unique dam records with a broad suite of attributes, but the provided records are not georeferenced. GOODD depicts the spatial details of more than 38 000 dam points and their catchments but does not include any other attribute. GRanD is georeferenced and provides multiple essential attributes, but the records are limited to 7320 large dams. Accordingly, our comparison first emphasized the aspects of dam quantity, reservoir area, and if applicable the spatial pattern and distribution of the dams.
More detailed comparisons (among ICOLD, GranD v1. 3, and GeoDAR v1. 3) for each of the 165 countries are given in Table  S6  in the Supplement. Catchment areas of the reservoirs often indicate the stream order of the impounded river and thus the scales of flow and sediment alterations by the dam. Locating dams with an improved representation of catchment areas, particularly smaller ones, has been increasingly needed by hydrologic modeling and watershed management  (Grill et al. , 2019; Lin et al. , 2019). To evaluate how GeoDAR spatially resolved the WRD in this aspect, we directly used the values of "catchment area" provided in the attributes. As many records in the WRD are missing catchment areas, we combined the available values in both the WRD and GRanD, and when a dam has catchment areas in both datasets, we preferred the value in GRanD.
The increased dam quantity in GeoDAR is manifested as a ubiquitous improvement of the spatial density of smaller dams worldwide (Fig. 12 ). Since GeoDAR v1. 1 has absorbed GRanD v1. 3, the global patterns for capacious reservoirs are overall similar between the two datasets. What is noticeably different are the proliferated density of thousands of smaller reservoirs, particularly those beyond the main focus of GRanD (such as those smaller than 100 mcm). The substantial increase in smaller dams and reservoirs is corroborated by the distribution curves in Fig. 9a , where the mode storage capacity (i. e. , the capacity corresponding to the peak frequency) shifted from about 100 mcm in GRanD to about 3-5 mcm in GeoDAR (both v1. 0 and v1. 1). The area between the distribution curves is largely explained by the addition of ∼ 16 500 dams smaller than 100 mcm in GeoDAR v1.
However, this pattern also reflects the disparities due to other factors, such as a possible bias in the WRD (as it is a volunteered dataset, and not all member nations contributed equally), the accessibility of regional registers for geo-matching, and geocoding challenges for different regions. The top five countries in terms of dam count increase are the US (an increase of 6039 or 314 %), China (4352 or 474 %), India (963 or 290 %), South Africa (667 or 248 %), and Spain (575 or 219 %) (Table  S6  in the Supplement). These five countries cover 72 % of the global dam count increase (17 463). Similarly, the top five countries in terms of storage capacity increase are the US (123 km 3 or 16 %), Canada (73 km 3 or 8 %), Brazil (66 km 3 or 12 %), China (44 km 3 or 7 %), and India (33 km 3 or 12 %), which to- By further aggregating national statistics to each continent, Fig.
As shown in the distribution curves (Fig. 11a ), the average increasing rate is augmented from about 30 % for catchments larger than 1000 km 2 and about 80 % for catchments between 10 and 1000 km 2 to nearly 600 % for those smaller than 10 km 2. The mode of catchment areas decreases from about 200-400 km 2 in GRanD to 30-100 km 2 in Geo-DAR, with the latter much closer to the mode of the entire WRD (15-50 km 2 ). As a result, the number of dams with a catchment size smaller than 25 km 2 , for example, which is the channelization threshold for the high-resolution MERIT Basins hydrography dataset  (Lin et al. , 2019; Yamazaki et al. , 2017) , is 3570 or 27 % in GeoDAR in comparison to 695 or 10 % in GRanD. These small-catchment dams, once integrated into river networks, may substantially improve the performance of routing models.
Spatially complementary to GOODD The recently published GOODD (V1) dataset  (Mulligan et al. , 2020)  includes 38 667 dam points in the world, which were digitized by scanning through Google Earth imagery with support of regional inventories and the Shuttle Radar Topography Mission Water Body Dataset (SWBD;  Farr et al. , 2007). Despite lacking essential attributes, GOODD is thus far the most comprehensive global inventory of dam locations and catchments. The digitization was performed during 2007 to 2011 and was later updated in 2016. This means that reservoirs postdating 2016 were not yet included in the dataset. The completeness and accuracy of GOODD also depend on the sizes of the dams or reservoirs. According to  Mulligan et al. (2020) , the resolution and quality of available Google Earth imagery during the digitization period were low in some parts of the world (such as China), and an experiment in the US showed that detectable dams and reservoirs from low-resolution imagery (e.
119 • N, 72. 927 • E; Fig. 16j ), which was completed more recently in 2014. Since the area of the Duber Khwar Reservoir (about 0. 05 km 2 ) is smaller than the resolution of Hy-droLAKES (0. 1 km 2 ), and the dam completion year overlaps with the image acquisition period of the UCLA Circa 2015 Lake Inventory (from May 2013 to August 2015;  Sheng et al. , 2016) , GeoDAR georeferenced the dam point but did not successfully retrieve the reservoir polygon. To approximate how GeoDAR and GOODD complement each other globally, we intersected both dam datasets with the 30 m resolution UCLA Circa 2015 Lake Inventory. As a result of manual snapping to the 30 arcsec HydroSHEDS streamflow network  (Lehner et al. , 2008) , some of the points in GOODD ended up having substantial geographic offsets from the actual dam or reservoir locations.
For a pilot experiment, we applied a 1 km tolerance (about 30 arcsec on the Equator) when intersecting the UCLA Circa 2015 Lake Inventory with GOODD and kept a 500 m tolerance as used in Sect. 2. 5 for intersecting the lake inventory with GeoDAR. The result shows that among the 55 000 or so water bodies that intersect either dataset, 80 % intersect with GOODD and the other 20 % with GeoDAR alone. These statistics imply that GeoDAR may have an ability to expand the number of dams in GOODD by roughly 25 % (i. e. , 20 % divided by 80 %). Since we applied a larger tolerance for GOODD, this estimated expansion by GeoDAR is likely conservative (considering that the number of GOODD-intersecting reser-voirs may be overestimated). If a 500 m tolerance is used for both intersections, the expansion by GeoDAR will increase to roughly 45 %.
Data availability GeoDAR v1. 0 (dam points) and v1. 1 (both dam points and reservoir polygons) are available for download from the Zenodo repository https://doi. org/10. 5281/zenodo. 6163413 (Wang et al. , 2022). The dam points are stored in both csv and shapefile formats, and the reservoir polygons are provided in shapefile. Their attributes and values are described in Table  3  as well as on the repository website. The data usage information is described in Sect. 3. 3. Other citation courtesy and disclaimer information are given in the "Disclaimer" section and on the repository website. All released datasets and information are available under the Creative Commons Attribution 4. 0 International (CC-BY 4. 0) license (https://creativecommons. org/licenses/by/4. 0, last access: 31 March 2022).
Summary and applications We have produced a comprehensive and spatially resolved dam and reservoir dataset, GeoDAR, which complementarily improved the existing global inventories of large dams. We demonstrated that the production of GeoDAR is not a direct compilation or collation of existing dam datasets. Instead, it involved a first known effort to georeference the ICOLD WRD. This was jointly enabled by geo-matching (or tableassociating) multi-source regional registers and geocoding descriptive attributes through the Google Maps API. This georeferencing effort resulted in GeoDAR v1. 0, which contains 22 560 spatially resolved dam points, each associated with a WRD record, with an overall accuracy of 95 %. Each Earth Syst. Sci. Data, 14, 1869 Data, 14, -1899 Data, 14, , 2022  https://doi. org/10.
5194/essd-14-1869-2022 of the georeferenced records was also labeled with a QA score, providing users a reference to the qualities of individual dam locations. Our georeferencing process and accuracy validation, as we have elaborated in substantive detail, have important methodological value for future expansions of spatial dam inventories using similar approaches, such as Geo-Wiki and OpenStreetMap. To further ensure the optimal inclusion of the world's largest dams, we harmonized the georeferenced WRD (or GeoDAR v1. 0) carefully with GRanD v1. 3. Using the harmonized dam points as spatial identifiers, most of their reservoir boundaries were then retrieved from high-resolution water body datasets. This ICOLD-GRanD harmonization and the subsequent reservoir retrieval resulted in GeoDAR v1.
Besides an improved quantity and spatial detail, another unique value of GeoDAR is its capability of bridging the locations of dams to a broad suite of attributes that are essential to scientific applications. A standing dilemma of existing global dam datasets is the divergence between the focus on dam quantity or spatial detail and the provision of detailed attributes for a limited dam quantity. This dilemma was partially ameliorated by GeoDAR because its georeferenced dams and reservoirs were explicitly indexed to WRD and/or GRanD records where many attributes are available. Since the original WRD is not georeferenced (or at least, their spatial coordinates are not directly accessible), our perception was that the task of georeferencing the WRD to enable a spatially explicit application of the attribute information, even at regional scales, falls on individual users.
We envision that GeoDAR, with its enhanced spatial density and extended accessibility to essential attributes, will benefit from a wide spectrum of disciplines and applications. It is worth noting that although most dams in GeoDAR are smaller than those in GRanD or AQUASTAT, they are still compliant with ICOLD's size criteria, which exclude countless tiny on-farm reservoirs and water storage tanks. Nevertheless, we have suggested from regional examples that GeoDAR partially complements some of the most extensive global dam inventories such as GOODD, despite GOODD owning a larger number of dams. In this sense, even just with the 25 000 or so geometric dam points, GeoDAR contributes yet another fundamental extension to global water infrastructure databases. If these dam points are rectified to high-resolution hydrographic networks (such as MERIT Hydro;  Lin et al.
, 2021; Yamazaki et al. , 2019) , GeoDAR, together with other existing dam and barrier datasets, can help refine our understanding of how human water infrastructure fragmented global rivers and their ecosystems  (Belletti et al. , 2020; Grill et al. , 2019; Yang et al. , 2022) , especially with a more exhaustive inclusion of smaller and/or headwater catchments. Alongside the detailed dam points, GeoDAR's reservoir boundaries provide thus far the most comprehensive global base maps for assessing reservoir dynamics and the impacts of human water regulation. In combination with the expanding constellation of satellite sensors (e. g. , ICESat-2, Sentinel-6, and the forthcoming SWOT), this high-resolution base map will, for instance, enable a more complete and accurate monitoring of water storage variation and surface evaporation in global reservoirs  (Biancamaria et al.
We thank Aote Xin at Kansas State University for assisting in data harmonization, quality control, and validation and for providing comments on the manuscript. We thank Yao Li at Texas A&M University for providing information on incomplete reservoir polygons and Elizabeth M. Prior at Virginia Tech for providing information on some of the duplicate dam points in the US during the open-discussion process. The authors are also grateful to Bernhard Lehner at McGill University for his constructive suggestions and comments on data curation, usage, and dissemination. We also acknowledge Google Maps Platform (https://cloud. google. com/maps-platform, last access: 14 February 2022) for providing the geocoding API. Financial support. This research has been supported by the NASA Surface Water and Ocean Topography (SWOT) grant (grant no.
7. 0. 4, Skyscan). They were then segmented into binary images using simple global thresholding methods because of the low noise and good resolution of the data sets. For the trabecular mircroarchitecture and the trabecular and cortical BMD, 200 slices were selected from the images immediately after the distal growth plate of the tibia. The bone mineral density was computed from their respective region of interest (ROI) for the trabecular and cortical bone using hydroxyapatite phantoms for calibration with CTAn software (v1. 16. 4. 1, Skyscan)  [48]. For the cortical porosity parameters, the µCT images were acquired at the following settings: source voltage 80 kV, source current 100 µA, matrix 2000 × 1336, exposure time 345 ms, rotation step 0. 3 degrees, frame averaging 10, filter aluminum 0.
Numerical Model Parameter Identification The two sets of parameters (Section 3. 3) were identified (Table  3 ) from the experimental data (Table  1 ). It was assumed that the variation in cortical bone thickness (change in porosity) provided, over longer periods of time, the corresponding variation in bone density (change in stiffness) per unit volume. Hence, for a given body weight (load case) and bone geometry, it is possible to directly calculate the elastic mechanical energy developed within the bone and the model parameters. Table  3. Parameters for the proposed theoretical model. Energies were determined from only the experimental data (rat weight, size of bone, test conditions, etc. ). The ki and Ai parameters were calculated from the experimental data after the completion of the running tests.
number: # MPTMAG-49 K). Plates were run on a Bio-Plex MAGPIX™ Multiplex Reader with Bio-Plex anager™ MP Software (Luminex, Austin, TX). Hormone concentrations were expressed in pg/mL, and detection limit was 68. 29 (insulin), 0. 50 (glucagon), 0. 64 (ghrelin), 5. 07 (leptin), 0. 59 (GLP-1), 2. 98 (PAI-1), 4. 31 (GIP) and 184. 89 (resistin), 1. 70 (ACTH), 1. 60 (BDNF), 9. 50 (FSH), 1. 70 (GH), 46. 2 (prolactin), 1. 90 (TSH) and 1. 90 (LH) pg/mL, respectively.
Statistical analysis Data were analyzed using the R statistical software package, version 3. 6. 2 (http:www. r-project. org/) and Graph-Pad Prism 7. 0 software, including principal component analysis (PCA), multiple linear regression analysis and multiple correlations with hierarchical clustering based on Euclidean distance matrixes. Values are represented as mean ± standard error of the mean (SEM) for each experimental group, according to the assay. The significance of differences within and between groups was evaluated by a three-way analysis of variance (ANOVA), whose factors were "genotype", "treatment" and "sex"; followed by Tukey post-hoc test for multiple comparisons. Alternatively, baseline comparisons were evaluated by a two-way ANOVA, whose factors were "genotype" and "sex", followed by Tukey post-hoc test.
Funding This research was funded by the  European Regional Development Funds-European Union  ( ERDF-EU ) and  Fatzheimer  project  EULAC-HEALTH H2020 , grant number  EU-LACH16/T010131 ;  Ministerio de Economía, Industria y Competitividad, Gobierno de España , grant number  RTC-2016-4983-1 ;  EU-ERDF  and  Instituto de Salud Carlos III.
, 2020; Seroussi et al. , 2020) , since no observations exist for that period in Antarctica. We apply the results from the Norwegian Earth System Model (NorESM;  Bentsen et al. , 2013) , one of the ISMIP6-suggested climate models. The NorESM simulations do not provide a perfect representation of the past climate evolution, but it was found to have the smallest biases in the Southern Ocean and atmosphere  (Barthel et al. , 2020). Present-day ocean conditions are given by the data of  Schmidtko et al. (2014) , which are adjusted using the basin-wide temperature corrections from the PICO parameter optimisation presented in Sect. 3. 3, and atmospheric surface mass balance and surface temperatures are used from  RACMOv2. 3 (1995 to 2014 averages, van Wessem et al. , 2018). These datasets are used for present-day climate, and anomalies are applied from the NorESM-1 output, following ISMIP6.
This approach captures transient changes in the atmosphere or ocean while linearly correcting biases. Ocean and atmosphere climate conditions for the equilibrium state in 1850 were obtained as follows: we first generate a timeseries of atmosphere and ocean anomalies from the modelled historic evolution that pass through zero anomalies between 1995 and 2014. We then add these anomalies to the present-day climatologies for the atmosphere and ocean which make sure that our forcing timeseries passes through the present-day dataset in the period between 1995 and 2014. Finally we take the average over the first 30 years of the respective timeseries to arrive at historic conditions. Note that the atmospheric forcing as provided by ISMIP6 starts in 1950 and we keep it constant at the 1950 to 1980 average between 1850 and 1950.