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3,788 results for “indices”
ERA5-Land selected indicators daily aggregates for Africa, 1961
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1961.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1958
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1958.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1953
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1953.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1954
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1954.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1957
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1957.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1955
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1955.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Africa, 1950
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 1950.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Europe, 2022
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Europe for 2022.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
ERA5-Land selected indicators daily aggregates for Europe, 2023
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Europe for 2023.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p>
Zonal Statistics of Weather Indicators for Brazilian Municipalities from the BR-DWGD Project
<p>This dataset presents daily weather indicators for Brazilian municipalities computed with zonal statistics using the data from the <a href="https://sites.google.com/site/alexandrecandidoxavierufes/brazilian-daily-weather-gridded-data" target="_blank" rel="noopener">BR-DWGD project</a> (version 3.2.3), from 1961-01-01 to 2024-03-20.</p> <p> </p> <table> <tbody> <tr> <td>File</td> <td>Indicator</td> <td>Unit</td> </tr> <tr> <td>pr_3.2.3.parquet</td> <td>Precipitation</td> <td>mm</td> </tr> <tr> <td>ETo_3.2.3.parquet</td> <td>Evapotranspiration</td> <td>mm</td> </tr> <tr> <td>Tmax_3.2.3.parquet</td> <td>Maximum temperature</td> <td>°C</td> </tr> <tr> <td>Tmin_3.2.3.parquet</td> <td>Minimum temperature</td> <td>°C</td> </tr> <tr> <td>Rs_3.2.3.parquet</td> <td>Solar radiation</td> <td>MJm-2</td> </tr> <tr> <td>u2_3.2.3.parquet</td> <td>Wind speed at 2 m height</td> <td>m/s</td> </tr> <tr> <td>RH_3.2.3.parquet</td> <td>Relative humidity</td> <td>%</td> </tr> </tbody> </table> <p>The methodology to compute the zonal statistics follows <a href="https://doi.org/10.1017/eds.2024.3" target="_blank" rel="noopener">https://doi.org/10.1017/eds.2024.3</a> .</p>
INDICATORS TO EVALUATE THE LABOUR INSERTION OF PEOPLE WITH DISABILITIES IN CONVENTIONAL COMPANIES IN SPAIN: QUANTITIATIVE DATA SET OF DELPHI STUDY (PHASE 2 AND PHASE 3)
<p><span>The level of labor integration of people with disability (PwD) is notably lower than that of people without disabilities. In order to evaluate the success of the labor market integration of people with disabilities, it is necessary to establish a series of indicators that go beyond hiring rates. Hence, the objective of this study is to develop a list of indicators with their specified individual weight that will serve to evaluate the success of the labor market insertion of PwD in conventional companies. </span></p> <p><span>Methodology: </span></p> <p><span>The Delphi method was used. </span></p> <p><span>PHASE 1</span></p> <p><span>In Phase 1, an open-ended questionnaire was distributed to 48 human resources and disability experts. <span><br></span></span></p> <p><span>PHASE 2</span></p> <p><span>Based on the theoretical dimensions obtained, a list of 52 indicators was drawn up and the experts were asked to evaluate the importance of each item using a scale of 0 to 10 points. In addition, in this second questionnaire, they were encouraged to propose improvements in the final wording of the items, as well as in the relevance and denomination of the dimensions into which they had been grouped. No suggestions were received to modify the wording or to incorporate additional items.</span></p> <p><span>PHASE 3</span></p> <p><span>Once the scores of all the participants had been collected, a third questionnaire was sent out with the aim of achieving a statistical consensus within the group of experts. In this questionnaire, each panel member was informed of their degree of agreement or disagreement in relation to the group as a whole, without revealing the identity of the other participants. In other words, each participant was provided with information on the average rating of the group and their own initial rating (from Phase 2) of each of the 52 indicators, offering them the option to modify their response if they considered it appropriate. If they chose to change their assessment, they were asked to justify their reasons.</span></p> <p><span><span>To assess the possible convergence of opinion, the change in the responses received in the third phase with respect to the second phase was analyzed. We examined whether there had been variations in the scores given by the experts in the second phase once the group's mean ratings had been received. For this purpose, the “proportion of experts” statistic was used to verify that the average value of the responses in this third phase was within a range from [-0.5 to +0.5], compared to the average value of the scores in the second phase. In the case of non-convergence of opinion, this methodology allows for as many rounds as necessary until convergence is achieved. </span></span></p> <p><span><span>THIS DATA SET COLLECT THE ANSWERS OF THE EXPERTS OF PHASE 2 AND PHASE 3.</span></span></p>
HOLSEA-NL: Holocene water level and sea-level indicator dataset for the Netherlands
<p>This dataset contains an assembly of geological water-level indicators, relevant for studying relative sea level rise (RSLR), regional subsidence quantification and causal breakdown, coastal prism accommodation and Holocene aggradation chronology of the Holocene Netherlands. It gives a sources-referenced, uniform overview of 658 basal geological water-level indicators collected from original research of various type and application (140 primary references). From the indicators, 59% was collected in 1950-2000, mainly in academic studies and survey mapping campaigns; 37% was collected in 2000-2020 in academic studies and archaeological surveying projects, 4% was newly collected (this study), the latter mainly in previously under sampled central and northern Netherlands regions. 117 are true sea-level indicators (so-called SLIPs), the majority of datapoints (536) are inland water level indicators that are upper limiting to sea-level. The total number of entries is 712, because we included some literature mentioned rejected samples and deep positioned intercalated water level indicators.</p> <p>The dataset is compiled in the so-called HOLSEA workbook format. It covers measured, calculated and classification fields defining the geological observational data and its uncertainties, allowing to document and assess indicative meaning adapted to specific use variants. Hereto, the workbook contains expansions to the original format. See Related Works (ESSD paper: De Wit et al. 2024).</p>
Daily time series of 12 human thermal stress indices in Greece aggregated at commune level (1998-2022)
<p>The overview table of the dataset containing 12<strong> </strong>Human Thermal Stress Indices in Greece (<strong>HTSI-GR</strong>):</p> <div> <table> <tbody> <tr> <th> <p>Heat indices names</p> </th> <th> <p>Description</p> </th> <th> <p>Units</p> </th> <th> <p>Reference</p> </th> <th> <p>Dataset file names</p> </th> </tr> <tr> <td> <p><strong>AT</strong></p> </td> <td> <p>Apparent Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Steadman, R. G. Norms of apparent temperature in Australia. Aust. Met. Mag. 43, 1–16 (1994).</p> </td> <td> <p>AT_min_1998-01-01_2022-12-31.csv, AT_mean_1998-01-01_2022-12-31.csv, AT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>HI</strong></p> </td> <td> <p>Heat Index</p> </td> <td> <p>°C</p> </td> <td> <p>Rothfusz, L.P. Te heat index equation. National Weather Service Technical Attachment. Report No. SR 90–23 (1990).</p> </td> <td> <p>HI_min_1998-01-01_2022-12-31.csv, HI_mean_1998-01-01_2022-12-31.csv, HI_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>Humidex</strong></p> </td> <td> <p>Humidity Index</p> </td> <td> <p>°C</p> </td> <td> <p>Masterson, J. & Richardson, F.A. Humidex: a method of quantifying human discomfort due to excessive heat and humidity (Environment Canada, 1979).</p> </td> <td> <p>Humidex_min_1998-01-01_2022-12-31.csv, Humidex_mean_1998-01-01_2022-12-31.csv, Humidex_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>NET</strong></p> </td> <td> <p>Normal Effective Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Landsberg HE. The assessment of human bioclimate: a limited review of physical parameters. Technical Note No. 123, WMO-No. 331 (World Meteorological Organization, 1972).</p> </td> <td> <p>NET_min_1998-01-01_2022-12-31.csv, NET_mean_1998-01-01_2022-12-31.csv, NET_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>WBGT</strong></p> </td> <td> <p>Wet Bulb Globe Temperature (simple)</p> </td> <td> <p>°C</p> </td> <td> <p>Australian Bureau of Meteorology. Thermal comfort observations http://bom.gov.au/info/thermal_stress/ (2020).</p> </td> <td> <p>WBGT_min_1998-01-01_2022-12-31.csv, WBGT_mean_1998-01-01_2022-12-31.csv, WBGT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>thermofeelWBGT</strong></p> </td> <td> <p>Wet Bulb Globe Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Stull, R. Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteorol. Climatol. 50, 2267–2269 (2011).</p> </td> <td> <p>thermofeelWBGT_min_1998-01-01_2022-12-31.csv, thermofeelWBGT_mean_1998-01-01_2022-12-31.csv, thermofeelWBGT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>WBT</strong></p> </td> <td> <p>Wet Bulb Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Stull, R. Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteorol. Climatol. 50, 2267–2269 (2011).</p> </td> <td> <p>WBT_min_1998-01-01_2022-12-31.csv, WBT_mean_1998-01-01_2022-12-31.csv, WBT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>WCT</strong></p> </td> <td> <p>Wind Chill Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Office of the Federal Coordinator for Meteorological services and supporting research (OFCM). Report on Wind Chill Temperature and extreme heat indices: evaluation and improvement projects. Report No. FCM-R19-2003 (U.S. Office of the Federal Coordinator for Meteorological Services and Supporting Research, 2003).</p> </td> <td> <p>WCT_min_1998-01-01_2022-12-31.csv, WCT_mean_1998-01-01_2022-12-31.csv, WCT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>MRT</strong></p> </td> <td> <p>Mean Radiant Temperature</p> </td> <td> <p>°C</p> </td> <td> <p>Weihs, P. et al. The uncertainty of UTCI due to uncertainties in the determination of radiation fluxes derived from measured and observed meteorological data. Int. J. Biometeorol. 56, 537–555 (2012).</p> </td> <td> <p>MRT_min_1998-01-01_2022-12-31.csv, MRT_mean_1998-01-01_2022-12-31.csv, MRT_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>UTCI</strong></p> </td> <td> <p>Universal Thermal Climate Index (UTCI)</p> </td> <td> <p>°C</p> </td> <td> <p>Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494<br>(2012).</p> </td> <td> <p>UTCI_min_1998-01-01_2022-12-31.csv, UTCI_mean_1998-01-01_2022-12-31.csv, UTCI_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>UTCI2</strong></p> </td> <td> <p>Indoor environment UTCI with 2 parameters (air temperature and humidity)</p> </td> <td> <p>°C</p> </td> <td> <p>Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494<br>(2012).</p> </td> <td> <p>UTCI2_min_1998-01-01_2022-12-31.csv, UTCI2_mean_1998-01-01_2022-12-31.csv, UTCI2_max_1998-01-01_2022-12-31.csv</p> </td> </tr> <tr> <td> <p><strong>UTCI3</strong></p> </td> <td> <p>Outdoor shaded space environment UTCI with 3 parameters (air temperature, humidity, and wind speed)</p> </td> <td> <p>°C</p> </td> <td> <p>Bröde, P. et al. Deriving the operational procedure for the Universal Thermal Climate Index (UTCI). Int. J. Biometeorol. 56, 481–494<br>(2012).</p> </td> <td> <p>UTCI3_min_1998-01-01_2022-12-31.csv, UTCI3_mean_1998-01-01_2022-12-31.csv, UTCI3_max_1998-01-01_2022-12-31.csv</p> </td> </tr> </tbody> </table> </div> <p> </p> <p>The overview table of the <strong>HTSI-GR</strong> additional resources folder containing support files and instructions for dataset replication:</p> <table> <tbody> <tr> <th> <p><strong>File names</strong></p> </th> <th> <p>Description</p> </th> </tr> <tr> <td> <p><strong>0. Calculate thermofeelWBGT.py</strong></p> </td> <td> <p>A python script that calculates the Wet Bulb Globe Temperature (WBGT) using the Thermofeel library. Processes NetCDF files containing daily meteorological data and outputs WBGT values in new NetCDF files for each day.</p> </td> </tr> <tr> <td> <p><strong>1. Merge_HI_by_max-mean-min.py</strong></p> </td> <td> <p>A python script that merges daily NetCDF files containing heat index (HI) data into three separate files based on mean, maximum and minimum values for further processing.</p> </td> </tr> <tr> <td> <p><strong>2. QGIS_zonal_statistics.py</strong></p> </td> <td> <p>A python script that calculates zonal statistics for heat indices using QGIS python console. Uses a shapefile of Greek communes and a raster NetCDF file containing daily index values, and outputs daily CSV files with computed statistics.</p> </td> </tr> <tr> <td> <p><strong>3. Zonal_format.py</strong></p> </td> <td> <p>A python script that formats the zonal statistics results into a comprehensive dataset. Combines daily CSV files into a single CSV, fills in missing data using nearest neighbour values, and produces a final formatted dataset.</p> </td> </tr> <tr> <td> <p><strong>Greek Communes.ZIP</strong></p> </td> <td> <p>Contains the shapefile of Greek communes derived from the Hellenic Statistical Authority (ELSTAT) required for zonal statistics calculations. KALCODE and Commune names are linked in the .shp.</p> </td> </tr> <tr> <td> <p><strong>Nearest Neighbour data table.csv</strong></p> </td> <td> <p>A support table to script <strong>3.Zonal</strong><strong>_format.py</strong> that lists communes with missing data and their nearest neighbour with data.</p> </td> </tr> <tr> <td> <p><strong>Read me.txt</strong></p> </td> <td> <p>Provides an overview and instructions for using the scripts. Describes the purpose of each script, lists prerequisites, and provides step-by-step instructions for replicating the dataset.</p> </td> </tr> </tbody> </table> <p> </p> <p> </p>
Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines
<p>Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines. The provided land cover maps follow the high carbon stock approach (HCSA) stratifying vegetation based on the estimated carbon density (aboveground biomass). A deep convolutional neural network was trained to estimate canopy top height from Sentinel-2 optical satellite images using reference data derived from GEDI lidar waveforms. Carbon density and high carbon stock classes were derived from these dense canopy height maps using calibration data from an airborne lidar campaign in Sabah, Borneo. The resulting maps have a ground sampling distance (GSD) of 10 m and are based on images between 1st of September 2020 and 1st of March 2021.</p> <p>The style files (color_style_HCS.qml, color_style_canopy_top_height.qml) contain the color coding and can be loaded for visualization (e.g. in QGIS).</p> <p>The indicative HCS maps contain 9 land cover categories noted as "Label: name [colorcode]":</p> <p> 0: Open land (OL) [#440154]<br> 1: Scrub (S) [#404387]<br> 2: Young regenerating forest (YRF) [#29788e]<br> 3: Low density forest (LDF) [#22a884]<br> 4: Medium density forest (MDF) [#7ad251]<br> 5: High density forest (HDF) [#fde725]<br> 10: Oil palm [#fcffa4]<br> 11: Coconut [#a4feff]<br> 50: Urban [#fa0000]<br> 255: No data</p> <p><strong>Citation: </strong>Use of these data require citation of this dataset and the original research articles. These citations are as follows:</p> <p>Lang, N., Schindler, K., & Wegner, J. D. (2021). High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR. arXiv preprint arXiv:2107.07431.</p> <p>Rodríguez, A. C., D'Aronco, S., Schindler, K., & Wegner, J. D. (2021). Mapping oil palm density at country scale: An active learning approach. <em>Remote Sensing of Environment</em>, <em>261</em>, 112479.</p> <p>Lang, N., Rodríguez, A. C., Schindler, K., & Wegner, J. D. (2021). Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.5012448</p> <p> </p>
Aggregated data of abundance indices
<p>For the reconstruction of flight peaks, an abundance index was calculated by relating records to search efforts based on the evidence of field activities left in the database by proficient observers. For this search-effort correction, we selected observers with at least 50 records of at least 10 butterfly species each year. As a measure of their collective search effort, we calculated the sum for each day of all the 100 × 100 m grid cells from which an observer had reported records (of any species, including non-butterflies; also including absence records). This method of ‘proven day-grid-visits’ has become the standard proxy for search effort when analyzing incidental observations of the portal waarnemingen.be. Day-grid-visits do not cover search effort completely, because records are not submitted from every visited hectare grid cell, but strongly correlates with it.</p> <p>We provide the raw data containing day (2009-2020), the X and Y coordinate of the centroid of the 100x100 m grid cell (In Lambert 72, EPSG:<em>31370</em> Projected coordinate system for Belgium), the number of peacock butterflies reported, and the number of hectare day grid visits. </p>
Riverine Flood Insurance assessment indicators under climate and socio-economic change
<p>Expected annual river flood damages, flood insurance premiums, and insurance penetration rates, for EU-regions (NUTS2) and under future climatic and socio-economic conditions (RCP-SSP combinations).</p>
MedAID_Key performance indicators for gilthead seabream reared under low temperature
<p>This data set contains the results from a nutritional trial performed at the Centre of Marine Sciences of Algarve (CCMAR, Faro, Portugal) within the framework of WP2 of the MedAID project (Mediterranean Aquaculture Integrated Development, Horizon 2020, GA number 727315). This experiment impacts aimed to evaluate growth performance, feed utilisation, nutrient digestibility and nitrogen losses to the environment in gilthead seabream juveniles (<em>Sparus aurata</em>, initial weight: ± 154.5 g) reared under low temperature (~13°C). Three diets were tested: a commercial-like diet (COM), containing 44% crude protein and 27.5% fishmeal, and two experimental diets with a lower protein content of 42% (ECO and ECOSup). In both ECO diets fishmeal inclusion was reduced (10% in ECO and 7.5% in ECOSup diet) and 15% poultry meal was included. Additionally, the ECOSup diet was supplemented with a mix of feed additives intended to promote fish growth performance and feed intake. For further information on experimental conditions, please refer to the publication: “Teodósio R., Aragão C., Colen R., Carrilho R., Dias J. and Engrola S., 2021. A nutritional strategy to promote gilthead seabream performance under low temperatures. Aquaculture 537: 736494. doi:10.1016/j.aquaculture.2021.736494”. In addition to properly citing this dataset, it would be appreciated that when using this dataset in a publication the original publication is cited.</p>
HISAR - Hydrologic Indices of South American Rivers
<p>This is a preview of the HISAR dataset (Hydrologic Indices of South American Rivers). </p> <p>The HISAR dataset is freely available for non-commercial use. The files provided are (i) drainage line shapefile with river reaches as represented by the MGB model and 73 attributes corresponding to hydrologic indices derived from simulated time series; (ii) gauge points shapefile with 73 attributes corresponding to hydrologic indices derived from observed time series; (iii) maps with hydrologic indices, (iv) maps with information of the error of some indices and (v) the scripts used to calculate the indices. This database provides a spatial view of the variability of the river flow regime characteristics.</p> <p>The line shapefile has 33,749 river reaches with an average length of 15 km and drainage area > 1000 km². The ESRI shapefile also has the attributes of drainage area (<em>Upst_Area_</em> in km²), length (<em>Ltr_Km_ in</em> km), <em>UC</em> (corresponding catchment attribute from hydrological modelling), <em>HYear_min</em> (starting month of the hydrological year of minimum flow) and <em>HYear_max</em> (starting month of the hydrological year of maximum flow). A value of -9999999 is used as a symbol of ‘no data’.</p> <p>Some river reaches do not have all hydrologic indices calculated, due to series of streamflows that could not meet specific criteria. For instance, the baseflow recession constant was automatically calculated using at least five consecutive days of decreasing streamflow, all of which below the Q90 (streamflow value that is exceeded 90% of the time), and this condition was not found in all cases.</p> <p>The gauge points shapefile has 1329 points with 73 attributes corresponding to hydrologic indices derived from observed time series. The drainage area of the gauges ranging from 1,000 to 4,703,503 km<sup>2</sup>. The ESRI shapefile also has the attributes of code, name, latitude (lat), longitude (long), drainage area (<em>Upst_Area_</em> in km²), Country were the gauge point are located, <em>HYear_min</em> (starting month of the hydrological year of minimum flow) and <em>HYear_max</em> (starting month of the hydrological year of maximum flow). A value of -9999999 is used as a symbol of ‘no data’.</p> <p>For more information about HISAR dataset see the journal article DOI: in preparation.</p>
Morphotectonic indices and earthquake dataset of Batui Thrust Segment, Banggai, Indonesia
<p>To measure the tectonic activity of a region, morphotectonic, a quantitave measurement of landscapes are carried out. Combination between geological, geomorphology, and earthquake data approach are powerful source to determine process that make up mountainous landscape. The combined data are applied to Pagimana and adjacent area, Banggai, Indonesia by dividing into eight watersheds and aim the Batui Thurst as main objective. The data comprised of 8.5m vertical resolution Digital Elevation Model (DEM) and earthquakes catalog were downloaded from the open-source database and extracted using composited GIS software. We use seven morphometry indices consist of Mountain Front Sinousity (Smf), Drainage Basin: Asymmetry Factor (AF) and Transverse Topographic Symmetry (T), Hypsometric Integral (HI), Channel Sinuosity (S), Ratio of Valley Floor Width to Valley Height (Vf), Stream Length-Gradient Index (SL), and Basin Elongation Ratio (Re) and embed them on eight watersheds which taken by remote sensing. Weighing indices and watersheds matrix also calculated to define correlation between each watershed on each index. Fieldwork also carried out to ensure the presence of Batui Thrust and other related structural geology which have been developed on the Pagimana. This dataset is potentially can be used for geologist, geophysicist and geomorpher to get new insight on the forming of East Arm of Sulawesi landscape and the Banggai-Sula Microcontinent development. Beside, the urban planner and risk assessor can be easily analyze the dataset to avoid and minimize effect of the potential earthquake hazard in the future.</p>
Data to support Whitney JL, Coleman RR, Deakos MH "Genomic evidence indicates small island-resident populations and sex-biased behaviors of Hawaiian Reef Manta Rays"
<p>Datasets supporting the manuscript: Whitney JL, Coleman RR, Deakos MH "Genomic evidence indicates small island-resident populations and sex-biased behaviors of Hawaiian Reef Manta Rays". <em>BMC Ecology and Evolution </em><strong>23</strong>, 31 (2023). https://doi.org/10.1186/s12862-023-02130-0</p> <p>Nuclear data:</p> <p>"Mobula-alfredi_nuclear_reference_RAD_contigs.fasta" is a fasta of 359,751 contigs that serve as the reference for nuclear alignment of genotypes to RAD loci. Contigs begin and end with GATC cut site.</p> <p>Mobula-alfredi_nuclear_all_2048snps_38genotypes.vcf is a VCF file with all 2048 nuclear SNPs in final filtered SNP dataset. 38 genotypes are included from Maui Nui and Hawaii Island. This 2048 SNPs includes both 2038 neutral and 10 outlier SNPs. </p> <p>Mobula-alfredi_nuclear_neutral_2038snps_38genotypes.vcf is a VCF file with 2038 neutral nuclear SNPs genotyped in 38 individuals from Maui Nui and Hawaii Island. </p> <p>Mobula-alfredi_nuclear_outliers_10snps_38genotypes.vcf is a VCF file with 10 outlier SNPs genotyped in 38 individuals from Maui Nui and Hawaii Island. </p> <p>Structure (.str) files are also provided in addition to VCFs. In all files Population prefixes M=Maui Nui and K=Hawaii Island. </p> <p>Mitochondrial data:</p> <p>Mobula-alfredi_mitogenome_34haplotypes_9sites_min4x.vcf is a VCF file with 9 variant sites across the mitogenome haplotyped in 34 individuals from Maui Nui and Hawaii Island. </p> <p>Mobula-alfredi_mitogenome_34haplotypes_allsites_min4x.fasta is a FASTA file with whole mitogenomes aligned to OP562409 [https://www.ncbi.nlm.nih.gov/nuccore/OP562409]. Sites with less than 4x coverage were masked with Ns. </p> <p>Mobula-alfredi_mitogenome_reference_OP562409.fasta is a FASTA file containing the <em>Mobula alfredi</em> reference mitogenome OP562409 [https://www.ncbi.nlm.nih.gov/nuccore/OP562409].</p> <p> </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.