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586 results for “March”
Level A Pan Europe Solar Index for estimation of Potential evaporation March
Solar Index for estimation of Potential evaporation March. Solar Index (SI) maps are input needed for spatial estimation of potential evaporation by using modified Blaney Criddle method (Schrödter 1985, Parajka et al., 2003). SI maps are available for each month. SI maps are available for each month. Spatial resolution: 1km2. Solar Index maps (SI_xxx) for estimation of potential evaporation by using modified Blaney Criddle method. Maps are available for each month (xxx). Format ArcGIS ASCII grid. Maps are estimated from GTOPO30 DEM. Coordinates: geographical. SI index is estimated in GIS GRASS (r.sun module)
Smoke designations for eastern Kansas monitoring sites during March-May 2022
<p class="Abstract">Prescribed fires (fires intentionally set for mitigation purposes) produce pollutants, which have negative effects on human and animal health. One of the pollutants produced from fires is fine particulate matter (PM<sub>2.5</sub>). PM<sub>2.5</sub> can penetrate deep into the lungs and harm cardiovascular and respiratory systems. The Flint Hills region of Kansas experiences extensive prescribed burning each spring (March - May). Smoke from prescribed fires is often understudied due to a lack of monitoring in the rural regions where prescribed burning occurs, as well as the short duration and small size of the fires. Our goal was to attribute PM<sub>2.5</sub> concentrations to the prescribed burning in the Flint Hills. To determine PM<sub>2.5</sub> increases from local burning, we used low-cost PM<sub>2.5</sub> sensors (PurpleAir) and satellite observations. The Flint Hills were also affected by smoke transported from fires in other regions during 2022. We separated the transported smoke from smoke from fires in eastern Kansas. Based on data from the PurpleAir sensors, we found the 24-hour median PM<sub>2.5</sub> increased by 5.2 µg m<sup>-3</sup> on days impacted by smoke from fires in the eastern Kansas region compared to days unimpacted by smoke. We found the Flint Hills to be the most smoke PM<sub>2.5</sub> impacted region compared to the surrounding area across satellite products and in-situ measurements. </p>
HIRHAM–NAOSIM simulations for the periods March/April 2018 and March to September 2020
<p>This dataset group comprises subsets of two HIRHAM–NAOSIM simulations for the periods March/April 2018 and March to September 2020, as used in the paper “Observations and modeling of areal surface albedo and surface types in the Arctic” by Jäkel et al. (2024, https://doi.org/10.5194/tc-18-1185-2024).</p> <p>The simulation for 2018 was originally carried out for the period from January to September 2018. It was started from rest with temperature, salinity, ice thickness, and ice concentration fields from ORAS5. The simulation for 2020 was originally carried out for the period 2019-2020 and initialized with fields from 1 January 2019, 00 UTC of an earlier long-term simulation. The prognostic atmospheric fields of the two simulations were nudged to the corresponding ERA5 fields with a vertically uniform nudging time scale of 16.67 h. Sea ice and ocean fields were initialized with fields from an earlier long-term simulation. Further details on the simulations are given in "On the importance to consider the cloud dependence in parameterizing the albedo of snow on sea ice" by Foth et al. (2023, https://doi.org/10.5194/egusphere-2023-634).</p>
Geomagnetic field data at Jicamarca and Piura on 23 March 2023
<p>The H-component of the geomagnetic field at Jicamarca (JICA, equator) and Piura (PIUR, off-equator) stations on 23 March 2023. </p>
Data complementing the Avian influenza overview December 2023 - March 2024
<p>Data complementing the Avian influenza overview December 2023 - March 2024</p> <p> </p> <p><strong>Annex A – Data on wild bird species and HPAI virus detections in birds</strong></p> <p>The annex contains tables and figures on wild bird species and HPAI virus detections in birds.</p> <p> </p> <p><strong>Annex B – Characteristics of the HPAI A(H5Nx)-affected poultry establishments</strong></p> <p>The annex contains a table with the characteristics of the HPAI A(H5Nx)-affected poultry establishments by affected European country submitted to ADIS between 2 December 2023 and 15 March 2024.</p> <p> </p> <p><strong>Annex C – Data on virus sequences</strong></p> <p>The annex contains information on authors, originating and submitting laboratories of the sequences from GISAID's EpiFlu™ Database on which this research is based. All data submitters may be contacted directly via <a href="https://eur03.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.gisaid.org%2F&data=05%7C02%7C%7Cbccd5bb40f5e4a4ccee508dbfbeb3823%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C638380763212264062%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=Yxkh9bN0bH3m4Pak8DJa4Mkdj1PTc6%2FYqIoei05qg1M%3D&reserved=0">www.gisaid.org</a>.</p>
Dimensions of Education March, 2024
Open the record for dataset details and reuse information.
LPL7 - March 2024
<p>Measurements of LPL7 (Archipielago Chinijo) in March 2024</p> <p>Archipielago Chinijo</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL7">info</a></p>
LPL2 - March 2024
<p>Measurements of LPL2 (Caldera de Taburiente NP) in March 2024</p> <p>La Palma</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL2">info</a></p>
LPL11 - March 2024
<p>Measurements of LPL11 (Frontera Rural Park) in March 2024</p> <p>El Hierro</p><p> Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL11">info</a></p>
Fig. 2 in Tapeworms detected in wolf populations in Central Italy (Umbria and Marche regions): A long-term study
Fig. 2. Neighbor-Joining phylogenetic tree of the sequenced Taenia species and E. granulosus s.s. G3. Maximum Composite Likelihood method, 1000 bootstraps (MEGA11 Software).
Fig. 1 in Tapeworms detected in wolf populations in Central Italy (Umbria and Marche regions): A long-term study
Fig. 1. Details of sampling and results: number of wolves by region (A); number of positive wolves (B); percentages of positive and negative samples (C); geolocation of parasites (D).
Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь. Fig. 5. Dependence of start of spawning of the Japanese scallop and Pacific (giant) oyster in Peter the Great Bay on the sum of sea surface temperatures (March–June): 1 – beginning of spawning of the Japanese scallop; 2 – beginning of spawning of the Pacific oyster; 3 – sum of sea surface temperatures for the period from March to June. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 5. Зависимость начала нереста приморского гребешка и тихоокеанской устрицы в Зал. Петра Великого от суммы поверхностных температур (март–июнь): 1 – начало нереста приморского гребешка; 2 – начало нереста тихоокеанской устрицы; 3 – сумма поверхностных температур За период с марта по июнь. Fig. 5. Dependence of start of spawning of the Japanese scallop and Pacific (giant) oyster in Peter the Great Bay on the sum of sea surface temperatures (March–June): 1 – beginning of spawning of the Japanese scallop; 2 – beginning of spawning of the Pacific oyster; 3 – sum of sea surface temperatures for the period from March to June.
Рис. 2. График вЗаимосвяЗи меЖду суммой средних месячных температур воды марта и апреля и датами начала нереста. Fig. 2. Graph of relationship between the sum of the average monthly water temperatures of March and April and the start dates of spawning. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 2. График вЗаимосвяЗи меЖду суммой средних месячных температур воды марта и апреля и датами начала нереста. Fig. 2. Graph of relationship between the sum of the average monthly water temperatures of March and April and the start dates of spawning.
Solar and meteorological data collected from the Radio Telescope Bras D'Eau station (Mauritius) by the ENERGY-Lab at the University of La Reunion between November 2015 and March 2023
<p>Scientific data provided by ENERGY-Lab located at the University of La Reunion. These data come from solar and meteorological stations present in the following territories: La Reunion, Comoros, Madagascar, Mauritius, Seychelles and South Africa. A THREDDS Data Server was created as part of the IOS-net (Indian Ocean Solar Network, https://galilee.univ-reunion.fr) project which aims to study the solar field and the optimisation of intelligent solar energy systems in the countries of the IOC (Indian Ocean Commission). These data are served by Unidata's Thematic Realtime Environmental Distributed Data Services (THREDDS) Data Server (TDS) in a variety of interoperable data services and output formats.</p> <p>Dataset is available from the THREDDS Data Server to this url: <a href="https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html"> https://galilee.univ-reunion.fr/thredds/catalog/dataStations/catalog.html</a>.</p> <p>Data are also viewable and exploitable on the mobile application of the IOS-net project. The SolarIO app is downloadable in all stores.</p> <p><strong><em>ENERGY-Lab data may be reused, provided that related metadata explaining the data has been reviewed by the user, and that the data are appropriately acknowledged.</em></strong></p>
Monthly averaged lightning data extracted from 1-year EMAC simulation including LCC-lightning (between 1 March, 2017 and 28 February, 2018, T42L90MA resolution).
<p>About Dataset<br> Monthly averaged lightning data extracted from 1-year EMAC simulation (between 1 March, 2017 and 28 February, 2018, T42L90MA resolution).</p> <p>Authors: Francisco J. Perez-Invernon, Heidi Huntrieser, Patrick Joeckel and Francisco J. Gordillo-Vazquez</p> <p>Description of the data<br> P_cth.tar: Lightning parameterization based on cloud top height. Scaling factor is applied.<br> L_cth.tar: Lightning parameterization based on cloud top height and modified over the oceans. A scaling factor of 0.571 have to be applied.<br> G_updr: Lightning parameterization based on updraft velocity. Scaling factor is applied.<br> A_prec: Lightning parameterization based on convective precipitation. A scaling factor of 0.76 have to be applied.<br> A_updr: Lightning parameterization based on Updraft strength at 440~hPa. A scaling factor of 0.01618 have to be applied.<br> P_cth + A_prec: Lightning parameterization based on cloud top height and updraft velocity. A scaling factor of 1.13 have to be applied.</p> <p>File format: netcdf</p> <p> Example:<br> <br> netcdf LCC_2017_______20170201_0000_mmlb_PaR_T {<br> dimensions:<br> time = UNLIMITED ; // (1 currently)<br> lon = 128 ;<br> lat = 64 ;<br> tbnds = 2 ;<br> variables:<br> double time(time) ;<br> time:long_name = "time" ;<br> time:bounds = "time_bnds" ;<br> time:units = "day since 2017-01-01 00:00:00" ;<br> time:calendar = "gregorian" ;<br> double YYYYMMDD(time) ;<br> YYYYMMDD:long_name = "time" ;<br> YYYYMMDD:units = "days as %Y%m%d.%f" ;<br> YYYYMMDD:calendar = "gregorian" ;<br> double dt(time) ;<br> dt:long_name = "delta_time" ;<br> dt:units = "s" ;<br> double nstep(time) ;<br> nstep:long_name = "current time step" ;<br> float lon(lon) ;<br> lon:long_name = "longitude" ;<br> lon:units = "degrees_east" ;<br> float lat(lat) ;<br> lat:long_name = "latitude" ;<br> lat:units = "degrees_north" ;<br> float aps(time, lat, lon) ;<br> aps:long_name = "surface pressure" ;<br> aps:units = "Pa" ;<br> aps:representation = "GP_2D_HORIZONTAL" ;<br> aps:grid_type = "gaussian" ;<br> aps:table = 128 ;<br> aps:code = 134 ;<br> aps:REFERENCE_TO = "g3b: aps" ;<br> aps:coordinates = "lon lat" ;<br> aps:cell_methods = "time: point" ;<br> float aps_ave(time, lat, lon) ;<br> aps_ave:long_name = "surface pressure" ;<br> aps_ave:units = "Pa" ;<br> aps_ave:representation = "GP_2D_HORIZONTAL" ;<br> aps_ave:grid_type = "gaussian" ;<br> aps_ave:table = 128 ;<br> aps_ave:code = 134 ;<br> aps_ave:REFERENCE_TO = "g3b: aps" ;<br> aps_ave:coordinates = "lon lat" ;<br> aps_ave:cell_methods = "time: mean" ;<br> float fpscg(time, lat, lon) ;<br> fpscg:long_name = "CG flash frequency" ;<br> fpscg:units = "1/s" ;<br> fpscg:REFERENCE_TO = "lnox_PaR_T_gp: fpscg" ;<br> fpscg:coordinates = "lon lat" ;<br> fpscg:cell_methods = "time: point" ;<br> float fpscg_ave(time, lat, lon) ;<br> fpscg_ave:long_name = "CG flash frequency" ;<br> fpscg_ave:units = "1/s" ;<br> fpscg_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpscg" ;<br> fpscg_ave:coordinates = "lon lat" ;<br> fpscg_ave:cell_methods = "time: mean" ;<br> float fpsic(time, lat, lon) ;<br> fpsic:long_name = "IC flash frequency" ;<br> fpsic:units = "1/s" ;<br> fpsic:REFERENCE_TO = "lnox_PaR_T_gp: fpsic" ;<br> fpsic:coordinates = "lon lat" ;<br> fpsic:cell_methods = "time: point" ;<br> float fpsic_ave(time, lat, lon) ;<br> fpsic_ave:long_name = "IC flash frequency" ;<br> fpsic_ave:units = "1/s" ;<br> fpsic_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsic" ;<br> fpsic_ave:coordinates = "lon lat" ;<br> fpsic_ave:cell_methods = "time: mean" ;<br> float fpsm2cg(time, lat, lon) ;<br> fpsm2cg:long_name = "CG flash density" ;<br> fpsm2cg:units = "1/s/m2" ;<br> fpsm2cg:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2cg" ;<br> fpsm2cg:coordinates = "lon lat" ;<br> fpsm2cg:cell_methods = "time: point" ;<br> float fpsm2cg_ave(time, lat, lon) ;<br> fpsm2cg_ave:long_name = "CG flash density" ;<br> fpsm2cg_ave:units = "1/s/m2" ;<br> fpsm2cg_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2cg" ;<br> fpsm2cg_ave:coordinates = "lon lat" ;<br> fpsm2cg_ave:cell_methods = "time: mean" ;<br> float fpsm2ic(time, lat, lon) ;<br> fpsm2ic:long_name = "IC flash density" ;<br> fpsm2ic:units = "1/s/m2" ;<br> fpsm2ic:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2ic" ;<br> fpsm2ic:coordinates = "lon lat" ;<br> fpsm2ic:cell_methods = "time: point" ;<br> float fpsm2ic_ave(time, lat, lon) ;<br> fpsm2ic_ave:long_name = "IC flash density" ;<br> fpsm2ic_ave:units = "1/s/m2" ;<br> fpsm2ic_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2ic" ;<br> fpsm2ic_ave:coordinates = "lon lat" ;<br> fpsm2ic_ave:cell_methods = "time: mean" ;<br> float fpslcc10(time, lat, lon) ;<br> fpslcc10:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10:units = "1/s" ;<br> fpslcc10:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc10" ;<br> fpslcc10:coordinates = "lon lat" ;<br> fpslcc10:cell_methods = "time: point" ;<br> float fpslcc10_ave(time, lat, lon) ;<br> fpslcc10_ave:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10_ave:units = "1/s" ;<br> fpslcc10_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc10" ;<br> fpslcc10_ave:coordinates = "lon lat" ;<br> fpslcc10_ave:cell_methods = "time: mean" ;<br> float fpslcc20(time, lat, lon) ;<br> fpslcc20:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20:units = "1/s" ;<br> fpslcc20:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc20" ;<br> fpslcc20:coordinates = "lon lat" ;<br> fpslcc20:cell_methods = "time: point" ;<br> float fpslcc20_ave(time, lat, lon) ;<br> fpslcc20_ave:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20_ave:units = "1/s" ;<br> fpslcc20_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpslcc20" ;<br> fpslcc20_ave:coordinates = "lon lat" ;<br> fpslcc20_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc10(time, lat, lon) ;<br> fpsm2lcc10:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10:units = "1/s/m2" ;<br> fpsm2lcc10:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc10" ;<br> fpsm2lcc10:coordinates = "lon lat" ;<br> fpsm2lcc10:cell_methods = "time: point" ;<br> float fpsm2lcc10_ave(time, lat, lon) ;<br> fpsm2lcc10_ave:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10_ave:units = "1/s/m2" ;<br> fpsm2lcc10_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc10" ;<br> fpsm2lcc10_ave:coordinates = "lon lat" ;<br> fpsm2lcc10_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc20(time, lat, lon) ;<br> fpsm2lcc20:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20:units = "1/s/m2" ;<br> fpsm2lcc20:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc20" ;<br> fpsm2lcc20:coordinates = "lon lat" ;<br> fpsm2lcc20:cell_methods = "time: point" ;<br> float fpsm2lcc20_ave(time, lat, lon) ;<br> fpsm2lcc20_ave:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20_ave:units = "1/s/m2" ;<br> fpsm2lcc20_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2lcc20" ;<br> fpsm2lcc20_ave:coordinates = "lon lat" ;<br> fpsm2lcc20_ave:cell_methods = "time: mean" ;<br> float fpssprite(time, lat, lon) ;<br> fpssprite:long_name = "Sprites flash frequency" ;<br> fpssprite:units = "1/s" ;<br> fpssprite:REFERENCE_TO = "lnox_PaR_T_gp: fpssprite" ;<br> fpssprite:coordinates = "lon lat" ;<br> fpssprite:cell_methods = "time: point" ;<br> float fpssprite_ave(time, lat, lon) ;<br> fpssprite_ave:long_name = "Sprites flash frequency" ;<br> fpssprite_ave:units = "1/s" ;<br> fpssprite_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpssprite" ;<br> fpssprite_ave:coordinates = "lon lat" ;<br> fpssprite_ave:cell_methods = "time: mean" ;<br> float fpsm2sprite(time, lat, lon) ;<br> fpsm2sprite:long_name = "Sprites flash density" ;<br> fpsm2sprite:units = "1/s/m2" ;<br> fpsm2sprite:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2sprite" ;<br> fpsm2sprite:coordinates = "lon lat" ;<br> fpsm2sprite:cell_methods = "time: point" ;<br> float fpsm2sprite_ave(time, lat, lon) ;<br> fpsm2sprite_ave:long_name = "Sprites flash density" ;<br> fpsm2sprite_ave:units = "1/s/m2" ;<br> fpsm2sprite_ave:REFERENCE_TO = "lnox_PaR_T_gp: fpsm2sprite" ;<br> fpsm2sprite_ave:coordinates = "lon lat" ;<br> fpsm2sprite_ave:cell_methods = "time: mean" ;<br> float bps(time, lat, lon) ;<br> bps:long_name = "BJ flash frequency" ;<br> bps:units = "1/s" ;<br> bps:REFERENCE_TO = "bluejetbPaR_T_gp: bps" ;<br> bps:coordinates = "lon lat" ;<br> bps:cell_methods = "time: point" ;<br> float bps_ave(time, lat, lon) ;<br> bps_ave:long_name = "BJ flash frequency" ;<br> bps_ave:units = "1/s" ;<br> bps_ave:REFERENCE_TO = "bluejetbPaR_T_gp: bps" ;<br> bps_ave:coordinates = "lon lat" ;<br> bps_ave:cell_methods = "time: mean" ;<br> float bpsm2(time, lat, lon) ;<br> bpsm2:long_name = "BJ flash density" ;<br> bpsm2:units = "1/s/m2" ;<br> bpsm2:REFERENCE_TO = "bluejetbPaR_T_gp: bpsm2" ;<br> bpsm2:coordinates = "lon lat" ;<br> bpsm2:cell_methods = "time: point" ;<br> float bpsm2_ave(time, lat, lon) ;<br> bpsm2_ave:long_name = "BJ flash density" ;<br> bpsm2_ave:units = "1/s/m2" ;<br> bpsm2_ave:REFERENCE_TO = "bluejetbPaR_T_gp: bpsm2" ;<br> bpsm2_ave:coordinates = "lon lat" ;<br> bpsm2_ave:cell_methods = "time: mean" ;<br> double time_bnds(time, tbnds) ;<br> time_bnds:long_name = "time bounds" ;<br> time_bnds:units = "days since 2017-01-01T00:00:00Z" ;<br> time_bnds:cell_methods = "time: point"</p>
Ground Penetrating Radar data acquired over Von Postbreen, Svalbard, March 2018.
<p>GPR data and diffraction focusing Madagascar code</p> <p>Data authors: Richard Delf and Robert Bingham, University of Edinburgh.</p> <p>Data and code associated with Delf et al. "Reanalysis of polythermal glacier thermal structure using radar diffraction focussing"</p> <p>Ground-Penetrating Radar data was acquired over Von Postbreen, Svalbard, to investigate the internal velocity structure by diffraction focusing.</p> <p>Data were acquired in March 2018 over 2 days, using a PulseEkko Pro 25 MHz system, towed behind a snowscooter in an in-line, common-offset configuration. Data are stored in the SEGY format.</p> <p>Differential GPS was used for positioning; coordinates are included in the SEGY data.</p> <p><strong>Data Description</strong></p> <p>Three groups of data are present here within the .zip file.</p> <p>data with no preprocessing: files in ./data/aq_data/18_VP_0_*.SGY</p> <p> SEGY Radar data with pre-processing applied. Significant ringing is observed in the upper regions of the radargrams. Note file names do not correlate with the files in subsequent folders.</p> <p>data with preprocessing: filenames in ./data/raw_data/18_VP_1_*.SGY</p> <p> SEGY Radar data with pre-processing applied to remove ringing and other noise using an SVD filter and bandpass filtering. Radargrams are sorted into shorter lines across and up the glacier. See associated paper for additional details.</p> <p>processed_data: filenames 18_VP_2_*.SGY</p> <p> The same data as above, with diffraction coherence after Schwarz et al (2019) applied. See associated paper for additional details.</p> <p> </p> <p><strong>Processing files:</strong></p> <p>SConstruct: Madagascar SConstruct file for processing the above profiles to derive the velocity profiles described in the associated (Delf et al) paper.</p>
LPL2 - March 2021
<p>Measurements of LPL2 (PN Caldera de Taburiente) in March 2021</p> <p>El Paso, La Palma</p> <p>Light Pollution Laboratorie <a href="https://data.eelabs.eu/api/lpls/LPL2">info</a></p>
Dataset of first appearances of the scholarly bibliographic references on English Wikipedia articles as of 1 March 2017 and as of 1 October 2021
<p><strong>Abstract</strong></p> <p>We developed a methodology to detect the oldest scholarly reference added to Wikipedia articles by which a certain paper is uniquely identifiable as the "first appearance of the scholarly reference." We identified the first appearances of 923,894 scholarly references (611,119 unique DOIs) in180,795 unique pages on English Wikipedia as of March 1, 2017, and stored them in the dataset. Moreover, we assessed the precision of the dataset, which was and it was a high precision regardless of the research field. In this version, it is available not only the dataset of English Wikipedia as of March 1, 2017, but also English Wikipedia as of October 1, 2021, generated by using the same methodology.</p> <p> </p> <p><strong>Data Records</strong></p> <p>The data format of the dataset is JSON lines, where each line is a single record. In this dataset, we detected the first appearance of each scholarly reference added to Wikipedia articles. If there are multiple references corresponding to the same paper on the same page, only the oldest one is collected. Sample of the record is the following.</p> <ul> <li>doi -- DOI corresponding to the paper (String), e.g., "10.1006/anbe.1996.0497"</li> <li>paper_type -- Document type of the paper (String), e.g., "journal-article"</li> <li>paper_container_title -- Journal title, book title, or proceedings title (Array of String), e.g., ["Animal Behaviour"]</li> <li>paper_publisher -- Publisher name (String), e.g., "Elsevier BV"</li> <li>paper_title -- Paper title (Array of String), e.g., ["Push or pull: an experimental study on imitation in marmosets"]</li> <li>paper_published_year -- Published year (String), e.g., "1997"</li> <li>paper_issue -- Issue number (String), e.g., "4"</li> <li>paper_volume -- Volume number (String), e.g., "54"</li> <li>paper_page -- Page numbers (String), e.g., "817-831"</li> <li>paper_author -- Authors information consisted of given and family names, sequences (order in author names), and affiliations (Array of JSON), e.g., [{"given":"THOMAS", "family":"BUGNYAR", "sequence":"first", "affiliation":[]}, {"given":"LUDWIG", "family":"HUBER", "sequence":"additional", "affiliation":[]}]</li> <li>issn -- ISSN related to the paper (Array of String), e.g., ["0003-3472"]</li> <li>research_field -- Research fields from ESI categories (Array of String), e.g., ["PLANT & ANIMAL SCIENCE"]</li> <li>page_id -- Page id (String), e.g., "577858"</li> <li>page_title -- Page title (String), e.g., "Imitation"</li> <li>revision_id -- Revision id (String), e.g., "203309031"</li> <li>revision_timestamp -- Revision timestamp (String), e.g., "2008-04-04 15:54:09 UTC"</li> <li>revision_comment -- Revision comment (edit summary) (String), e.g., "/* Animal Behaviour */"</li> <li>editor_name -- Wikipedia editor's name (String), e.g., "Nicemr"</li> <li>editor_type -- Type of the editor (String), e.g., "User"</li> </ul> <p><strong>References</strong></p> <ul> <li>Kikkawa, J., Takaku, M. & Yoshikane, F. "Dataset of first appearances of the scholarly bibliographic references on Wikipedia articles", Scientific Data, Vol. 9, Article number 85, pp. 1-11, 2022. <a href="https://doi.org/10.1038/s41597-022-01190-z">https://doi.org/10.1038/s41597-022-01190-z</a>.</li> </ul> <p><strong>FUNDING</strong></p> <ul> <li>JSPS KAKENHI Grant Number <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-20K12543">JP20K12543</a></li> <li>JSPS KAKENHI Grant Number <a href="https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21K21303">JP21K21303</a></li> </ul>
Market prices in Tigray (March 2020 – March 2022)
<p>In northern Ethiopia, Tigray continues to be blockaded. The 2021 crop yield was just 25-50 percent of what it would be in a regular year. At the beginning of March 2022, colleagues and friends in Tigray provided us with average statistics on the cost of living in Tigray (especially food). Farmlands had been poorly ploughed and planted lately or not at all due to military targeting of farming activities. Hence, every crop has gotten more costly. As a result of distress or desperation sales, the price of live animals has dropped dramatically. Coffee, firewood, gasoline, and transportation have also seen significant price rises. Extreme shortages of food and energy supplies drive up inflation, but the fact that there are very limited amounts of cash in circulation curbs it.</p>
Play-by-Play Euroleague 21-22 (updated March 20th)
<p>Play-By-Play is a description of what happens in a each play in a basketball game. This information has been obtained through Web Scrapping. It contains information about the action, the player who performed it, the time of the match in which it was played and the partial result at that time. The manipulation of Play-By-Play should allow us to draw conclusions through advanced statistics, such as: what is the performance of the possessions; what value does the offensive rebound have in the game; etc.</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.