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12,237 results for “2019”
11 April 2019 MRMS Data Subset for Harkema et al. (2024)
<p>Subset of Multi-Radar Multi-Sensor (MRMS) data: 3D Reflectivity, Composite Reflectivity, and Isothermal Reflectivity. More specifically, the subset data is between 00 and 16 UTC 11 April 2019. Note: Some time(s) and file(s) maybe be missing between the subset period.</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor (2019): BSF, minNDTI, and NOS
<h2><strong>Data information</strong></h2> <p>This dataset provides annual aggregated predictors BSF, minNDTI, and NOS for the year 2019. They are calculated as follows:</p> <ul> <li><strong>minNDTI (minimum NDTI)</strong> is determined as the minimum value of the six NDTI values over a year (<a href="https://www.sciencedirect.com/science/article/pii/S0034425711003439?casa_token=5P1XeaRMuT4AAAAA:8VyzOkBJb_gbpap0uzjuT6YoYhylvqc_vY2-AOX2KCsZGrwQw1jySGd4Ox9JFz55TPQmF231IQ">Zheng et al., 2012</a>).</li> <li><strong>BSF (bare soil fraction)</strong> is calculated by dividing the number of pixels classified as bare surface within a year's time series (identified by the criterion of NDVI values below 0.35) by the total number of pixels analyzed in that year (<a href="https://www.mdpi.com/2072-4292/11/18/2121">Castaldi et al., 2019</a>).</li> <li><strong>NOS (number of seasons)</strong> indicates the frequency of cropping cycles within a year, calculated by counting NDVI peaks throughout the year (<a href="https://www.mdpi.com/2072-4292/6/3/2473">Li et al., 2014</a>, <a href="https://www.sciencedirect.com/science/article/pii/S0034425720304685?casa_token=u4YFBsBotd4AAAAA:TgMiw5E7HNA9-oSF-oszmaRLzLPQJJmV-I13lvdkog1ibfEIZoPxJ3jA4F8Jcu5eXpzVBOyjew">Liu et al., 2020</a>). </li> </ul> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P75 (2019): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P75 (percentile 75) values of corresponding predictors for the year 2019. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P50 (2019): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P50 (median) values of corresponding predictors for the year 2019. The median values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P25 (2019): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P25 (percentile 25) values of corresponding predictors for the year 2019. The P25 values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
University of New Hampshire Pressure Mapped Munition (PMM) Experiments 2019 - 2021
<p>This project contains the data collected by the University of New Hampshire (UNH) Coastal Processes Lab for three field experiments focusing on investigating munition mobility in nearshore/surfzone regions. The first experiment took place on May 17 2019, the second experiment took place on February 1-2 2021, and the last experiment to place on October 26-27 2021. The data was collected at Wallis Sands Beach in Rye, New Hampshire. </p> <p>The main instrument utilized was the Pressure Mapped Munition (PMM), which is a fully autonomous cylindrical surrogate munition that can resolve the pressure field on its surface. The PMM is constructed from a 229 mm long section of 304 stainless steel pipe with a 140 mm outside diameter and 12.7 mm wall thickness. To make autonomous measurements of surface pressure and relative position, the PMM houses 16 high resolution TE Connectivity MS5837-02BA absolute pressure sensors and a Lord MicroStrain 3DM®-GX5-25 inertial measurement unit (IMU). There are two rings of pressure sensors around the cylinder. Pressure sensors 1-8 are in ring 1 and pressure sensors 9-16 are in ring 2. Each ring is 6.35 cm from the ends of the cylinder and the rings are 15.24 cm apart. The radial spacing between sensors around each ring is 45°, which minimizes the arc length and height difference between sensors while maintaining axial pairs of sensors. This orientation allows for sensor redundancy in the case of sensor failure and allows for error correction if faulty values from one of the sensors is suspected. When the PMM is lying flat the vertical displacement between the topmost and bottommost sensor is 129 - 140 mm depending on the orientation of the instrument. </p> <p>For the May experiment, a Nortek Vector was deployed along with the PMM. The Nortek Vector is an Acoustic Doppler Velocimeter (ADV) which collects high-resolution, single-point, 3-dimensional velocity data (Nortek, 2022b). For this experiment the ADV transducer was about 30 cm above the sediment bed while the ADV pressure sensor was about 50 cm above the sediment bed. The Nortek Vector was not deployed for the Feburary and October experiments.</p> <p>For the February and October experiments, another instrument, the Pressure Stick (PS), was deployed with the PMM. The Pressure Stick is a fully autonomous pressure-profiling instrument. The PS contains eight micro-controlled, time-synced TE Connectivity MS5837-02BA absolute pressure and temperature sensors distributed along a 70 cm distance to measure pressure throughout the water column and into the sediment bed. The Pressure Stick allows for a quantification of intermittent bed instability (momentary liquefaction) and its potential role in munition mobility. More information about the PS can be found in Marry & Foster (2024).</p> <p>Global Positioning System (GPS) surveys of the beach profile were also completed on February 2, 2021 (at the end of the experiment) and October 26, 2021 (at the beginning of the experiment), and the survey data is included in this project. Data of the offshore wave conditions as measured by the Jeffrey's Ledge waverider buoy (station 44098) from the National Data Buoy Center (NDBC) (https://www.ndbc.noaa.gov/station_page.php?station=44098) for all three experiments are given here as well.</p> <p>This effort was supported by Strategic Environmental Research and Development Program (SERDP, Project Number 17 MR-2731). These datasets are presented in the final report for Project Number 17 MR-2731.</p> <h2>Dataset files</h2> <p>A description of each data file is given below:</p> <h3>Pressure Mapped Munition (PMM) Data</h3> <p>1. PMM_20190517.txt<br> This file contains the raw data from the PMM for the experiment on May 17, 2019<br> - Date = date of sample<br> - Time = time of sample<br> - P1 = pressure from sensor 1 in mbar<br> - P2 = pressure from sensor 2 in mbar<br> - P3 = pressure from sensor 3 in mbar<br> - P4 = pressure from sensor 4 in mbar<br> - P5 = pressure from sensor 5 in mbar<br> - P6 = pressure from sensor 6 in mbar<br> - P7 = pressure from sensor 7 in mbar<br> - P8 = pressure from sensor 8 in mbar<br> - P9 = pressure from sensor 9 in mbar<br> - P10 = pressure from sensor 10 in mbar<br> - P11 = pressure from sensor 11 in mbar<br> - P12 = pressure from sensor 12 in mbar<br> - P13 = pressure from sensor 13 in mbar<br> - P14 = pressure from sensor 14 in mbar<br> - P15 = pressure from sensor 15 in mbar<br> - P16 = pressure from sensor 16 in mbar<br> - T = temperature in degrees Celsius</p> <p>2. IMU_20190517.txt<br> This file contains the raw IMU data from the PMM for the experiment on May 17, 2019<br> - Date = date of sample<br> - Time = time of sample<br> - yaw = orientation of the IMU in the yaw direction, measured in degrees<br> - pitch = orientation of the IMU in the pitch direction, measured in degrees. Due to the orientation of the IMU in the PMM, the pitch time series refers to when there are changes in rotation along the long axis of the PMM (i.e. when it rolls). <br> - roll = orientation of the IMU in the roll direction, measured in degrees. Due to the orientation of the IMU in the PMM, the roll time series refers to when there are changes in rotation along the short axis of the PMM (i.e. when it tilts and one end of the cylinder is higher than the other end). </p> <p>3. PMM_20210202.txt<br> This file contains the raw data from the PMM for the experiment on February 1-2, 2021<br> - Date = date of sample<br> - Time = time of sample<br> - P1 = pressure from sensor 1 in mbar<br> - P2 = pressure from sensor 2 in mbar<br> - P3 = pressure from sensor 3 in mbar<br> - P4 = pressure from sensor 4 in mbar<br> - P5 = pressure from sensor 5 in mbar<br> - P6 = pressure from sensor 6 in mbar<br> - P7 = pressure from sensor 7 in mbar<br> - P8 = pressure from sensor 8 in mbar<br> - P9 = pressure from sensor 9 in mbar<br> - P10 = pressure from sensor 10 in mbar<br> - P11 = pressure from sensor 11 in mbar<br> - P12 = pressure from sensor 12 in mbar<br> - P13 = pressure from sensor 13 in mbar<br> - P14 = pressure from sensor 14 in mbar<br> - P15 = pressure from sensor 15 in mbar<br> - P16 = pressure from sensor 16 in mbar<br> - T = temperature in degrees Celsius</p> <p>4. IMU_20210202.txt<br> This file contains the raw IMU data from the PMM for the experiment on February 1-2, 2021<br> - Date = date of sample<br> - Time = time of sample<br> - yaw = orientation of the IMU in the yaw direction, measured in degrees<br> - pitch = orientation of the IMU in the pitch direction, measured in degrees. Due to the orientation of the IMU in the PMM, the pitch time series refers to when there are changes in rotation along the long axis of the PMM (i.e. when it rolls). <br> - roll = orientation of the IMU in the roll direction, measured in degrees. Due to the orientation of the IMU in the PMM, the roll time series refers to when there are changes in rotation along the short axis of the PMM (i.e. when it tilts and one end of the cylinder is higher than the other end). </p> <p>5. PMM_20211026.txt<br> This file contains the raw data from the PMM for the experiment on October 26-27, 2021<br> - Date = date of sample<br> - Time = time of sample<br> - P1 = pressure from sensor 1 in mbar<br> - P2 = pressure from sensor 2 in mbar<br> - P3 = pressure from sensor 3 in mbar<br> - P4 = pressure from sensor 4 in mbar<br> - P5 = pressure from sensor 5 in mbar<br> - P6 = pressure from sensor 6 in mbar<br> - P7 = pressure from sensor 7 in mbar<br> - P8 = pressure from sensor 8 in mbar<br> - P9 = pressure from sensor 9 in mbar<br> - P10 = pressure from sensor 10 in mbar<br> - P11 = pressure from sensor 11 in mbar<br> - P12 = pressure from sensor 12 in mbar<br> - P13 = pressure from sensor 13 in mbar<br> - P14 = pressure from sensor 14 in mbar<br> - P15 = pressure from sensor 15 in mbar<br> - P16 = pressure from sensor 16 in mbar<br> - T = temperature in degrees Celsius</p> <p>6. IMU_20211026.txt<br> This file contains the raw IMU data from the PMM for the experiment on October 26-27, 2021<br> - Date = date of sample<br> - Time = time of sample<br> - yaw = orientation of the IMU in the yaw direction, measured in degrees<br> - pitch = orientation of the IMU in the pitch direction, measured in degrees. Due to the orientation of the IMU in the PMM, the pitch time series refers to when there are changes in rotation along the long axis of the PMM (i.e. when it rolls). <br> - roll = orientation of the IMU in the roll direction, measured in degrees. Due to the orientation of the IMU in the PMM, the roll time series refers to when there are changes in rotation along the short axis of the PMM (i.e. when it tilts and one end of the cylinder is higher than the other end). </p> <h3>Nortek Vector Data</h3> <p>Vector_20190517.txt<br> This file contains the Nortek Vector data from the experiment on May 17 2019.<br> - Vec_time = time of Vector samples<br> - Vpress = pressure in mbar<br> - Vpress_smooth = smoothed pressure in mbar<br> - Vu = cross-shore velocity in m/s<br> - Vv = along-shore velocity in m/s<br> - Vw = vertical velocity in m/s</p> <h3>Pressure Stick (PS) Data</h3> <p>1. PS_20210202.txt</p> <p> This file contains the raw data from the Pressure Stick for the experiment on February 1-2, 2021<br> - Date = date of sample<br> - Time = time of sample<br> - P1 = pressure from sensor 1 (topmost sensor) in mbar, sensor 1 is about 25 cm above the sediment bed<br> - P2 = pressure from sensor 2 in mbar, sensor 2 is about 15 cm above the sediment bed<br> - P3 = pressure from sensor 3 in mbar, sensor 3 is about 9 cm above the sediment bed<br> - P4 = pressure from sensor 4 in mbar, sensor 4 is about 3 cm above the sediment bed<br> - P5 = pressure from sensor 5 in mbar, sensor 5 is about 3 cm in the sediment bed<br> - P6 = pressure from sensor 6 in mbar, sensor 6 is about 13 cm in the sediment bed<br> - P7 = pressure from sensor 7 in mbar, sensor 7 is about 28 cm in the sediment bed<br> - P8 = pressure from sensor 8 (bottom-most sensor) in mbar, sensor 8 is about 43 cm in the sediment bed<br> - T1 = temperature from sensor 1 (topmost sensor) in degrees Celsius, sensor 1 is about 25 cm above the sediment bed<br> - T2 = temperature from sensor 2 in degrees Celsius, sensor 2 is about 15 cm above the sediment bed<br> - T3 = temperature from sensor 3 in degrees Celsius, sensor 3 is about 9 cm above the sediment bed<br> - T4 = temperature from sensor 4 in degrees Celsius, sensor 4 is about 3 cm above the sediment bed<br> - T5 = temperature from sensor 5 in degrees Celsius, sensor 5 is about 3 cm in the sediment bed<br> - T6 = temperature from sensor 6 in degrees Celsius, sensor 6 is about 13 cm in the sediment bed<br> - T7 = temperature from sensor 7 in degrees Celsius, sensor 7 is about 28 cm in the sediment bed<br> - T8 = temperature from sensor 8 (bottom-most sensor) in degrees Celsius, sensor 8 is about 43 cm in the sediment bed </p> <p>2. PS_20211026.txt</p> <p> This file contains the raw data from the Pressure Stick for the experiment on October 26-27, 2021<br> - Date = date of sample<br> - Time = time of sample<br> - P1 = pressure from sensor 1 (topmost sensor) in mbar, sensor 1 is about 19 cm above the sediment bed<br> - P2 = pressure from sensor 2 in mbar, sensor 2 is about 9 cm above the sediment bed<br> - P3 = pressure from sensor 3 in mbar, sensor 3 is about 3 cm above the sediment bed<br> - P4 = pressure from sensor 4 in mbar, sensor 4 is about 3 cm in the sediment bed<br> - P5 = pressure from sensor 5 in mbar, sensor 5 is about 9 cm in the sediment bed<br> - P6 = pressure from sensor 6 in mbar, sensor 6 is about 19 cm in the sediment bed<br> - P7 = pressure from sensor 7 in mbar, sensor 7 is about 34 cm in the sediment bed<br> - P8 = pressure from sensor 8 (bottom-most sensor) in mbar, sensor 8 is about 49 cm in the sediment bed<br> - T1 = temperature from sensor 1 (topmost sensor) in degrees Celsius, sensor 1 is about 19 cm above the sediment bed<br> - T2 = temperature from sensor 2 in degrees Celsius, sensor 2 is about 9 cm above the sediment bed<br> - T3 = temperature from sensor 3 in degrees Celsius, sensor 3 is about 3 cm above the sediment bed<br> - T4 = temperature from sensor 4 in degrees Celsius, sensor 4 is about 3 cm in the sediment bed<br> - T5 = temperature from sensor 5 in degrees Celsius, sensor 5 is about 9 cm in the sediment bed<br> - T6 = temperature from sensor 6 in degrees Celsius, sensor 6 is about 19 cm in the sediment bed<br> - T7 = temperature from sensor 7 in degrees Celsius, sensor 7 is about 34 cm in the sediment bed<br> - T8 = temperature from sensor 8 (bottom-most sensor) in degrees Celsius, sensor 8 is about 49 cm in the sediment bed </p> <h3>GPS Data</h3> <p>1. UNH_GPS_WS_20210202.txt (.pos)</p> <p> This file contains data from a GPS survey taken on February 2nd 2021 as the post-deployment survey for the February experiment. <br> - GPST = time stamp of the survey sample <br> - latitude = latitude in degrees<br> - longitude = longitude in degrees<br> - height = elevation height, relative to WGS84/ellipsoidal in meters<br> - Q = quality of the data point. Q = 1 is a 'good' data point, Q = 2 is an 'okay' data point, and Q > 2 are 'bad' data points. <br> - ns = Number of satellites<br> <br>2. UNH_GPS_WS_20211026.txt (.pos)</p> <p> This file contains data from a GPS survey taken on October 26th 2021 as the pre-deployment survey for the October experiment. <br> - GPST = time stamp of the survey sample <br> - latitude = latitude in degrees<br> - longitude = longitude in degrees<br> - height = elevation height, relative to WGS84/ellipsoidal in meters<br> - Q = quality of the data point. Q = 1 is a 'good' data point, Q = 2 is an 'okay' data point, and Q > 2 are 'bad' data points. <br> - ns = Number of satellites</p> <h3>Waverider Buoy (NDBC station 44098) Data</h3> <p>1. NDBC_44098_JeffreysLedgeBuoy_May2019.txt</p> <p> This file contains offshore wave data from Jeffrey's Ledge waverider buoy (station 44098) from the National Data Buoy Center (NDBC)(https://www.ndbc.noaa.gov/station_page.php?station=44098) throughout the May 2019 experiment (May 17th 2019 00:08:00 - May 18th 2019 12:38:00). Please see "Description of Measurements" at https://www.ndbc.noaa.gov/measdes.shtml for a discussion of each of the variables in this file.</p> <p>2. NDBC_44098_JeffreysLedgeBuoy_February2021.txt</p> <p> This file contains offshore wave data from Jeffrey's Ledge waverider buoy (station 44098) from the National Data Buoy Center (NDBC)(https://www.ndbc.noaa.gov/station_page.php?station=44098) throughout the February 2021 experiment (February 1st 2021 01:26:00 - Feburary 2nd 2021 09:56:00). Please see "Description of Measurements" at https://www.ndbc.noaa.gov/measdes.shtml for a discussion of each of the variables in this file.</p> <p>3. NDBC_44098_JeffreysLedgeBuoy_October2021.txt</p> <p> This file contains offshore wave data from [Jeffrey's Ledge waverider buoy (station 44098) from the National Data Buoy Center (NDBC)](https://www.ndbc.noaa.gov/station_page.php?station=44098) throughout the October 2021 experiment (October 26th 2021 00:26:00 - October 27th 2021 23:56:00). Please see "Description of Measurements" at https://www.ndbc.noaa.gov/measdes.shtml for a discussion of each of the variables in this file.</p>
Disclosure UK Methodological Notes Data 2015, 2017, 2019
<p>Data extracted from Disclosure UK methodological notes for the years 2015, 2017 and 2019 for companies who made payment disclosures on Disclosure UK in the years 2015, 2017 and 2019. Data extracted relates to guidance from the The Prescription Medicines Code of Practice Authority and requirements in the code of conduct developed by the Association of the British Pharmaceutical Industry.</p>
2019_Herbaceous_Wetlands_Copernicus
<p>Copernicus global land cover, herbaceous wetlands, 100m, and proportion herbaceous wetland (1km) in 2019. </p> <p><strong>Abstract</strong>:</p> <p>Landuse/landcover datasets are provided through the Copernicus climate data service, (Buchhorn, M.; Smets, B.; Bertels, L.; De Roo, B.; Lesiv, M.; Tsendbazar, N.E., Linlin, L., Tarko, A. (2020): Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015-2019: Product User Manual; Zenodo, Geneve, Switzerland, September 2020; doi: 10.5281/zenodo.3938963).</p> <p>This 100m resolution product has been windowed to the MOOD extent (erprobaherbwet100m.tif). and then aggregated to 1km resolution version which contains the proportion of each pixel that is assigned as herbaceous wetland (erprobapropherbwet1km.tif)</p> <p> </p> <p><strong>File naming scheme:</strong> </p> <p>This 100m resolution product has been windowed to the MOOD extent (erprobaherbwet100m.tif). and then aggregated to 1km resolution version which contains the proportion of each pixel that is assigned as herbaceous wetland (erprobapropherbwet1km.tif)</p> <p><strong>Projection + EPSG code:</strong><br>Latitude-Longitude/WGS84 (EPSG: 4326)</p> <p><strong>Spatial extent:</strong><br>Extent -32.0000000000000000,10.0000000000000000 : 68.9999999999999574,81.9999999999999716</p> <p><strong>Spatial resolution:</strong><br>100-meter and 1000-meter</p> <p><strong>Temporal resolution:</strong><br>The year 2019</p> <p><strong>Pixel values:</strong><br> The proportion of each pixel that is assigned as herbaceous wetland</p> <p><strong>Source: </strong><br>The Copernicus climate data service</p> <p><strong>Software used:</strong><br>The software used for map production is ESRI ArcMap 10.8</p> <p><strong>License: </strong>CC-BY-SA 4.0<br><strong>Processed by:</strong><br>ERGO (Environmental Research Group Oxford) https://ergoonline.co.uk/ for the H2020 MOOD project</p>
Metagenome-assembled genomes from Stordalen Mire, Sweden (2019) (MAGs from long-read, short-read, & hybrid assemblies)
<p>METHODS:</p> <p>Soil samples (6 total) were collected at the Stordalen Mire site in 2019 from two depths (1-5 & 20-24 cm below ground) across three habitats (Palsa, Bog, and Fen). DNA was extracted based on the protocol described by <a href="http://dx.doi.org/10.17504/protocols.io.yxmvm244bg3p/v1">Li et al. (2024)</a>. For short reads, libraries were prepared at the Joint Genome Institute (JGI) with the KAPA Hyperprep kit, and sequenced with Illumina NovaSeq 6000. For long reads, libraries were prepared with the SMRTbell Express Template Prep Kit 2.0 (PacBio), then sequenced using PacBio Sequel IIe at JGI. PacBio data was processed at JGI to form filtered CCS (Circular Consensus Sequencing) reads. </p> <p>Assemblies were generated with short-only, long-only, and hybrid read sources: <strong>Short-only</strong> was assembled with <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411777/">metaSPAdes</a> (v3.15.4) using <a href="https://zenodo.org/records/10806928">Aviary</a> (v0.5.3) with default parameters. <strong>Long-only</strong> was assembled with <a href="https://www.nature.com/articles/s41592-020-00971-x">metaFlye</a> (v2.9-b1768) using <a href="https://zenodo.org/records/10806928">Aviary</a> (v0.5.3) with default parameters. <strong>Hybrid</strong> assembly was performed using <a href="https://zenodo.org/records/10806928">Aviary</a> v0.5.3 with default parameters. This involved a step-down procedure with long-read assembly through <a href="https://www.nature.com/articles/s41592-020-00971-x">metaFlye</a> (v2.9-b1768), followed by short-read polishing by <a href="https://genome.cshlp.org/content/27/5/737">Racon</a> (v1.4.3), <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0112963">Pilon</a> (v1.24) and then Racon again. Next, reads that didn't map to high-quality metaFlye contigs were hybrid assembled with <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5411777/">SPAdes (--meta option)</a> and binned out with <a href="https://peerj.com/articles/7359/">MetaBAT2</a> (v2.1.5). For each bin, the reads within the bin were hybrid assembled using <a href="https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005595">Unicycler</a> (v0.4.8). The high-coverage metaFlye contigs and Unicycler contigs were then combined to form the assembly fasta file. Genome recovery was performed using <a href="https://zenodo.org/records/10806928">Aviary</a> v0.5.3 with samples chosen for differential abundance binning by <a href="https://zenodo.org/records/10939393">Bin Chicken</a> (v0.4.2) using <a href="https://zenodo.org/records/7130825">SingleM metapackage S3.0.5</a>. This involved initial read mapping through <a href="https://zenodo.org/records/10531254">CoverM</a> (v0.6.1) using <a href="https://academic.oup.com/bioinformatics/article/34/18/3094/4994778">minimap2</a> (v2.18) and binning by <a href="https://peerj.com/articles/1165/">MetaBAT</a>, <a href="https://peerj.com/articles/7359/">MetaBAT2</a> (v2.1.5), <a href="https://www.nature.com/articles/s41587-020-00777-4">VAMB</a> (v3.0.2), <a href="http://doi.org/10.1038/s41467-022-29843-y">SemiBin</a> (v1.3.1), <a href="https://zenodo.org/records/10460259">Rosella</a> (v0.4.2), <a href="https://www.nature.com/articles/nmeth.3103">CONCOCT</a> (v1.1.0) and <a href="https://academic.oup.com/bioinformatics/article/32/4/605/1744462">MaxBin2</a> (v2.2.7). Genomes were analyzed using <a href="https://www.nature.com/articles/s41592-023-01940-w">CheckM2</a> (v1.0.2) and clustered at 95% ANI using <a href="https://zenodo.org/records/10526086">Galah</a> (v0.4.0).</p> <p> </p> <p>FILES:</p> <ul> <li><strong>EMERGE_MAGs_2019_long-short-hybrid.tar.gz</strong> - Archive containing the MAG files (.fna).</li> <li><strong>metadata_MAGs_2019_EMERGE.tsv</strong> - Table containing source sample names and accessions, GTDB classifications, CheckM2 quality information, NCBI GenomeBatch- and MIMAG(6.0)-formatted attributes, and other metadata for the MAGs.</li> </ul> <p> </p> <p>FUNDING:</p> <p>This research is a contribution of the EMERGE Biology Integration Institute (<a href="https://emerge-bii.github.io/">https://emerge-bii.github.io/</a>), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.</p> <p>This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632. DE-SC0010580. and DE-SC0016440.</p> <p>We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.</p> <p>Data from the Joint Genome Institute (JGI) was collected under BER Support Science Proposal 503530 (DOI: <a href="https://doi.org/10.46936/10.25585/60001148">10.46936/10.25585/60001148</a>), conducted by the U.S. Department of Energy Joint Genome Institute (<a href="https://ror.org/04xm1d337">https://ror.org/04xm1d337</a>), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.</p>
Water Body Checklists 2019: Adriatic Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Adriatic Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Water Body Checklists 2019: Ceram Sea Species List
Species checklists created using effechecka and modified polygons from IHO. The polygons were reduced in resolution.<p></p>List of species collected from the Ceram Sea using effechecka and a modified polygon from the International Hydrographic Association. A filter was applied (based on data from WoRMS) to remove all non-marine taxa.
Shorelines, elevation transects and environmental data for Drew Point, Ak study area, 2019-2022
<p>This record consists of the shorelines and their derived products, elevation transects and environmental data used for the Cryosphere preprint: <em>Multiple modes of shoreline change along the Alaskan Beaufort Sea observed using ICESat-2 altimetry and satellite imagery</em>. There are three primary datasets:</p> <p><em>ERA-5 </em>contains hourly output ERA5 reanalysis dataset (Hersbach et al., 2020, https://doi.org/10.24381/cds.adbb2d47) from a single pixel (-153.83, 70.87) from May 1st through November 30th for 2019, 2020, 2021, and 2022. It also contians a list of derived open water days for each year.</p> <p><em>ICESat-2 </em>contains the subsetted ATL03 photons (Neuman et. al, 2023, https://doi.org/10.5067/ATLAS/ATL03.006) for RGT 137 ground tracks 1r, 2r and 3r on 07 April 2019, 04 January 2020, 02 July 2021, and 31 December 2021, elevation profiles derived from SlideRule (Shean et al., 2023), and shoreline boundaries picked from the elevation profiles and Planet imagery. It also includes cross-shore transects used to project ICESat-2-derived changes into the local shoreline-perpindicular direction.</p> <p><em>Planet_shorelines</em> contains shorelines drived from 3m multispectral imagery from Planet Labs (Planet Team, 2024) as well as their derivatiev products. This included shorelines used for year-to-year change (<em>annual_coastlines</em>) and for uncertainty analysis (<em>uncertainty_coastlines</em>). It also includes the derived year-to year-change <em>(shoreline_change),</em> the baseline and cross-shore transects used to derived these change estiamtes, and a table with our derived annual shoreline change estimates compared with previously published estimates in this region.</p> <p>More detailed information on each datasets can be found in each folder's README file.</p>
Monthly global ocean carbonyl sulfide and carbon disulfide flux data (2000–2019)
<p>This data product reports simulated monthly global ocean–atmosphere fluxes of carbonyl sulfide (OCS) and carbon disulfide (CS2) at 0.5° × 0.5° resolution (equivalent to 55 km × 55 km at the equator) between January 2000 and December 2019.</p> <p>Data are contained in two NetCDF files:</p> <ul> <li>ocs-flux-monthly-2000-to-2019.nc: Monthly global ocean OCS fluxes, 2000–2019</li> <li>cs2-flux-monthly-2000-to-2019.nc: Monthly global ocean CS2 fluxes, 2000–2019</li> </ul> <p>Data characteristics</p> <ul> <li>Version: 1.0.1 (2025-04-07)</li> <li>Spatial coverage: global</li> <li>Spatial resolution: 0.5° longitude × 0.5° latitude</li> <li>Temporal coverage: 2000-01-15 thru 2019-12-15 (nominal timestamps fall on the 15th day of each month)</li> <li>Temporal resolution: monthly</li> </ul> <p>Related manuscript</p> <p>Sun, W., Merder, J., Zhao, G., Lennartz, S. T., & Michalak, A. M. (2025). Tropical sources dominate the ocean carbonyl sulfide budget. Under consideration in <em>Global Biogeochemical Cycles.</em></p>
Dataset from Holding et al. (2019)––Seasonal and spatial patterns of primary production in a high latitude fjord
<p>Unprecedented melting of the Greenland Ice Sheet (GrIS) is impacting the coastal ocean, and its effects on fjord ecology remain understudied. It has been suggested that as glaciers retreat, primary production regimes may be altered, rendering fjords less productive. Here we present data from the paper Holding et al. (2019). Seasonal and spatial patterns of primary production in a high-latitude fjord affected by Greenland Ice Sheet run-off. <em>Biogeosciences</em>, <em>16</em>(19), 3777-3792, /doi.org/10.5194/bg-16-3777-2019. This paper investigates patterns of primary productivity in a northeast Greenland fjord (Young Sound, 74°N), which receives run-off from the GrIS via land-terminating glaciers. This dataset includes measures of size fractioned primary production and chlorophyll <em>a </em>biomass, as well as CTD data and biochemical parameters. Furthermore, primary production was measured using photosynthesis v. irradiance (PI) curves, thus PI curve parameters are also available. The data were taken during the ice-free season along a spatial gradient of meltwater influence. </p> <p>We thank Egon Frandsen, Kunuk Lennert, and Ivali Lennert for excellent assistance during fieldwork. This research has beensupported by the Danish Environmental Protection Agency’s programme for Arctic research (DANCEA) (grant no. MST-112-0023), The Carlsberg Foundation (grant no. 2013_01_0532), the Norwegian Research Council (Mi- croPolar) (grant no. RCN 225956), and the European Commission, H2020 Research Infrastructures (GrIS-Melt (grant no. 752325) and INTAROS (grant no. 727890)). </p>
Cambridge butterfly wing collection - Ecuador, August 2019
<p>EN: This upload contains photographs taken by Annalie Barker and Joana Meier at the University of Cambridge, from a butterfly wing collection from Ecuador (August 2019), in collaboration with Caroline Bacquet (IKIAM). Individual sample names can be found in the information sheet. Further Information on individual samples from the Butterfly Genetics Group Collection can be found on the public database Earthcape (<a href="https://heliconius.ecdb.io/">click here for the database</a>, and <a href="https://heliconius.zoo.cam.ac.uk/databases/earthcape-specimen-database/">here for FAQ</a>). Please contact Joana Meier (jm2276[at]cam.ac.uk) or Chris Jiggins (c.jiggins[at]zoo.cam.ac.uk) for further information.</p> <p> </p> <p>ES: Este repositorio contiene fotografías tomadas por Annalie Barker y Joana Meier en la Universidad de Cambridge, de mariposas de Ecuador (Agosto 2019), en colaboración con Caroline Bacquet (IKIAM, Ecuador). Puede encontrar información sobre muestras individuales de Butterfly Genetics Group Collection en la base de datos pública Earthcape (<a href="https://heliconius.ecdb.io/">haga clic aquí para la base de datos</a>, y <a href="https://heliconius.zoo.cam.ac.uk/databases/earthcape-specimen-database/">aquí para preguntas frecuentes</a>) Por favor, póngase en contacto con Joana Meier (jm2276 [arroba] cam.ac.uk) or Chris Jiggins (c.jiggins [arroba] zoo.cam.ac.uk) con sus preguntas o peticiones.</p>
45 Vulnerability Discoverability Timelines from the 2019 Collegiate Penetration Testing Competition
<p><em><strong>Description</strong></em></p> <p>This is a collection of manually curated timelines from the 2019 Collegiate Penetration Testing Competition (CPTC). Collection and annotation are described in detail in this publication:</p> <ul> <li>Benjamin S. Meyers, Sultan Fahad Almassari, Brandon N. Keller, and Andrew Meneely. Examining Penetration Tester Behavior in the Collegiate Penetration Testing Competition. Forthcoming at Transactions on Software Engineering and Methodology. https://dl.acm.org/doi/10.1145/3514040</li> </ul> <p><em><strong>Included Files</strong></em></p> <ul> <li><strong><em>2019_cptc_timelines.csv</em>:</strong> Completed timelines for ten teams from the 2019 CPTC nationals competition.</li> <li><strong><em>2019_cptc_timeline_columns.csv</em>:</strong> Descriptions of the columns in <strong><em>2019_cptc_</em></strong><em><strong>timelines.csv</strong></em>.</li> <li><strong><em>2019_cptc_vulnerabilities.csv</em>:</strong> Brief vulnerability descriptions and CWE mappings.</li> </ul> <p><em><strong>Other Resources</strong></em></p> <ul> <li>Complete Splunk log data dumps are available <a href="http://mirrors.rit.edu/cptc/2019/mirrors/nationals/">here</a>. These must be ingested and viewed with a Splunk instance.</li> <li>To request access to the CPTC team reports, please contact Brock Wagehoft (<a href="mailto:bew1127@rit.edu">email</a>).</li> </ul> <p><em><strong>Contact</strong></em></p> <p>Please contact Benjamin S. Meyers (<a href="mailto:bsm9339@rit.edu">email</a>) with questions about this data and its collection.</p> <p><em><strong>Acknowledgments</strong></em></p> <p>Collection of this data has been sponsored in part by the National Science Foundation grant 1922169, and by a Department of Defense DARPA SBIR program (grant 140D63-19-C-0018).</p>
Alien CSI Akrotiri Bioblitz 2019
<p>The Akrotiri Bioblitz in Cyprus was a one-off event held as part of the Alien-CSI COST Action (CA17122). It took place for 24 hours between Wednesday, February 27 and Thursday, February 28, 2019. The aim of this bioblitz was to improve knowledge of the biodiversity of the Akrotiri Peninsula, identify potential risks to the biodiversity caused by invasive species and trial methods that could be used throughout Europe for this purpose. More information about the event can be found on these pages: <a href="https://osf.io/csvgz/wiki/home/">https://osf.io/csvgz/wiki/home/</a>. </p> <p> </p> <p>All bioblitz records can be found in the iNaturalist project <a href="https://www.inaturalist.org/projects/akrotiri-bioblitz-cyprus">akrotiri-bioblitz-cyprus</a> and in the GBIF dataset (Hadjikyriakou et al. 2019). At the time of writing, 396 species had obtained “research grade” status on iNaturalist and included also captive and cultivated organisms. These species were assigned native/alien status, a level of establishment using the categories described by Groom et al. (2019a) and their occurrence in Akrotiri and Cyprus was determined based on the Flora of Cyprus by Hand et al. (2021), the Cyprus Database of Alien Species (CyDAS) by Martinou et al. (2020), the Amphibians and Reptiles of Cyprus by Baier et al. (2013), Fauna Europaea (<a href="https://fauna-eu.org/">https://fauna-eu.org</a>), Sparrow and John (2016), Ridout (1983), Peterson et al. (2019), Guerrini et al. (2007), Englezou et al. (2018), Nahum et al. (2010), Christia et al. (2011), Jørgensen and Sørensen (2008), Can and Arap (2005), Haouas gharsallah et al. (2010), Flint (1997), Bouaziz-Yahiatene et al. (2017), Zogaris et al. (2012), Gözüaçık and Atay (2016), Groom et al. (2019b), Savvides et al. (2015) and Litterski & Mayrhofer (1998). In addition, we compared our bioblitz species checklist with the species present in the occurrence dataset on GBIF preceding the bioblitz (GBIF 2019). </p> <p>An overview of the establishment status of the recorded species during the Akrotiri bioblitz can be found in the csv file and summary table (pdf) included in this upload.</p>
How does moving Public Engagement with Research Online Change Audience Diversity? Comparing Inclusion Indicators for 2019 & 2020 European Researchers' Night events
<p>Taking place annually in more than 400 cities, European Researchers’ Night is a pan- European synchronized event that aims to bring researchers closer to the public. In this paper audience profiles are compared from events in 2019 and 2020. In 2019, face-to-face events reached an estimated 1.6 million attendees, while in 2020, events shifted online due to the COVID-19 pandemic and reached an estimated 2.3 million attendees. Focusing on social inclusion metrics, survey data is analyzed across two national contexts (Ireland and Malta) in 2019 (n=656) and 2020 (n=506). The results from this exploratory, descriptive study shed light on how moving public engagement with research online shifted audience profiles. Based on prior research about the digital divide in access and use of online media, hypotheses were proposed that online European Researchers’ Night events would attract audiences with higher educational attainment levels and greater self-reported, subjective economic well-being. While changes were observed from 2019 to 2020, results for each hypothesis show a mixed picture. The first hypothesis was upheld for the highest education levels but failed for the lowest levels suggesting that the pivot to online events simultaneously attracted participants with no formal education and those with postgraduate qualifications, while attracting less of those with undergraduate or lower levels of education. The second hypothesis was not upheld, with online European Researchers’ Night events attracting audiences with slightly higher levels of economic well-being compared to face-to-face events. The findings of this study indicate that European Researchers’ Night events present a clear opportunity to measure the effects of the digital divide in relation to public engagement with research across Europe.</p>
High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019
<p><strong>Dataset overview</strong></p> <p>This dataset contain ship-based, high-resolution (underway) estimates of mixed layer net community production (NCP) and ancillary data from three summertime cruises in the Central and Eastern North American Arctic in 2015, 2018 and 2019. NCP estimates were derived from underway O2/Ar observations, obtained using ship-based membrane inlet mass spectrometry. Ancillary data include geospatial information (time, location), surface and depth-resolved hydrography and biogeochemical observations, and select output from a simulation of an oceanographic circulation model, based on the NEMO framework.</p> <p>Please cite as:</p> <p>Izett, R. and Tortell, P. 2021. High-resolution, mixed layer NCP estimates and ancillary data from the Central and Eastern North American Arctic: 2015, 2018, 2019 (Dataset). Zenodo. https://doi.org/https://doi.org/10.5281/zenodo.5593381.</p> <p>This dataset is supplement to:</p> <p>Izett, R. W., Castro de la Guardia, L., Chanona, M., Myers, P. G., Waterman, S, and Tortell, P. D. Impact of vertical mixing on summertime net community production in Canadian Arctic and Subarctic waters: Insights from in situ measurements and numerical simulations. </p> <p> </p> <p><strong>Abstract</strong></p> <p>We present ΔO<sub>2</sub>/Ar-based estimates of mixed layer net community production (NCP) from three summer cruises in the North American Arctic and Subarctic oceans. Coupling shipboard underway and discrete observations with output from an ocean circulation model, we correct the NCP estimates for vertical mixing fluxes impacting the surface O<sub>2</sub> budget. Large positive mixing fluxes, exceeding 100 mmol O<sub>2</sub> m<sup>-2</sup> d<sup>-1</sup>, were derived in regions of strong wind-driven mixing, such as the Labrador Sea, and in the physically-dynamic Canadian Arctic Archipelago. In contrast, flux corrections were small (<10 mmol O<sub>2</sub> m<sup>-2 </sup>d<sup>-1</sup>, on average) in the density-stratified Baffin Bay, where mixing was low, and parts of the well-mixed Hudson Strait, where vertical O<sub>2</sub> gradients were weak. The distribution of corrected NCP was highly heterogenous across the study region, reflecting varying contributions of nutrient supply, freshwater input and sea ice dynamics. Elevated NCP was apparent in the Labrador Sea, Hudson Strait, and nearshore regions influenced by glacial meltwater and recent ice retreat. Low NCP and localized net heterotrophy occurred in Baffin Bay, and near strong freshwater and organic matter sources in Hudson Bay and the Queen Maud Gulf. A multiple linear regression model developed using available oceanographic data explained ~58 % of the observed NCP variability. Our work demonstrates the spatially explicit influence of vertical mixing on ΔO<sub>2</sub>/Ar-based NCP calculations across varied hydrographic conditions, and presents a novel approach to account for this process. This study contributes new knowledge of biological productivity distributions in under-sampled, rapidly changing, high-latitude waters.</p> <p> </p> <p><strong>Lay summary</strong></p> <p>Net community production (NCP; i.e., net organic matter production) constrains the ocean’s ability to support marine ecosystems and remove carbon dioxide from the atmosphere. A common approach to estimating NCP involves measurements of upper ocean oxygen (O<sub>2</sub>) concentrations. However, while vertical mixing may be a significant component of the surface water O<sub>2</sub> budget in some regions, applications of this approach typically do not quantify the magnitude of this flux, which can lead to potentially inaccurate NCP estimates. In this paper, we introduce a method combining ship-based measurements and the output from an ocean circulation model to refine NCP calculations for vertical mixing effects in North American Arctic and Subarctic oceans. The dataset reveals high NCP in the Labrador Sea (Inuktitut: <em>Lâbradorip Imappinga</em>), North Atlantic, Hudson Strait (<em>Ikirasarjuaq</em>) and northern Canadian Arctic Archipelago (CAA), and low values in Baffin Bay (<em>Saknirutiak Imanga</em>) and southern CAA. Riverine freshwater input to Hudson Bay (<em>Tasiujarjuar</em>) and the Queen Maud Gulf (<em>Ugjulik</em>) can reduce local NCP, while glacial meltwater may stimulate NCP elsewhere. Overall, this work provides a new NCP dataset in an under-sampled region. Similar studies will be necessary to document changes in biological productivity in response to changing environmental conditions in polar waters.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This work was supported by the ArcticNet and MEOPAR Networks of Centres of Excellence Canada, Polar Knowledge Canada, the Natural Sciences and Engineering Research Council of Canada (NSERC) and Compute Canada.</p> <p>Hydrography and ancillary oceanographic data were provided by the Amundsen Science group of Université Laval.</p> <p>The model simulation was run by P. Myers (University of Alberta).</p> <p>The underway gas data were collected by R. Izett & P. Tortell (University of British Columbia)</p> <p>All data were archived by R. Izett. </p> <p> </p> <p><strong>Files and variables:</strong></p> <p>data_yyyy (data provided in NetCDF and Matlab format; "yyyy" denotes sampling year): Ship-board observations and derived quantities.</p> <table> <tbody> <tr> <td><em>Variable</em></td> <td><em>Description </em></td> <td><em>Unit</em></td> </tr> <tr> <td>time</td> <td>UTC YYYY Julian Day (year-day since YYYY-01-01)</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Decimal degrees E</td> </tr> <tr> <td>dist</td> <td>Along-track distance</td> <td>km</td> </tr> <tr> <td>reg_index</td> <td>Regional index</td> <td> </td> </tr> <tr> <td>region_mask_lat</td> <td>Latitude for region indices mask</td> <td>Decimal degrees N</td> </tr> <tr> <td>region_mask_long</td> <td>Longitude for region indices mask</td> <td>Decimal degrees E</td> </tr> <tr> <td>region_mask</td> <td>Regional masks</td> <td> </td> </tr> <tr> <td>sst</td> <td>Sea surface temperature measured in the instrument laboratory</td> <td>deg. C</td> </tr> <tr> <td>sal</td> <td>Sea surface salinity measured in the instrument laboratory</td> <td> </td> </tr> <tr> <td>chl_fluor</td> <td>Calibrated mixed layer Chl a fluorescence in the instrument laboratory</td> <td>(mg Chl a)/m3</td> </tr> <tr> <td>do2ar</td> <td>Biological O2 saturation anomaly, deltaO2/Ar</td> <td>%</td> </tr> <tr> <td>kwo2</td> <td>Weighted O2 gas transfer velocity</td> <td>m/d</td> </tr> <tr> <td>bioflux_ncp</td> <td>Bioflux-NCP</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>cor_ncp</td> <td>corrected-NCP</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>uw_kz</td> <td>Underway model-based eddy diffusivity at the base of the mixed layer</td> <td>m2/s</td> </tr> <tr> <td>bling_ncp</td> <td>BLING model-based mixed layer NCP, matched to underway cruise time/position</td> <td>mmol O2/m2/d</td> </tr> <tr> <td>prof_time</td> <td>UTC 2015 Julian Day (year-day since 2015-01-01) at CTD profile stations</td> <td>UTC Days</td> </tr> <tr> <td>prof_lat</td> <td>Latitude N at CTD profile stations</td> <td>Decimal degrees N</td> </tr> <tr> <td>prof_long</td> <td>Longitude E at CTD profile stations</td> <td>Decimal degrees E</td> </tr> <tr> <td>prof_dist</td> <td>Along-track distance at CTD profile stations</td> <td>km</td> </tr> <tr> <td>prof_reg_index</td> <td>Regional index at CTD profile stations</td> <td> </td> </tr> <tr> <td>prof_do2bdz</td> <td>Subsurface O2b gradient, dO2B/dZ at CTD profile stations</td> <td>mmol O2/m4</td> </tr> <tr> <td>prof_mld</td> <td>Mixed layer depth, calculated at CTD profile stations</td> <td>m</td> </tr> <tr> <td>prof_pycnocline_dep</td> <td>Pycnocline depth, calculated at CTD profile stations</td> <td>m</td> </tr> </tbody> </table> <p> </p> <p>nemo_yyyy (data provided in NetCDF format only; "yyyy" denotes sampling year): 4-dimensional gridded NEMO model output. </p> <table> <tbody> <tr> <td>Varaible</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>Model time, UTC YYYY Julian Day (year-day since YYYY-01-01)</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Model Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Model Decimal degrees W</td> </tr> <tr> <td>depth_grid_kz</td> <td>kz depth</td> <td>m</td> </tr> <tr> <td>depth_grid</td> <td>depth</td> <td>m</td> </tr> <tr> <td>kz</td> <td>Eddy diffusivity coefficient</td> <td>m2/s</td> </tr> <tr> <td>T</td> <td>Temperature</td> <td>deg-C</td> </tr> <tr> <td>sal</td> <td>Salinity</td> <td> </td> </tr> <tr> <td>oxy</td> <td>Oxygen concentration</td> <td>mol O2/m3</td> </tr> </tbody> </table> <p> </p> <p>ArcticNet2003-2014_derived_quantities (data provided in NetCDF format only): Derived quantities from ArcticNet sampling.</p> <table> <tbody> <tr> <td>Varaible</td> <td>Description </td> <td>Unit</td> </tr> <tr> <td>time</td> <td>UTC; Days since 2010-01-01</td> <td>UTC Days</td> </tr> <tr> <td>lat</td> <td>Latitude N</td> <td>Decimal degrees N</td> </tr> <tr> <td>long</td> <td>Longitude E</td> <td>Decimal degrees E</td> </tr> <tr> <td>reg_index</td> <td>Regional index</td> <td> </td> </tr> <tr> <td>prof_do2bdz</td> <td>Subsurface O2b gradient, dO2B/dZ at CTD profile stations</td> <td>mmol O2/m4</td> </tr> <tr> <td>prof_mld</td> <td>Mixed layer depth, calculated at CTD profile stations</td> <td>m</td> </tr> <tr> <td>prof_pycnocline_dep</td> <td>Pycnocline depth, calculated at CTD profile stations</td> <td>m</td> </tr> </tbody> </table> <p> </p> <p> </p>
2019 search and interaction log from the data catalogue: Research Data Australia
<p>In order to provide a better support to user's data discovery activity, we analysed a data search log in order to understand how data seekers interact with a data search system when they search for data. The data search log is from the research data discovery portal: <a href="https://researchdata.edu.au">Research Data Australia (RDA)</a>. RDA is the data discovery service of the Australian Research Data Commons (ARDC). ARDC is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program.</p> <p>Please read the research paper "<a href="https://doi.org/10.1108/JD-12-2021-0245">Large-scale Analysis of Query Logs to Profile Users for Dataset Search</a>" for detailed description and analysis of the datasets, and the software "<a href="https://zenodo.org/record/6321621#.Yh79Tt9xUmA">Python code for processing and clustering a data search log</a>" for the data process and analysis.</p> <p>The search log consists of the entire user-front activity log data for the duration of January to December 2019. During this period, the catalogue contained about 150,000 metadata records of datasets.</p> <p>The dataset (2019_search_log_sessioned.txt) was generated from raw log data with following steps:</p> <ul> <li>Remove entries that were likely from machines instead of human users. Those recorded machine activities may result from downstream aggregators who harvested metadata from RDA by directly sending queries to the catalogue URL instead of using the API endpoint.</li> <li>Identify search sessions from a user - a search session includes all activities a user conducts with a search system in order to satisfy a (information/data) search needs. We followed the following steps to identify search sessions. First, we identified a user by IP address, where a unique IP address was considered a single user. We recognise the limitation of this approach, as several users may share the same IP address, however the IP address is the only information available for identifying a user. <br> Past research in log analysis usually apply the following two methods to identify a session: 30 minutes from the same IP address, and/or more than 30 minutes of inactivity between the current activity event and its immediate preceding event. We examined both methods carefully for our log data and concluded that both ended with large unwanted sessions from machine activities. Therefore, we take a brutal approach, by taking only a session from an IP address with a maximum 30 minutes duration.</li> <li>We also removed sessions whose 40% of activities resulted in ’page not found’ or whose activities were all about accessing grants. Within a session, we removed "duplicated" activities that were exactly as their precedent activity with less than one second time span (this could have been a result of reloading a page).</li> </ul> <p>The dataset (id_to_title_subject.csv) lists title and subject headings per record id.</p> <p> </p>
ScienceDex guides
Understand access before you commit
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.