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12,237 results for “2019”
Marsh water table height, logging data from the Shad Creek Spartina marsh site for April-November 2019, Rowley, MA, PIE LTER.
Measurements of water table height in the Shad Creek marsh located near the Shad Creek eddy flux tower, Rowley, MA. Measurements were taken every 5 minutes at each logger along a transect of water level loggers running perpendicular to the Nelson stream bank at Nelson Island, MAR-PR-Wtable-Shad for April-November 2019.
PIE LTER eddy flux measurements during 2019 from second high marsh site (Spartina patens/short Spartina alterniflora) Tall Tower off Nelson Island Creek, Rowley, Massachusetts
We deployed an eddy covariance system to measure ecosystem-atmosphere exchange of CO2 above a high marsh system (Spartina patens, short Spartina alterniflora) located on the Parker River Wildlife Refuge in marshes of Plum Island Sound, Rowley MA. The system is located near a higher elevation rock outcropping protected area which allows the tower set up to remain during the winter as it is protected from ice flows. The data represents CO2 exchange for all 12 months of 2019.
Eddy flux measurements during 2019 from low marsh site (Spartina alterniflora) within Shad Creek catchment, Rowley, Massachusetts, PIE LTER.
We deployed an eddy covariance system to measure ecosystem-atmosphere exchange of CO2 above a low marsh system (Spartina alterniflora) located within the Shad creek catchment off Plum Island Sound, Rowley MA. The data represents CO2 exchange for mid-April through early November 2019. This site was established in 2015.
Years 2019-2021, 15 minute measurements of stage in a small headwater stream draining a highly suburban catchment (72% residential), Saw Mill Brook, Burlington, MA.
Year 2019, 2020, and 2021 continuous measurements, every 15 minutes, were made of depth and stream temperature in a small headwater stream, Saw Mill Brook, Burlington, MA, draining a highly suburban catchment (72% residential) in the Ipswich River watershed. Discharge is determined from stage using discharge vs stage regressions.
Year 2019-2021, 15 minute measurements of stage, water temperature in a small headwater stream draining a mainly wetland catchment (49% wetlands/swamp + 36% forest), Bear Meadow Brook, draining Cedar Swamp, Reading, MA.
Year 2019, 2020 and 2021 continuous measurements, every 15 minutes, were made of depth and stream temperature in a small headwater stream, Saw Mill Brook, Burlington, MA, draining a highly suburban catchment (72% residential) in the Ipswich River watershed. Discharge is determined from stage using discharge vs stage regressions.
High-frequency observations of overwash water and bed elevations from five MeOw (Measuring Overwash) stations: Smith Island, VA, August-November 2019
Water and bed surface elevation measurements from five MeOw (Measuring Overwash) stations installed on Smith Island, VA, from 21 August 2019 to 22 November 2019. The five MeOw stations consist of one ultrasonic distance sensor inside a stilling well to measure water levels, and a second attached to an arm extending outwards from the well to measure the elevation of the sediment surface (when dry) or the water surface (when inundated). Note: Station MeOw2 was damaged and produced no data, and so is not included in this dataset.
Blue Crab Densities in a Landscape Context on the Virginia Coast, Summer 2019
Table 1 Crab Data : Blue crab (Callinectes sapidus) abundance was sampled at twenty-four sites in Spider Crab Bay, Cobb Bay, and South Bay (comprising two large seagrass meadows: "South Bay" for the northern portion and "Ship Shoal" for the southern portion) as part of a study to evaluate the drivers of blue crab spatial patterns across restored seagrass meadows in the coastal bays of Virginia, USA. The four meadows are representative of seagrass meadows in the region that have recovered after experiencing large declines due to wasting disease and storms (Orth et al., 2006). Each meadow contained six fixed sampling sites. Each captured crab's morphological and demographic characteristics were evaluated; as such, data are presented at the individual level. Table 2 Landscape Data: Landscape measurements were taken for twenty-four sites in Spider Crab Bay, Cobb Bay, and South Bay (comprising two large seagrass meadows: "South Bay" for the northern portion and "Ship Shoal" for the southern portion) as part of a study to evaluate the drivers of blue crab spatial patterns across restored seagrass meadows in the coastal bays of Virginia, USA. The four meadows are representative of seagrass meadows in the region that have recovered after experiencing large declines due to wasting disease and storms (Orth et al. 2006). Each meadow contained six fixed sampling sites. All landscape measurements were made using QGIS 3.14.0 (QGIS.org 2020). Seagrass landscape measurements were calculated using maps developed from aerial imagery by Orth et al. (2019). Oyster reefs polygons were mapped by Ross and Luckenbach (2009). Salt marsh polygons were mapped by Berman et al. (2011) and modified based on imagery from Planet Team (2019). Mean seagrass shoot density measurements were based on a VCR-wide 2019 sampling effort led by McGlathery (2018).
ICDAR 2019 Competition on Baseline Detection (cBAD)
<p>This dataset contains the training, evaluation, and test set for the ICDAR 2019 Competition on Baseline Detection (cBAD).</p> <p>A newly created freely available real world dataset consisting of 3021 annotated document page images that are collected from seven European archives and form the basis of cBAD. The baselines in all images were manually annotated. The training and the evaluation sets contain PAGE XMLs with annotated text regions and baselines.</p> <p>Competition Website: https://scriptnet.iit.demokritos.gr/competitions/11/</p>
Data for Research Assessment in the Transition to Open Science. 2019 EUA Open Science and Access Survey Results
<p>This database refers to the data collected by the European University Association (EUA) for its Open Science and Access Survey 2019, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at <a href="https://eua.eu/resources/publications/888:research-assessment-in-the-transition-to-open-science.html">https://eua.eu/resources/publications/888:research-assessment-in-the-transition-to-open-science.html</a>.</p> <p>The data included in this database refers only to those universities and higher education institutions that accepted their data to be available in open access (n=174). All information that could lead to the identification of individual universities and higher education institutions was removed from the database (cf. cells highlighted in red). The following files are available:</p> <ul> <li>2019 EUA Open Science and Access Survey</li> <li>Database in the following formats: .xlsx (Microsoft Excel)</li> <li>Survey Codebook: includes information on all the variables and their coding.</li> </ul>
Via Sacra Pond YSI Data from 2019-11-20 to 2019-12-04
<p>General Metadata for Via Sacra Pond YSI Data</p> <p> </p> <p>Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <pre><code>via_sacra_pond_YYYY-MM-DD_metadata.txt </code></pre> <p>Where YYYY-MM-DD is the date that the deployment ended.</p> <p> </p> <p>File Created</p> <ul> <li>2020-01-14 by KF</li> </ul> <p> </p> <p>File Modified</p> <p> </p> <p>Description</p> <p>These data are from a YSI EXO3 multiparameter sonde deployed at approximatly 0.4 m in Via Sacra Pond (37.223340 N, -78.473026 W) in approximately 1 m of water.</p> <p> </p> <p>Sonde Specifics</p> <p>The specific sensors and SNs deployed are on the metadata specific to each deployment but generally the sonde has:</p> <ul> <li>Temperature</li> <li>Conductivity</li> <li>ODO</li> <li>Total Algae</li> <li>Turbidity</li> <li>fDOM</li> </ul> <p> </p> <p>Measurement Parameters, units, and Variable Names</p> <ul> <li>date.time = the combined date and time of the measurement (YYYY-MM-DD HH:MM:SS)</li> <li>date = the sampling date reported by the YSI (M/D/YYYY)</li> <li>time = the sampling time reported by the YSI (HH:MM:SS)</li> <li>fract_sec = the sampling fractions of a second reported by the YSI</li> <li>site_name = the site name on the YSI deployment (left blank)</li> <li>chl_RFU = the chlorophyll concentration (RFU)</li> <li>chl_conc = the chlorophyll concentration (ug/L)</li> <li>cond = the conductivity (uS/cm)</li> <li>nLF_cond = the nLF conductivity (uS/cm)</li> <li>perc_ODO = the percent oxygen saturation</li> <li>perc_local_ODO = the percent local oxygen saturation</li> <li>ODO_conc = the dissolved oxygen concentration (mg/L)</li> <li>sal = the salinity (psu)</li> <li>sp_cond = the specific conductivity (uS/cm)</li> <li>BGA_PC_RFU = the Blue-green algae Phycocyanin concentration (RFU)</li> <li>BGA_PC_conc = the Blue-green algae Phycocyanin concentration (ug/L)</li> <li>TDS = the total dissolved solids (mg/L)</li> <li>temp = the temperature (dC)</li> <li>turb_FNU = the turbidity (FNU)</li> <li>TSS = total suspended solids</li> <li>battery = the battery voltage</li> <li>cable_pwr = the cable power</li> </ul>
Via Sacra Pond YSI Data from 2019-12-18 to 2020-01-09
<p>General Metadata for Via Sacra Pond YSI Data</p> <p> </p> <p>Files</p> <p>Specific metadata for each deployment can be found as text files with the file format of:</p> <pre><code>via_sacra_pond_YYYY-MM-DD_metadata.txt </code></pre> <p>Where YYYY-MM-DD is the date that the deployment ended.</p> <p> </p> <p>File Created</p> <ul> <li>2020-01-14 by KF</li> </ul> <p> </p> <p>File Modified</p> <p> </p> <p>Description</p> <p>These data are from a YSI EXO3 multiparameter sonde deployed at approximatly 0.4 m in Via Sacra Pond (37.223340 N, -78.473026 W) in approximately 1 m of water.</p> <p> </p> <p>Sonde Specifics</p> <p>The specific sensors and SNs deployed are on the metadata specific to each deployment but generally the sonde has:</p> <ul> <li>Temperature</li> <li>Conductivity</li> <li>ODO</li> <li>Total Algae</li> <li>Turbidity</li> <li>fDOM</li> </ul> <p> </p> <p>Measurement Parameters, units, and Variable Names</p> <ul> <li>date.time = the combined date and time of the measurement (YYYY-MM-DD HH:MM:SS)</li> <li>date = the sampling date reported by the YSI (M/D/YYYY)</li> <li>time = the sampling time reported by the YSI (HH:MM:SS)</li> <li>fract_sec = the sampling fractions of a second reported by the YSI</li> <li>site_name = the site name on the YSI deployment (left blank)</li> <li>chl_RFU = the chlorophyll concentration (RFU)</li> <li>chl_conc = the chlorophyll concentration (ug/L)</li> <li>cond = the conductivity (uS/cm)</li> <li>nLF_cond = the nLF conductivity (uS/cm)</li> <li>perc_ODO = the percent oxygen saturation</li> <li>perc_local_ODO = the percent local oxygen saturation</li> <li>ODO_conc = the dissolved oxygen concentration (mg/L)</li> <li>sal = the salinity (psu)</li> <li>sp_cond = the specific conductivity (uS/cm)</li> <li>BGA_PC_RFU = the Blue-green algae Phycocyanin concentration (RFU)</li> <li>BGA_PC_conc = the Blue-green algae Phycocyanin concentration (ug/L)</li> <li>TDS = the total dissolved solids (mg/L)</li> <li>temp = the temperature (dC)</li> <li>turb_FNU = the turbidity (FNU)</li> <li>TSS = total suspended solids</li> <li>battery = the battery voltage</li> <li>cable_pwr = the cable power</li> </ul>
Applications on the F-Droid Website (May-June 2019)
<p>The applications on the f-droid website (https://f-droid.org/en/). These applications were scraped around May-June 2019</p>
Model outputs for occurrence and hunting data‐based models of wild boar distribution and abundance, July 2019 update
<p>These maps are wild boar habitat suitability outputs based on newly available data of wild boar, and models for predicting wild boar relative abundance using hunting yields.</p> <p><strong>Objectives</strong>:</p> <p>- Validation of previously produced hunting yield maps and new ones<br> - Downscaling to 10x10 km grid<br> - Downscaling to 2x2 km grid</p> <p><strong>Model settings and predictors: </strong> <br> - Model from ENETWILD report August 2019<br> - Assuming cells as municipality in 10x10 km grid downscaling<br> - Assuming cells as hunting grounds in 2x2 km grid downscaling </p> <p><strong>Conclusions guiding future methodological steps</strong><br> - To update wild boar hunting yield data for some specific regions;<br> - To increase hunting yield data resolution;<br> - To explore model independent parametrization for each bioregion.</p> <p><strong>Files:</strong></p> <p>August_2019_HY_nut00_10x10 >> Model outputs based on hunting yield GLM analyses<br> August_2019_occurrences_bioclim >> Model outputs based on Bioclim analyses<br> August_2019_occurrences_glm >> Model outputs based on Generalised linear model<br> August_2019_occurrences_ksvm >> Model outputs based on Support vector Machine analyses<br> August_2019_occurrences_maxent >> Model outputs based on Maxent analyses<br> August_2019_occurrences_randomForest>> Model outputs based on Random Forest analyses</p> <p>---------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>These maps are models obtained in intermediate phases of the ENETWILD project based on available information. <br> There are frequent updates in order to improve the results. For methodological approach and details check the paper: </p> <p>ENETWILD‐consortium, P. Acevedo, S .Croft, G C Smith, J. A. Blanco-Aguiar, J. Fernandez-Lopez, M. Scandura, M. Apollonio, E.Ferroglio, Oliver Keuling, M. Sange, S. Zanet, F. Brivio, T. Podgórski, K.Petrović, G. Body, A. Cohen, R. Soriguer, J. Vicente (2019). ENETwild modelling of wild boar distribution and abundance: update of occurrence and hunting data‐based models. EFSA Supporting Publications, 16(8), 1674E.<br> <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fefsa.onlinelibrary.wiley.com%2Fdoi%2Fabs%2F10.2903%2Fsp.efsa.2019.EN-1674&data=02%7C01%7C%7Ca8ad922eefde42f5cb5208d7c5054851%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637194498792136402&sdata=fqdiYEOqYIlHaDbp5a7kVdGQ6FWuFEydJNhSWOghH%2FQ%3D&reserved=0">https://efsa.onlinelibrary.wiley.com/doi/abs/10.2903/sp.efsa.2019.EN-1674</a></p> <p>.</p> <p>Permission for reuse occurrence outputs records is granted under the terms of a CC-BY-NC license.<br> Permission for reuse hunting yield outputs is granted under the terms indicated by EFSA.</p>
Coseismic ground deformation map for the Mw5.7 20 March, 2019 Acipayam earthquake (Turkey)
<p>This repository contains an interferogram from Sentinel-1 satellite acquired over the Mw5.7 20 March, 2019 Acipayam earthquake (Turkey). This interferogram was obtained by processing Sentinel-1 SAR data with the JPL-developed InSAR Scientific Computing Environment (ISCE) open-source software package.</p> <p>Sentinel-1 descending track 138:</p> <ul> <li>Master image: 11 Mar 2019, identifier: S1A_IW_SLC_1SDV_20190311T040709_20190311T040736_026285_02F00C_6A73</li> <li>Slave image: 23 Mar 2019, identifier: S1A_IW_SLC_1SDV_20190323T040709_20190323T040736_026460_02F67D_EC68</li> </ul> <p>This interferogram was generated for figures in: Jiang, Y., and González, P. J. (2020). "Bayesian inversion of wrapped satellite interferometric phase to estimate fault and volcano surface ground deformation models. " JGR: Solid Earth.</p>
PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2019 Week 43
<p>These data were collected from FIDIA machine tool controller during milling operation.</p> <p> </p>
PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2019 Week 52
<p>These data were collected from FIDIA machine tool controller during milling operation.</p> <p> </p>
PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2019 Week 38
<p>These data were collected from FIDIA machine tool controller during milling operation.</p>
PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2019 Week 48
<p>These data were collected from FIDIA machine tool controller during milling operation.</p> <p> </p>
PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2019 Week 51
<p>These data were collected from FIDIA machine tool controller during milling operation.</p> <p> </p>
PROGRAMS project. Machine tool data from Aurrenak S. Coop. on 2019 Week 50
<p>These data were collected from FIDIA machine tool controller during milling operation.</p> <p> </p>
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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.