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Recent Upper Colorado River Streamflow Declines Driven by Loss of Spring Precipitation
<div> <div> <p>The dataset accompanying the manuscript titled "Recent Upper Colorado River Streamflow Declines Driven by Loss of Spring Precipitation" provides comprehensive information on streamflow patterns in the Colorado River since 2000. The dataset is needed to run the analysis available on GitHub available <a href="https://github.com/dlhogan97/Spring-Precipitation-Effect-CO-River.git">here</a>. This is version 2, please use this version for the most up-to-date results.</p> <p><strong>Please read the accompanying README (available in the README.md file) for individual file descriptions and file nesting strategy that should be employed to easily reproduce this analysis.</strong></p> <p>The dataset covers a range of variables related to streamflow and precipitation, including but not limited to discharge measurements, seasonal variations, and relevant meteorological data. The primary focus of the dataset is to elucidate the observed streamflow deficits in the Colorado River, attributing these changes to decreased spring precipitation.</p> <p>Key features of the dataset include:</p> <ul> <li> <p>Time Coverage: The dataset spans a specified time range that aligns with the investigation into recent streamflow deficits in the Colorado River between 1964 and 2022.</p> </li> <li> <p>Spatial Scope: It includes data from relevant monitoring stations along within the Upper Colorado River, but focusing in the hydrologically vital headwater regions, providing a spatially distributed perspective.</p> </li> <li> <p>Variables: The dataset encompasses a variety of variables essential for understanding streamflow dynamics, with a particular emphasis on the impact of reduced spring precipitation.</p> </li> </ul> <p>Researchers and stakeholders interested in hydrological patterns, climate-driven changes, and water resource management in the Colorado River Basin will find this dataset valuable. It serves as a foundational resource for reproducibility, further analysis, and collaboration within the scientific community. The dataset is deposited on Zenodo to facilitate open access, sharing, and citation for broader research endeavors.</p> </div> </div>
Global River BankFull Discharge (GQBF) - Siberia(SI) & South Pacific/Australia(SP)
<p>The GQBF is the estimated bankfull discharge across ~2.87 million km (length) of global river reaches. The bankfull discharge here is defined as the maximum flow rate contained within a river just before inundation occurs in the surrounding floodplain. We based our river bankfull discharge estimation on a newly developed river network, Global RIver Topology (GRIT), using GRIT’s river reaches as the spatial scale to represent the variation in bankfull discharge. We included all GRIT river reaches that coincided with the Global River Width from Landsat (GRWL) river masks (with overlapping ratio >=0.5). This selects river reaches with satellite-derived width measurements >=30 m, resulting in a total length of ~2.87 million km. Here, the GQBF represents the time-averaged bankfull discharge at <1 km (river length) spatial resolution.</p> <p><strong>Regions</strong></p> <p>Added regions SI, SP Vector files.</p> <ul> <li>SI - Siberia</li> <li>SP - South Pacific/Australia</li> </ul> <p>The subcontinental catchment groups (vector, polygons) can be found at <a href="https://zenodo.org/records/11219313">GRIT domain polygon</a> (GRITv06_domain_GLOBAL.gpkg.zip). They allow for more fine-grained subsetting of data .</p> <p>Vector files are provided in geographic WGS84 coordinates (EPSG:4326).</p> <p><strong>Change log</strong></p> <ul> <li>v0.1 - 2024-09-29<br> <ul> <li>First globally complete dataset published</li> </ul> </li> <li>v0.1 - 2024-11-19 <ul> <li>Add vector files for regions SI, SP</li> </ul> </li> </ul>
HarP: Harmonized Prior river-lake database
<p><strong>Contact</strong>: Md Safat Sikder (mssikder@illinois.edu), Jida Wang (jidaw@illinois.edu)</p> <p> </p> <p><strong>Citation</strong></p> <p>Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Crétaux, J.-F., and Pavelsky, T. M., 2024. HarP: Harmonized Prior river-lake database. <em>Zenodo</em>, <a href="https://doi.org/10.5281/zenodo.14205131">https://doi.org/10.5281/zenodo.14205131</a>.</p> <p>If you only use the PLD-TopoCat dataset, please cite the following paper:</p> <p>Sikder, M. S., Wang, J., Allen, G. H., Sheng, Y., Yamazaki, D., Song, C., Ding, M., Crétaux, J.-F., and Pavelsky, T. M., 2023. Lake-TopoCat: A global lake drainage topology and catchment dataset. <em>Earth System Science Data</em>, 15, 3483-3511, <a href="https://doi.org/10.5194/essd-15-3483-2023">https://doi.org/10.5194/essd-15-3483-2023</a>.</p> <p> </p> <p><strong>Data description and components</strong></p> <p><strong>The Harmonized Prior river-lake database (HarP) for SWOT</strong> integrated the SWOT River Database (SWORD) (<em>Altenau et al.</em>, 2021) and the SWOT Prior Lake Database (PLD) (<em>Wang et al.</em>, 2023) into <strong>a geometrically (lake/river) explicit but topologically harmonized vector database</strong> to allow for coupled fluvial-lacustrine applications, including a synergistic use of both river and lake products from SWOT. </p> <p>In addition to the input river network (SWORD v16) and lake database (PLD v106), we used the MERIT Hydro v1.0.1 (<em>Yamazaki et al.</em>, 2019), a high-resolution (~90 m) global hydrography dataset, to develop this database.</p> <p>The SWORD-PLD harmonization process involves three major steps, with Step 3 being divided into three sub-steps. The processing chain is illustrated in the attached Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>", as well as in Section 2 of the product description document. The HarP database consists of the outputs from each of the steps. For convenience, the global landmass (excluding Antarctica) was partitioned to 68 Pfafstetter Level-2 basins/regions, with their IDs shown in Figure "<em>Pfaf2_basins.jpg</em>" attached.</p> <p> </p> <p>The HarP database consists of five datasets or components (outputs from each step), each with multiple features. The five datasets are described below, and more details are elaborated in the product description document.</p> <p><strong>1. Harmonized SWORD-PLD </strong>(file name "<em>Harmonized_SWORD_PLD</em>"): This is the fully harmonized SWORD-PLD dataset, <strong>the primary product of HarP </strong>(i.e., output of Step 3.3 in Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>"). This dataset couples SWORD and PLD into a geometrically segmented but topologically integrated dataset at the node, reach, and catchment scales (stored by three feature layers, respectively): </p> <p> (a) Harmonized feature nodes: Harmonized_feature_nodes_pfaf_xx<br> (b) Harmonized river network: Harmonized_river_network_pfaf_xx<br> (c) Harmonized feature catchments: Harmonized_feature_catchments_pfaf_xx<br> Note: ''pfaf_xx'' indicates the Pfafstetter Level-2 basin ID (shown in Fig. 'Pfaf2_basins.jpg').</p> <p>Figure "<em>HarP_example.jpg</em>", attached to this database, is an example of the fully harmonized SWORD-PLD dataset for the Ohio River Basin. The example shows three main features of the dataset: feature nodes (i.e., reach downstream ends, lake inlets, and lake outlets; see Fig. 3 in the product description document for definitions), river reaches (i.e., reaches characterized by SWORD alone, characterized by TopoCat alone, and shared by both SWORD and TopoCat), and catchments segmented by each of the feature nodes.</p> <p><strong>2. Intersected SWORD-PLD drainage configuration </strong>(file name "<em>Intersected_SWORD_PLD</em>"): This dataset is the intersected SWORD-PLD (prior river-lake) features (i.e., output of Step 2 in Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>"). This dataset was constructed independently from Step 1 and Step 3. In this dataset, the original geometries of SWORD and PLD are not altered, but instead, their geometric and drainage topological relationships are configured in the attribute tables. This dataset consists of three features:</p> <p> (a) Intersected reaches: Intersected_SWORD_reaches_pfaf_xx<br> (b) Intersected nodes: Intersected_SWORD_nodes_pfaf_xx<br> (c) Intersected lakes: Intersected_PLD_lakes_pfaf_xx</p> <p><strong>3. PLD-TopoCat </strong>(file name "<em>PLD_TopoCat</em>"): This dataset is the lake drainage topology and catchments (TopoCat) for PLD lakes (i.e., output of Step 1 in Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>"). PLD-TopoCat was developed to generate detailed lake drainage topology and connecting paths, which were later used to configure the off-SWORD-network PLD lakes into the tributaries that drain to SWORD. PLD-TopoCat was generated from PLD v106 and MERIT Hydro. Details of the developiong process and algorithm for TopoCat can be found at Sikder at al., (2023). PLD-TopoCat dataset contains six features:</p> <p> (a) Lake original polygon: PLD_lakes_pfaf_xx<br> (b) Lake raster polygon: Lake_raster_polygons_pfaf_xx<br> (c) Lake outlets: Lake_outlets_pfaf_xx<br> (d) Lake catchments: Lake_catchments_pfaf_xx<br> (e) Inter-lake reaches: Inter_lake_reaches_pfaf_xx<br> (f) Lake-network basins: Lake_network_basins_pfaf_xx<br> Note: full version of the PLD-TopoCat is available <a href="https://doi.org/10.5281/zenodo.14202301">here</a>.</p> <p><strong>4. SWORD-mirror network </strong>(file name "<em>SWORD_mirror</em>"): The SWORD-mirror network was constructed to facilitate the SWORD-TopoCat network merging process (i.e., output of Step 3.1 in Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>"). It is essentially <strong>a replica of SWORD except that the original SWORD reaches are geometrically modified to be aligned with the topological/hydrographic information depicted in MERIT Hydro</strong>. The SWORD-mirror network consists of four features:</p> <p> (a) SWORD-original reaches: SWORD_original_reaches_pfaf_xx<br> (b) SWORD-mirror prelim. reaches: SWORD_mirror_prelim_reaches_pfaf_xx<br> (c) SWORD-mirror reaches: SWORD_mirror_reaches_pfaf_xx<br> (d) SWORD-mirror reach catchments: SWORD_mirror_reach_catchments_pfaf_xx</p> <p><strong>5. Merged SWORD-mirror – TopoCat network </strong>(file name "<em>SWORD_TopoCat_merged</em>"): This dataset is the output of Step 3.2 in Figure "<em>SWORD-PLD_harmonization_steps.jpg</em>". It is essentially the merged product of the inter-lake reaches (from Step 2) and SWORD-mirror reaches (from Step 3.1). The merged SWORD-mirror – TopoCat network consists of three features:</p> <p> (a) Merged SWORD-TopoCat reaches: SWORD_TopoCat_merged_reaches_pfaf_xx<br> (b) SWORD nodes at SWORD-TopoCat confluence: SWORD_TopoCat_confluence_nodes_pfaf_xx<br> (c) Reach catchments for merged network: SWORD_TopoCat_reach_catchments_pfaf_xx</p> <p>The attribute tables for each of the feature components are explained in Section 4 of the product description document. All files of HarP are available in both shapefile and geodatabase formats.</p> <p> </p> <p><strong>Disclaimer</strong><br>Authors of this dataset claim no responsibility or liability for any consequences related to the use, citation, or dissemination of HarP. For any quesitons, please contact Safat Sikder and Jida Wang.</p>
RivFISH - An European database on fish species presence across river basins
<p>The RivFISH database aggregates the available data on freshwater-dependent fish presence in Europe, validated at the river basin level and considering taxonomical synonyms for species names, thus allowing for a maximization of data usage and robustness. This database also promotes interoperability with other datasets, including the IUCN Red List of Threatened Species, FishBase and the Catchment Characterisation and Modelling (CCM2) – River and Catchment Database v2.1. It is, as far as the authors know, the most up-to-date and comprehensive database on the presence of freshwater-dependent fish species for European river basins. The structure of the database is also prepared to deal with future alterations in species taxonomy, as well as new records of species occurrence in river basins.</p>
Distribution of waterbirds along the Drugeon river, France
<p>This dataset describes bird observations performed along the Drugeon river, France from 2006 to 2019.</p> <p>'<a href="https://zenodo.org/record/4540002/files/Carte.jpg?download=1"><strong>Carte.jpg</strong></a>' Map of the study area. The box shows the location of the Bannans and the Sainte-Colombe transects. Village locations and the route of the river have been taken from 'BD Carto ', kindly provided for research by the National Geographical Institute, and modified on the basis of field observations.</p> <p>'<a href="https://zenodo.org/record/4540002/files/Mat%20sup%201%20transects%20Drugeon%20synthe%CC%80se_Zenodo.xlsx?download=1"><strong>Mat sup 1 transects Drugeon synthèse_Zenodo.xlsx</strong></a>' since September 2006, two transects were carried out four times a fortnight,on foot in the early morning. All identified birds, by sight or by ear, were noted with special care to avoid double counting. The two transects are located in the downstream part of the Drugeon valley. The Bannans transect starts from the Drugeon diversion upstream from the Bannans flour mill and runs along the river downstream to the place called Mitray, with a lateral extension to the En Vau-Les-Aigues marsh located partly in the La Rivière-Drugeon village. This transect is approximately 5.4 km long, of which 1.4 km follow the river. It sampled the bird population of the river, the marshes more or less wooded with willows, birches and a few spruces, and the neighboring meadows and pastures. The flour mill dam provides a 3660 square meter body of water that is not flushed out because it is attached to a dwelling house. This is the only non-huntable wetland area along the two transects. The Sainte-Colombe transect starts from the village, crosses the mesophilic and then wet meadows to reach almost the downstream end of the Bannans transect. It then runs along the river in its undisturbed part to the Chaffois mill. The return to the village is via an agricultural path that crosses mesophilic and humid meadows, a marsh and a small wood of poplar and spruce trees. This transect is 8.6 km long including 2 km along the river. A U-shaped pond carved out of a marsh provides an open water surface of approximately 3400 square meters. It was also prospected during the transect.</p> <p>'<a href="https://zenodo.org/record/4540002/files/Mat%20sup%202%20donne%CC%81es%20brutes%20descente%20du%20Drugeon.xlsx?download=1"><strong>Mat sup 2 données brutes descente du Drugeon.xlsx</strong></a>' covers the censuses along the course of the Drugeon river from 2014 to 2019. Birds were recorded during a course carried out on foot along the river on a bank from the Dompierre bridge located between Vaux-et-Chantegrue and Bonnevaux villages to the Pont Rouge bridge next to Vuillecin village (29,2 km representing almost the entire course of the Drugeon river). Each year, the census was taken in sections during the second half of October, an average of one to one and a half months after the opening of the hunting season. Each waterbird observed was precisely located on a map and then plotted on Google Earth. Using Géoportail and field surveys, a precise description of the watercourse has been carried out on the whole of the prospected area: environment bordering each bank (forest, wooded marsh, herbaceous marsh, meadow, village), vegetation on each bank (continuous willow, megaphorbiaie with scattered willows, pure megaphorbiaie, phragmitaie, short herbaceous vegetation), width of the river, slope of the watercourse, status with respect to hunting (huntable zone, zone not huntable because located less than 150 m from homes and hunting reserve).</p> <p><em>The figures of the following three files are computed from the two files above: Mat sup 1 and Mat sup 2:</em></p> <p><strong>'<a href="https://zenodo.org/record/4540002/files/Mat sup 3 Down river walk.zip?download=1">Mat sup 3 Down river walk.zip</a>'</strong> Distribution and abundance 2014-2019 of the main waterbird species along the Drugeon river</p> <p><strong>'<a href="https://zenodo.org/record/4540002/files/Mat sup 4 Hunting season onset.zip?download=1">Mat sup 4 Hunting season onset.zip</a>'</strong> Abundance of the Anatidae species on the Bannans and Sainte-Colombe transects according to the opening date of waterbird hunting</p> <p><strong>'<a href="https://zenodo.org/record/4540002/files/Mat sup 5 Bannans transect.zip?download=1">Mat sup 5 Bannans transect.zip</a>'</strong> Abundance of the Anatidae species on the Bannans and Sainte-Colombe transects according to the opening date of waterbird hunting</p> <p><strong>'<a href="https://zenodo.org/record/4540002/files/transectsDomi.kml?download=1">TransectsDomi.kml</a>' </strong>a kml file locating the Bannans and Sainte-Colombe transects (polylines).</p>
Field and laboratory measurements of suspended-sediment particle size and concentration from nine rivers draining to the Great Barrier Reef
<p>Dataset includes in-situ (n = 144,912) and laboratory-dispersed (n = 64) particle size measurements collected using laser diffractometry from nine rivers discharging along 800 km of Great Barrier Reef, Queensland Australia coastline. Two field campaigns (24 February to 5 March 2021 and 24<sup>th</sup> to 31<sup>st</sup> of April 2021) were undertaken to collect vertical profiles of in-situ particle size, water velocity, turbidity, and salinity. Water samples were collected for analysis of laboratory-dispersed particle size and suspended-sediment concentration. Water samples were collected using US-P61 or Van-Dorn samplers deployed alongside a LISST 200x laser diffractometer and an EXO2 YSI multiparameter water quality sonde. Total depth and water velocity were measured using a Nortek Signature ADCP and Teledyne RiverRay ADCP during the first and second field campaigns, respectively. During both campaigns, measurements were undertaken during relatively high discharge events when discharge exceeded the 90<sup>th</sup> percentile of 2020/2021 gauged wet season flows.</p> <p>Data are provided in three csv files. "In_situ_data.csv" contains in situ measurements of particle size, turbidity, salinity, and depth along with estimates of shear rate. Shear rate is estimated from theory and measurements of ADCP-measured total depth and depth-averaged flow (see equation 2 of Livsey et al., 2022). "Lab_data_this_study.csv" contains particle size measurements of suspended-sediment following laboratory dispersion along with coeval measurements of in-situ particle size, turbidity, salinity, and shear rate averaged over the filling time of the US-P61 sampler. "Lab_data_DES_WQI.csv" contains laboratory dispersed particle size measurements collected by the Department of Environment and Science Water Quality Investigation Unit of Queensland Australia (Turner et al., 2013) and compared to data in "Lab_data_this_study.csv" in Livsey et al (2022). </p> <p>Further details of the data collection effort and interpretation of the data are published in Livsey et al (2022) at https://doi.org/10.1029/2021JC017988. </p> <p>Additional data from the 24 February to 5 March 2021 field campaign, funded by CSIRO Oceans and Atmosphere, are available from Crosswell et al (2022) at https://doi.org/10.25919/2vbh-cx08.</p> <p>References:</p> <p>Crosswell, Joey; Carlin, Geoff; Daniel, Livsey; Hillyer, Katie; Steven, Andy (2022): FNQ_2021_V01 Voyage dataset: Feb - March 2021; Biogeochemical and hydrodynamic obervations along the river-reef continuum of estuaries in eastern Cape York, Australia. v1. CSIRO. Data Collection. 10.25919/2vbh-cx08</p> <p>Livsey, D. L., Crosswell, J. R., Turner, R. R., Steven, A. D. L., & Grace, P. R. (2022) Flocculation of riverine sediment draining to the Great Barrier Reef, implications for monitoring and modelling of sediment dispersal across continental shelves. Journal of Geophysical Research: Oceans. https://doi.org/10.1029/2021JC017988</p> <p>Turner. R, Huggins. R, Wallace. R, Smith. R, Vardy. S, Warne. M St. J. (2013). Total suspended solids, nutrient, and pesticide loads (2010-2011) for rivers that discharge to the Great Barrier Reef Great Barrier Reef Catchment Loads Monitoring 2010-2011 Department of Science, Information Technology, Innovation and the Arts, Brisbane.</p> <p> </p> <p> </p>
Regional Flood Frequency Analysis of the Sava River in South-Eastern Europe
<p>Journal: Sustainability</p> <p>Abstract: Regional flood frequency analysis (RFFA) is a powerful method for interrogating hydrological series since it combines observational time series from several sites within a region to estimate risk-relevant statistical parameters with higher accuracy than from single-site series. Since RFFA extreme value estimates depend on the shape of the selected distribution of the data-generating stochastic process, there is need for a suitable goodness-of-distributional-fit measure in order to optimally utilize given data. Here we present a novel, least-squares-based measure to select the optimal fit from a set of five distributions, namely Generalized Extreme Value (GEV), Generalized Logistic, Gumbel, Log-Normal Type III and Log-Pearson Type III. The fit metric is applied to annual maximum discharge series from six hydrological stations along the Sava River in South-eastern Europe, spanning the years 1961 to 2020. Results reveal that (1) the Sava River basin can be assessed as hydrologically homogeneous and (2) the GEV distribution provides typically the best fit. We offer hydrological‒meteorological insights into the differences among the six stations. For the period studied, almost all stations exhibit statistically insignificant trends, which renders the conclusions about flood risk as relevant for hydrological sciences and the design of regional flood protection infrastructure.</p> <p>URL: https://www.mdpi.com/2071-1050/14/15/9282</p> <p>The uploaded datasets are the Annual Maximum Series of Sava River runoff for the six analysed hydrological stations: Radovljica, Čatež, Zagreb, Jasenovac, Županja and S. Mitrovica.</p>
MOdern River archivEs of Particulate Organic Carbon: MOREPOC
<p>Modern River Archives of Particulate Organic Carbon (MOREPOC) version 1.1 is a new, open-access, georeferenced, global database, featuring data on POC in suspended particulate matter (SPM) collected at 233 locations across 121 major river systems. This database includes 3,546 SPM data entries, among which 3,053 with POC content, 3,402 with stable carbon isotope (δ<sup>13</sup>C) values, 2,283 with radiocarbon activity (Δ<sup>14</sup>C) values, 1,936 with total nitrogen content, and 299 with aluminum-to-silicon mass ratios (Al/Si). The MOREPOC database aims at being used by the Earth System community to build comprehensive and quantitative models for the mobilization, alteration, and fate of terrestrial POC.</p> <p>The supply of particulate organic carbon (POC) associated with terrigenous solids transported to the ocean by rivers plays a significant role in the global carbon cycle. To advance our understanding of the source, transport, and fate of fluvial POC from regional to global scales, databases of riverine POC are needed, including elemental and isotope composition data from contrasted river basins in terms of geomorphology, lithology, climate, and anthropogenic pressure. MOREPOC will benefit the scientific community carrying out research on riverine POC sources, transport, and fate, furthermore, helping inform and validate Earth system models to improve the ability to model and understand the global carbon cycle. Existing environmental raster global datasets for climate, geomorphology, lithology, tectonics, hydrology, and land use, also offer promising prospects for the use of MOREPOC for identifying the controls on POC fluxes and composition, in particular using advanced statistical analysis or machine learning techniques. Moreover, MOREPOC enables a better understanding of sources, transport, and fate of fluvial POC combined with some existing ocean sediment databases. Future updates of MOREPOC should include new bulk POC parameters as well as data on molecular fractions, thermal labile fractions, or specific components such as black carbon or fossil carbon, which should, in turn, provide additional insight into the alteration of riverine POC from source to sink, an essential feature of the global carbon cycle.</p> <p><strong>Data description</strong></p> <p>The MOREPOC database consists of two parts: 1) the master metadata (MOREPOC_v1.1); 2) the summarization of references and methods (MOREPOC_v1.1_RM). A Readme is provided to better understand all parameters provided in the MOREPOC v1.1 database. </p> <p>MOREPOC_v1.1 includes one table, avaible as Excel spreadsheet (.xslx), comma-limited table (.csv), and GIS shapefile (compiled in .rar) using WGS84 coordinate system.</p> <ul> <li>MOREPOC_v1.1.xlsx</li> <li>MOREPOC_v1.1.csv</li> <li>MOREPOC_v1.1.rar (GIS shapefile)</li> </ul> <p>MOREPOC_v1.1_RM only provides one table, avaible as Excel spreadsheet (.xslx), comma-limited table (.csv).</p> <ul> <li>MOREPOC_v1.1_RM.xlsx</li> <li>MOREPOC_v1.1_RM.csv</li> </ul> <p>The database structure of MOREPOC is listed in Table.1 to understand all provided parameters, more information can be found in the companion manuscript.</p> <table> <caption><strong>Table. 1 Description of the parameters of the MOREPOC v1.1 database.</strong></caption> <tbody> <tr> <td><strong>Parameter</strong></td> <td><strong>Description</strong></td> <td><strong>MOREPOC column name</strong></td> </tr> <tr> <td>River name</td> <td>Name of the major river basin</td> <td>bas_id</td> </tr> <tr> <td>Sub river name</td> <td>Name of the sampled river/stream</td> <td>riv_id</td> </tr> <tr> <td>Country</td> <td>Name of country or places</td> <td>country</td> </tr> <tr> <td>Continent</td> <td>Name of the continent</td> <td>cont</td> </tr> <tr> <td>Sampling site/code</td> <td>Expedition sampling ID</td> <td>code</td> </tr> <tr> <td>Sampling date</td> <td>Time (month/day/year) when the SPM sample was collected</td> <td>time_m/d/y</td> </tr> <tr> <td>Latitude</td> <td>Decimal latitude using WGS 1984</td> <td>lat</td> </tr> <tr> <td>Longitude</td> <td>Decimal longitude using WGS 1984</td> <td>lon</td> </tr> <tr> <td>Sampling technique</td> <td>Method of SPM sampling</td> <td>type_spm</td> </tr> <tr> <td>Size fraction of SPM</td> <td>Reported size fractions analyzed</td> <td>fra_spm</td> </tr> <tr> <td>SPM concentration (mg/L)</td> <td>The total dry weight of SPM in mg per liter water column</td> <td>conc_spm</td> </tr> <tr> <td>POC concentration (mg/L)</td> <td>The total dry weight of POC in mg per liter water column</td> <td>conc_poc</td> </tr> <tr> <td>POC content (%)</td> <td>The total POC content of SPM in wt %</td> <td>per_poc</td> </tr> <tr> <td>POC content uncertainty (1σ)</td> <td>The analytical uncertainty for POC content (1σ)</td> <td>perc_poc_1sd</td> </tr> <tr> <td>δ<sup>13</sup>C (‰)</td> <td>δ<sup>13</sup>C values of POC (carbonate removed) in ‰</td> <td>d13C_poc</td> </tr> <tr> <td>δ<sup>13</sup>C uncertainty (1σ)</td> <td>The analytical uncertainty for δ<sup>13</sup>C of POC</td> <td>d13C_1sd</td> </tr> <tr> <td>Δ<sup>14</sup>C (‰)</td> <td>Δ<sup>14</sup>C values of POC (carbonate removed) in ‰</td> <td>D14C_poc</td> </tr> <tr> <td>Δ<sup>14</sup>C uncertainty (1σ)</td> <td>The analytical uncertainty for Δ<sup>14</sup>C of POC</td> <td>D14C_1sd</td> </tr> <tr> <td>Fraction modern (Fm)</td> <td>Fraction modern of POC</td> <td>F14C</td> </tr> <tr> <td>Radiocarbon ages (year)</td> <td>Radiocarbon ages before present (1950)</td> <td>age_14C</td> </tr> <tr> <td>TN content (%)</td> <td>The total nitrogen content of SPM in wt %</td> <td>perc_tn</td> </tr> <tr> <td>C<sub>org</sub>/N mass ratio</td> <td>Mass ratio of POC to TN in SPM</td> <td>cn_ratio</td> </tr> <tr> <td>Al/Si mass ratio</td> <td>Mass ratio of Al to Si in SPM</td> <td>alsi_ratio</td> </tr> <tr> <td>Reference</td> <td>Full list of citations of the data source</td> <td>ref</td> </tr> <tr> <td>Complete reference</td> <td>Complete information for cited references</td> <td>ref_c</td> </tr> <tr> <td>Measured parameters</td> <td>Summarization of elemental and isotopic carbon parameters measured</td> <td>para_m</td> </tr> <tr> <td>Calculated parameters</td> <td>Summarization of elemental and isotopic carbon parameters calculated</td> <td>para_c</td> </tr> <tr> <td>Filter</td> <td>Filter used to obtain SPM</td> <td>filter</td> </tr> <tr> <td>Acid</td> <td>The acid type used to remove carbonate in SPM</td> <td>acid</td> </tr> <tr> <td>Carbonate removal method</td> <td>The method used to remove carbonate in SPM</td> <td>m_acid</td> </tr> <tr> <td>Acid concentration</td> <td>The concentration of adopted acid to remove carbonate in SPM</td> <td>conc_acid</td> </tr> <tr> <td>carbonate removal temperature</td> <td>The environmental temperature for acid to remove carbonate in SPM</td> <td>temp_acid</td> </tr> <tr> <td>Carbonate removal duration</td> <td>The reaction time used for acid to remove carbonate in SPM</td> <td>time_acid</td> </tr> <tr> <td>Note</td> <td>Additional information for carbonate removal process</td> <td>note</td> </tr> </tbody> </table> <p><strong>Contributing Data</strong></p> <p>Please contact Yutian Ke at <a href="mailto:yutianke@caltech.edu">yutianke@caltech.edu</a> or <a href="mailto:yutian.ke@universite-paris-saclay.fr">yutian.ke@universite-paris-saclay.fr</a> if you are interested in contributing your published or unpublished data to MOREPOC.</p> <p><strong>Citation</strong></p> <p>Ke, Y. T., Calmels, D., Bouchez, J., Cécile, Q.: MOdern River archivEs of Particulate Organic Carbon: MOREPOC, Dataset version 1.1, Zenodo [dataset], <a href="https://doi.org/10.5281/zenodo.6541925">https://doi.org/10.5281/zenodo.7055970</a>.</p>
Raw and processed hydro-meteorological variables of Jucar river basin for feature selection
<p>The dataset Processed data – input WQEISS.csv was employed for the input variable selection step in Zaniolo et al., 2018. It includes monthly values of 28 hydro-meteorological variables and indexes of Jucar river basin, Spain, for the period 1986-2000, namely:</p> <ul> <li>2 temporal features: day and month of the year;</li> <li>12 inputs to the Jucar State Index: average monthly storage and groundwater levels, average three months river runoff, and cumulated areal precipitation over 12 months;</li> <li>8 additional observed variables in the basin: three months average outflows from, and inflows to, the main reservoirs, and mean monthly areal temperatures;</li> <li>6 traditional drought indicators: Standardized Precipitation Index (SPI) and Standardized Precipitation and Evaporation Index (SPEI). SPI and SPEI indicators are computed on mean monthly data over the entire basin for 3, 6, and 12 months time aggregations.</li> </ul> <p>The last column of the dataset reports the target variable, i.e., the monthly nominal shortage of water conveyed to the irrigation districts simulated via AQUATOOL model. For further details on the dataset please consult Zaniolo et al., 2018, or the dedicated website <a href="http://www.nrm.deib.polimi.it/?page_id=2438">http://www.nrm.deib.polimi.it/?page_id=2438</a></p> <p>The unprocessed data used to compute indices and temporal cumulations in Processed data – input WQEISS.csv are reported in table Raw Data.csv. Public observations of rainfall, streamflows and storage levels come from the SAIH (Hydrological Automatic Information System) of the CHJ (Jucar Hydrological Confederation). Users can directly download data for the last 12 months on the dedicated webpage <a href="http://saih.chj.es/chj/saih/?f">http://saih.chj.es/chj/saih/?f</a> while previous data records are provided for free by CHJ upon request. Observations from piezometers are downloadable from the Piezometric Network Information section section of the CHJ <a href="https://www.chj.es/es-es/medioambiente/redescontrol/Paginas/Piezometr%C3%ADa.aspx">https://www.chj.es/es-es/medioambiente/redescontrol/Paginas/Piezometr%C3%ADa.aspx</a>.</p>
Data From: The Oyster River Protocol: A multi assembler and kmer approach for de novo transcriptome assembly.
<p>Characterizing transcriptomes in non-model organisms has resulted in a massive increase in our understanding of biological phenomena. This boon, largely made possible via high-throughput sequencing, means that studies of functional, evolutionary and population genomics are now being done by hundreds or even thousands of labs around the world. For many, these studies begin with a <em>de novo</em> transcriptome assembly, which is a technically complicated process involving several discrete steps. The Oyster River Protocol (ORP), described here, implements a standardized and benchmarked set of bioinformatic processes, resulting in an assembly with enhanced qualities over other standard assembly methods. Specifically, ORP produced assemblies have higher Detonate and TransRate scores and mapping rates, which is largely a product of the fact that it leverages a multi-assembler and kmer assembly process, thereby bypassing the shortcomings of any one approach. These improvements are important, as previously unassembled transcripts are included in ORP assemblies, resulting in a significant enhancement of the power of downstream analysis. Further, as part of this study, I show that assembly quality is unrelated with the number of reads generated, above 30 million reads. Code Availability: The version controlled open-source code is available at <a href="https://github.com/macmanes-lab/Oyster_River_Protocol">https://github.com/macmanes-lab/Oyster_River_Protocol</a>. Instructions for software installation and use, and other details are available at <a href="http://oyster-river-protocol.rtfd.org/">http://oyster-river-protocol.rtfd.org/</a>.</p>
AMnrGC - Amazon river non-reduntant microbial genes catalogue
<p> </p> <p><strong>AMnrGC : Amazon river basin non-redundant microbial gene catalogue</strong></p> <p> </p> <p> RELEASE 2018/01<br> --------------------------------------</p> <p> </p> <p>1. INTRODUCTION</p> <p> AMnrGC is a collection of genes and proteins which were constructed<br> by use of Amazon river basin openly available metagenomes from<br> sequencing projects (SRP044326, PRJEB25171 and SRP039390). Briefly,<br> metagenomes were coassembled by groups made up their geographical<br> location with Megahit v.1.0 and the contigs were used to gene predictions<br> by Prodigal v.2.6.3. Genes sequences were length filtered (> 150 bp) and<br> clustered by CD-HIT-EST (version 4.6) at 95% of nucleotide identity and<br> 90% of overlap of the shorter gene. Theorical protein products were annotated<br> by the most completes databases up to date and their complete information<br> is available here.</p> <p> </p> <p>2. LOCATION</p> <p> AMnrGC versions will be available on the web only under the current ZENODO<br> repository: 10.5281/zenodo.1484504</p> <p> </p> <p>3. FORMAT</p> <p> Gene entries were named as ">AM_AGSSY_XXX" where XXX represents an unique numerical<br> identifier. Genes were deposited in their coding phase, because of this, all of them<br> can be used to generate the protein sequences by transeq function at ORF+1.<br> The protein entries correspond to genes artifical translation used in the annotations,<br> and also available, codified in the same way, but containing the indication "_1" in<br> the end of the header. Example:</p> <p> Gene:<br> >AM_AGSSY_151515</p> <p> Protein:<br> >AM_AGSSY_151515_1</p> <p> Annotations were provided as separate tables for each database used to annotate the<br> sequences. The header of these tables indicates the meaning of each value.</p> <p> </p> <p>2. FUTURE FORMAT CHANGES</p> <p> No major changes are expected for the main general format of the database.<br> New versions should include updated versions of annotations or even additional sequences,<br> numbered as subsequent entries.</p> <p> </p> <p>3. ACKNOWLEDGEMENTS<br> <br> This work is a joint effort of Laboratory of molecular biology from Federal<br> University of São Carlos, São Paulo, Brazil (LBM/UFSCAR) and Protists group<br> of Institut del Ciencias del Mar, Barcelone, Spain (ICM). We are grateful to<br> Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), as well as, the spanish funding organ Consejo Superior de Investigaciones Científicas (CSIC).</p> <p> </p> <p> This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.</p> <p> </p> <p>4. THE AMnrGC TEAM</p> <p> AMnrGC is maintained by a group of researchers. You can contact<br> the AMnrGC consortium.<br> <br> Current curators:</p> <p> - Célio Dias Santos Júnior (celio.diasjunior@gmail.com)<br> - Flavio Henrique-Silva (dfhs@ufscar.br)<br> - Ramiro R. Logares (ramiro.logares@icm.csic.es)<br> </p> <p>5. COPYRIGHT NOTICE</p> <p> AMnrGC - Amazon river basin non-redundant microbial gene catalogue<br> Copyright (C) 2018 The AMnrGC consortium.</p> <p> This database is provided “as is” and without any warranty of any kind,<br> of openly available for non-commerical purposes. You can redistribute and/or modify it<br> as you wish, under the terms of the ODBL 1.0 license:</p> <p> https://opendatacommons.org/licenses/odbl/1.0/</p> <p> For commercial purposes, please contact us. </p> <p>___________________</p> <p>The AMnrGC Consortium<br> 2018</p>
Seasonal streamflow hindcast data for the Upper Segura and Upper Tagus river basins
<p>The datasets provided here have been produced as part of the IMPREX project for work package 11, task 2. The aim was to evaluate the performance of climate model-based seasonal hydrological forecasting system that is used to predict drought indices of the Segura River Basin and the Tajo-Segura Water transfer System. These indices rely on forecasted discharge values and describe the expected status of the water availability in both systems. Also, for the Tagus basin, the forecasts can be used to determine the amount of water transferred to the Segura river basin. The key involved stakeholder is the River Basin Authority of the Segura Basin that uses these drought indices for drought mitigation actions. This task developed and evaluated a forecasting system for these drought indices.</p> <p>The dataset - <strong>Pseudo_Obs_SPHY_Spain02_Output.csv</strong> include streamflow simulations for the Entrepenas and Buendia discharge stations located in the Upper Tagus basin, Spain in addition to the Fuensanta and Cenajo discharge stations located in the Segura river basin, Spain. Hydrological outputs are produced using The Spatial Processes in Hydrology (SPHY) model (Terink et al., 2015) forced by the Spain02 V4 meteorological dataset (Herrera et al., 2016) for the period 1979-2010. Please note however that there are known issues with the Spain02 dataset for the years 2007-2010 that affect this dataset.</p> <p>The dataset <strong>- SPHY_ECMWF_S5_Output.csv </strong>includes streamflow simulations for the same locations using the SPHY model albeit forced with the ECMWF SEAS5 hindcast data for the period 1981-2010. The column “LD” represents lead month where the first month of hindcasts is signaled as 1, second as 2 and third as 3. The “Ens” column refers to the individual ensemble members as 25 in total are used for this setup.</p>
data-base of CO2, CH4, N2O and ancillary data in the Congo River
<p>data-base of CO2, CH4, N2O and ancillary data in the Congo River relative to paper "Variations of dissolved greenhouse gases (CO2, CH4, N2O) in the Congo River network overwhelmingly driven by fluvial-wetland connectivity" by Borges et al. (https://doi.org/10.5194/bg-2019-68)</p>
Dataset of nitrogen in rivers and streams in sub-Saharan Africa
<p>This is a dataset on concentrations and export of all nitrogen compounds in rivers and streams in sub-Saharan Africa reported in scientific literature (<em>n</em>=254) until July 2024. Data are aggregated by site and, where possible, data are reported for the dry and wet season separately. In addition to concentrations and export of nitrogen compounds, data on ancillary parameters, such as pH, electrical conductivity and dissolved oxygen area also included. Each site for which (approximate) coordinates could be extracted from the original study was assigned to a land cover class based on open source data on tree cover and the extent of cropland, settlement and wetlands across Africa (see second tab in the file 'Dataset.xlsx' for land cover classes and corresponding classification conditions as well as data sources). The third tab in the file 'Dataset.xlsx' contains links to the individual studies and full references are provided in the file 'Reference list.pdf'.</p>
Water quality data (River sediment, Nitrogen and Phosphorus loads) for Africa
<p>Output data on African water quality and scripts for preprint - "One third of African rivers fail to meet the 'good ambient water quality' nutrient targets" at <a href="https://dx.doi.org/10.2139/ssrn.4829742">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4829742</a> . Please check the readme file for data description. The data includes river flow, sediment load, nitrogen and Phosphorus loads for Africa at daily and yearly time scale. This work is currently under review in Ecological Indicators journal. </p>
NORDBALT-ECOSAFE WP1 River and Lake dataset
<p>Datasets used for analysis of good/moderate boundaries in river and lake types in the Nordic-Baltic region of Europe in an EU project named NORDBALT-ECOSAFE (see project homepage: for more information <a href="https://projects.au.dk/nordbalt-ecosafe">Nordbalt-Ecosafe (au.dk))</a></p> <p> </p>
Two decades of body length measurements in size-structured larval and juvenile fish populations in English rivers.
<p>Long term ecological datasets are valuable in providing context and understanding to complex ecological processes that occur over broad temporal scales, and provide a baseline for analysing change. Monitoring of fish populations in UK waterbodies and elsewhere is typically through measuring the length of individual fish caught in surveys. Through this method, the age structure of fish populations can be determined, as well as over winer survival rates and future recruitment success and cohort sizes can be predicted. The larval and juvenile period are when fish are considered most vulnerable to predation, competition, disease and environmental perturbations. </p> <p><br>This study presents the first long-term larval and juvenile fish lengths dataset for 67 survey sites over two decades (1999-2018) from the rivers Ancholme, Warwickshire Avon, Don, Trent, and Yorkshire Ouse (including the Swale, Ure, Nidd and Wharfe) in the United Kingdom. These rivers represent a range of topographical and biotopical characteristics. For the majority of this study, surveys were conducted on a monthly or fortnightly basis making both annual and seasonal analyses of size structure, growth and body length possible. Although there is some variation in the sampling frequency and some locations varied throughout the study according to requirements. In total, more than 380,000 larval or juvenile fish of 30 species were measured, likely representing one of the most comprehensive datasets of its type.</p> <p>Surveys were conducted in river margins, where the velocity was slowest and larval and juvenile fish tend to aggregate. Fish were captured using a 25 x 3 m micromesh (3 mm mesh size) seine net that was set in a rectangle parallel to the bank. This net capture fish as small as 5 mm and is the most appropriate method of catching larvae and juvenile fish, although occasionally some larger adult fish may have also been captured and measured as part of this dataset for completeness. All fish were identified to species and measured to standard length (mm) and released at the point of capture. The exception was the smallest larvae, which were euthanised with an overdose of methanesulphonate (MS-222) and preserved in 4% formalin solution for microscopic examination.</p> <p><br>The dataset contains 384,090 rows and 13 columns. Each row corresponds to a single fish that was measured at each site and date. Associated site information (site name, location, area fished (m<sup>2</sup>) and survey date) is reported for each row. When only a fraction of the catch was processed, the sub-sample size was reflected in the Count column (e.g. when half the sample was processed, the numbers of fish measured or only counted were multiplied by two). This enables accurate densities to be calculated as the total number of both measured and unmeasured fish is recorded.</p> <p>Description of columns found in the dataset:</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>Column heading</strong></p> </td> <td> <p><strong>Column description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Units</strong></p> </td> </tr> <tr> <td> <p>Fish _Catchment</p> </td> <td> <p>The river catchment/basin location of each fish site</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Fish_River</p> </td> <td> <p>The river/watercourse location of each fish site.</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Fish_SiteName</p> </td> <td> <p>The name of each fish site</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Fish_Latitude</p> </td> <td> <p>The latitude of each fish site (WGS 1984)</p> </td> <td> <p>Integer</p> </td> <td> <p>Decimal degrees</p> </td> </tr> <tr> <td> <p>Fish_Longitude</p> </td> <td> <p>The longitude of each fish site (WGS 1984)</p> </td> <td> <p>Integer</p> </td> <td> <p>Decimal degrees</p> </td> </tr> <tr> <td> <p>Fish_Area</p> </td> <td> <p>Area of fish site surveyed</p> </td> <td> <p>Integer</p> </td> <td> <p>m<sup>-2</sup></p> </td> </tr> <tr> <td> <p>Fish_SurveyDate</p> </td> <td> <p>Date fish survey was carried out</p> </td> <td> <p>Integer</p> </td> <td> <p>dd/mm/yyyy</p> </td> </tr> <tr> <td> <p>Fish_Year</p> </td> <td> <p>Year fish survey was carried out</p> </td> <td> <p>Integer</p> </td> <td> <p>yyyy</p> </td> </tr> <tr> <td> <p>Common_Name</p> </td> <td> <p>The common/vernacular name of each fish taxon recorded in the dataset.</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Latin_Name</p> </td> <td> <p>The scientific name of each fish taxon recorded in the dataset</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Net_Number</p> </td> <td> <p>The net number the fish in a given survey were caught on</p> </td> <td> <p>Integer</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Length_mm</p> </td> <td> <p>Length of individual fish caught</p> </td> <td> <p>Integer</p> </td> <td> <p>mm</p> </td> </tr> <tr> <td> <p>Count</p> </td> <td> <p>Count of fish caught accounting for sub- sampling</p> </td> <td> <p>Integer</p> </td> <td> <p>Number of fish</p> </td> </tr> </tbody> </table> <p> </p>
Data for manuscript titled 'Impact of urbanization and drought on river water quality, case study of nutrient levels in Cuenca and Giron (Azuay, Ecuador)'
<p>The uploaded zip-file entails the data obtained through four field campaigns performed in the province of Azuay (Ecuador) in the period July 2023 - May 2024, which is used as a basis for the manuscript titled 'Impact of urbanization and drought on river water quality, case study of nutrient levels in Cuenca and Giron (Azuay, Ecuador)' that was submitted to a Special Issue in the journal Water in 2024. The study aimed at illustrating the impact of urbanisation and drought on the abiotic water conditions of the rivers passing through the studied urban areas.</p> <p>The data includes a subfolder with data obtained from an external website (https://generacioncsr.celec.gob.ec/graficasproduccion/) and aligns with the folder structure of the GitHub-repository that contains the analysis scripts (to be added when the manuscript is accepted). The data file only contains the baseline data, while results can be obtained through running the R-scripts in the GitHub-repository. Additional comments on the analyses are also provided in the analysis scripts.</p> <p><strong>DATA COLLECTION</strong></p> <p>Information on the locations was collected prior to the first field campaign (July 2023) and confirmed in the field (and corrected when necessary). The following variables were registered: Date & Time, Coordinates (latitude and longitude, in WGS84 format), Altitude (in meters above mean sea level), Distance (to a fixed location downstream; being the province border), and Category (River or Stream).</p> <p>Information on the physicochemical conditions was collected directly in the field with a <strong>Horiba U-52</strong> multiprobe. The following variables were registered: Temperature, pH, Electrical conductivity (reference at 25 °C), Oxygen level (as concentration), and Turbidity (in NTU).</p> <p>At each site, a bucket was rinsed thrice with prevailing surface water and subsequently filled with a water sample of the top of the water column. The multiprobe was rinsed with this sample water and then submerged in the bucket, followed by continuous stirring (to avoid a decrease of the oxygen levels) until the readings stabilised. After stabilisation, readings were recorded on a separate data sheet prior to being digitalised.</p> <p>Information on the nutrient levels was obtained through the collection of water samples in the field and the subsequent analysis in the laboratory. The following nutrients were selected: ammonium, nitrate, nitrite, and orthophosphate. For the analyses, <strong>Merck test kits</strong> (equivalent to USEPA analyses) were used in combination with a Genesys UV-VIS spectrophotometer (Thermofisher).</p> <p><strong>In the field</strong>, a bucket was rinsed thrice with prevailing surface water and subsequently filled with a water sample of the top of the water column. A polyethylene syringe was rinsed thrice with sample water and subsequently filled prior to being fitted with a 0.45 µm PES filter. About 100 mL of sampled water was filtered and collected in a 250 mL polyethylene bottle that was rinsed with the first 5 mL of filtered water. The bottle was stored in a cooling box and transported to the laboratory.</p> <p><strong>In the laboratory</strong>, the 250 mL bottle was stored at 4 °C until analysis. Within 36 hours, concentrations of ammonium, nitrate, nitrite, and orthophosphate were determined <strong>in triplicate</strong>. More specifically, the following test kits were used to determine said nitrogen and phosphorus concentrations (with quantification range between brackets):</p> <ul> <li>Ammonium: 1.14752.0001 (0.05-3.00 mgN/L)</li> <li>Nitrate: 1.14773.0001 (2-20 mgN/L)</li> <li>Nitrite: 1.14776.0001 (0.02-1.00 mgN/L)</li> <li>Orthophosphate: 1.14848.0001 (0.05-5.00 mgP/L)</li> </ul> <p>Regarding the <strong>spectrophotometric determination</strong>, all analyses were complemented with a blank and a standard with a known concentration of each individual nutrient component. For each nutrient, a specific wavelength was used and the resulting absorbance was converted to the associated nutrient concentration through known factors (similar to the use of calibration curves), after setting the absorbance of the blank as reference absorbance (i.e. a concentration of 0 mg/L). All of the analyses were performed with plastic 1-cm cuvettes during the first campaign, while 5-cm cuvettes were used in the remaining three campaigns due to low nutrient levels (except for nitrate, for which an analysis through 5-cm cuvettes is not supported by the used test kits).</p>
Yangtze River Basin Water Reservoir (YWR) dataset
<p>The Yangtze River Basin Water Reservoir (YWR) dataset, developed by multi-source satellite remote sensing data, provides monthly time series data for 443 reservoirs (with a total storage capacity of 276.51 km³) in the Yangtze River Basin (YRB) from 1990 to 2023, including area and storage data for all 443 reservoirs and water level data for 175 reservoirs. This dataset is associated with the study: Wang et al., "Advanced monitoring of reservoirs in the Yangtze River Basin from 1990 to 2023 using multi-source satellite remote sensing", Journal of Remote Sensing, under review, 2025.</p>
Indicative distribution map for Ecosystem Functional Group F1.2 Permanent lowland rivers
<p>This archive contains indicative distribution maps and profiles for <strong>F1.2 Permanent lowland rivers</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.