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Flow-topography interactions in the western tropical Atlantic boundary off Northeast Brazil.
<p>Figures and other media files of the paper Flow-topography interactions in the western tropical Atlantic boundary 2off Northeast Brazil.</p>
Geographical Distribution Maps of Western Palearctic Weevil Taxa
<p>The electronic supplement belongs to the research article Schütte A, Stüben PE, Astrin JJ (2023) Molecular Weevil Identification Project: A Thoroughly Curated Barcode Release of 1300 Western Palearctic Weevil Species (Coleoptera: Curculionoidea) - Biodiversity Data Journal 11.</p> <p>The ZIP file contains 613 distribution maps from Western Palearctic weevil taxa. The distribution maps showing Europe originate from the Curculio Institute's website (www.curci.de). Additional information on distribution range and known synonyms were based on the information from the Löbl catalogs (Löbl & Smetana 2011, Löbl & Smetana 2013). The maximum distribution range of each species was measured in km with Google Earth's ruler function.</p> <p>An unzip software is needed to access the *.JPG files within the *.ZIP file. Microsoft operating systems support *.zip files natively since Windows XP. MAC operating systems offer the "archive utility" to access *.zip files. Android users must install an app like Winzip, WinRAR, or 7ZIP. The iOS 13 operating system and onwards allow unzipping *.zip archives natively (iPhone and iPad). The *.zip filetype support can be installed on Linux operating systems via the terminal command: "sudo apt-get install unzip". Command to unzip: "unzip \*.zip". The *.JPG files can be opened with any picture viewer or internet browser.</p> <p>References<br> Löbl L, Smetana A (2011) Catalogue of the Coleoptera. Vol. 7, Curculionoiodea I, Stenstrup, Apollo Books, 373 pp.<br> Löbl L, Smetana A (2013) Catalogue of the Coleoptera. Vol. 8, Curculionoiodea II, Leiden & Boston, Brill, 700 pp.</p>
The URW-KG: a Resource for Tackling the Under-Representation of non-Western Writers
<p>Digital media have enabled the access to an unprecedented literary knowledge. Authors, readers, and scholars are now able to discover and share an increasing amount of information about books and their authors. Notwithstanding, digital archives are still unbalanced: writers from non-Western countries are less represented, and such a condition leads to the perpetration of old forms of discrimination. In this paper, we present the Under-Represented Writers Knowledge Graph (URW-KG), a resource designed to explore and possibly amend this lack of representation by gathering and mapping information about works and authors from Wikidata and three other sources: Open Library, Goodreads, and Google Books. The experiments based on KG embeddings showed that the integrated information encoded in the graph allows scholars and users to be more easily exposed to non-Western literary works and authors with respect to Wikidata alone. This opens to the development of fairer and effective tools for author discovery and exploration.<br> </p>
Dataset for: Bedding scale correlation on Mars in western Arabia Terra
<p>Dataset for: Bedding scale correlation on Mars in western Arabia Terra</p> <p>A.M. Annex et al.</p> <p>Data Product Overview</p> <p>This repository contains all source data for the publication. Below is a description of each general data product type, software that can load the data, and a list of the file names along with the short description of the data product.</p> <p><strong>HiRISE Digital Elevation Models (DEMs).</strong></p> <p>HiRISE DEMs produced using the Ames Stereo Pipeline are in geotiff format ending with ‘*X_0_DEM-adj.tif’, the “X” prefix denotes the spatial resolution of the data product in meters. Geotiff files are able to be read by free GIS software like QGIS.</p> <p><strong>HiRISE map-projected imagery (DRGs).</strong></p> <p>Map-projected HiRISE images produced using the Ames Stereo Pipeline are in geotiff format ending with ‘*0_Y_DRG-cog.tif’, the “Y” prefix denotes the spatial resolution of the data product in centimeters. Geotiff files are able to be read by free GIS software like QGIS. The DRG files are formatted as COG-geotiffs for enhanced compression and ease of use.</p> <p><strong>3D Topography files (.ply).</strong></p> <p>Traingular Mesh versions of the HiRISE/CTX topography data used for 3D figures in “.ply” format. Meshes are greatly geometrically simplified from source files. Topography files can be loaded in a variety of open source tools like ParaView and Meshlab. Textures can be applied using embedded texture coordinates.</p> <p><strong>3D Geological Model outputs (.vtk)</strong></p> <p>VTK 3D file format files of model output over the spatial domain of each study site. VTK files can be loaded by ParaView open source software. The “block” files contain the model evaluation over a regular grid over the model extent. The “surfaces” files contain just the bedding surfaces as interpolated from the “block” files using the marching cubes algorithm.</p> <p><strong>Geological Model geologic maps (geologic_map.tif).</strong></p> <p>Geologic maps from geological models are standard geotiffs readable by conventional GIS software. The maximum value for each geologic map is the “no-data” value for the map. Geologic maps are calculated at a lower resolution than the topography data for storage efficiency.</p> <p><strong>Beds Geopackage File (.gpkg).</strong></p> <p>Geopackage vector data file containing all mapped layers and associated metadata including dip corrected bed thickness as well as WKB encoded 3D linestrings representing the sampled topography data to which the bedding orientations were fit. Geopackage files can be read using GIS software like QGIS and ArcGIS as well as the OGR/GDAL suite. A full description of each column in the file is provided below.</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>uuid</td> <td>String</td> <td>unique identifier</td> </tr> <tr> <td>stratum_order</td> <td>Real</td> <td>0-indexed bed order</td> </tr> <tr> <td>section</td> <td>Real</td> <td>section number</td> </tr> <tr> <td>layer_id</td> <td>Real</td> <td>bed number/index</td> </tr> <tr> <td>layer_id_bk</td> <td>Real</td> <td>unused backup bed number/index</td> </tr> <tr> <td>source_raster</td> <td>String</td> <td>dem file path used</td> </tr> <tr> <td>raster</td> <td>String</td> <td>dem file name</td> </tr> <tr> <td>gsd</td> <td>Real</td> <td>ground sampling distant for dem</td> </tr> <tr> <td>wkn</td> <td>String</td> <td>well known name for dem</td> </tr> <tr> <td>rtype</td> <td>String</td> <td>raster type</td> </tr> <tr> <td>minx</td> <td>Real</td> <td>minimum x position of trace in dem crs</td> </tr> <tr> <td>miny</td> <td>Real</td> <td>minimum y position of trace in dem crs</td> </tr> <tr> <td>maxx</td> <td>Real</td> <td>maximum x position of trace in dem crs</td> </tr> <tr> <td>maxy</td> <td>Real</td> <td>maximum y position of trace in dem crs</td> </tr> <tr> <td>method</td> <td>String</td> <td>internal interpolation method</td> </tr> <tr> <td>sl</td> <td>Real</td> <td>slope in degrees</td> </tr> <tr> <td>az</td> <td>Real</td> <td>azimuth in degrees</td> </tr> <tr> <td>error</td> <td>Real</td> <td>maximum error ellipse angle</td> </tr> <tr> <td>stdr</td> <td>Real</td> <td>standard deviation of the residuals</td> </tr> <tr> <td>semr</td> <td>Real</td> <td>standard error of the residuals</td> </tr> <tr> <td>X</td> <td>Real</td> <td>mean x position in CRS</td> </tr> <tr> <td>Y</td> <td>Real</td> <td>mean y position in CRS</td> </tr> <tr> <td>Z</td> <td>Real</td> <td>mean z position in CRS</td> </tr> <tr> <td>b1</td> <td>Real</td> <td>plane coefficient 1</td> </tr> <tr> <td>b2</td> <td>Real</td> <td>plane coefficient 2</td> </tr> <tr> <td>b3</td> <td>Real</td> <td>plane coefficient 3</td> </tr> <tr> <td>b1_se</td> <td>Real</td> <td>standard error plane coefficient 1</td> </tr> <tr> <td>b2_se</td> <td>Real</td> <td>standard error plane coefficient 2</td> </tr> <tr> <td>b3_se</td> <td>Real</td> <td>standard error plane coefficient 3</td> </tr> <tr> <td>b1_ci_low</td> <td>Real</td> <td>plane coefficient 1 95% confidence interval low</td> </tr> <tr> <td>b1_ci_high</td> <td>Real</td> <td>plane coefficient 1 95% confidence interval high</td> </tr> <tr> <td>b2_ci_low</td> <td>Real</td> <td>plane coefficient 2 95% confidence interval low</td> </tr> <tr> <td>b2_ci_high</td> <td>Real</td> <td>plane coefficient 2 95% confidence interval high</td> </tr> <tr> <td>b3_ci_low</td> <td>Real</td> <td>plane coefficient 3 95% confidence interval low</td> </tr> <tr> <td>b3_ci_high</td> <td>Real</td> <td>plane coefficient 3 95% confidence interval high</td> </tr> <tr> <td>pca_ev_1</td> <td>Real</td> <td>pca explained variance ratio pc 1</td> </tr> <tr> <td>pca_ev_2</td> <td>Real</td> <td>pca explained variance ratio pc 2</td> </tr> <tr> <td>pca_ev_3</td> <td>Real</td> <td>pca explained variance ratio pc 3</td> </tr> <tr> <td>condition_number</td> <td>Real</td> <td>condition number for regression</td> </tr> <tr> <td>n</td> <td>Integer64</td> <td>number of data points used in regression</td> </tr> <tr> <td>rls</td> <td>Integer(Boolean)</td> <td>unused flag</td> </tr> <tr> <td>demeaned_regressions</td> <td>Integer(Boolean)</td> <td>centering indicator</td> </tr> <tr> <td>meansl</td> <td>Real</td> <td>mean section slope</td> </tr> <tr> <td>meanaz</td> <td>Real</td> <td>mean section azimuth</td> </tr> <tr> <td>angular_error</td> <td>Real</td> <td>angular error for section</td> </tr> <tr> <td>mB_1</td> <td>Real</td> <td>mean plane coefficient 1 for section</td> </tr> <tr> <td>mB_2</td> <td>Real</td> <td>mean plane coefficient 2 for section</td> </tr> <tr> <td>mB_3</td> <td>Real</td> <td>mean plane coefficient 3 for section</td> </tr> <tr> <td>R</td> <td>Real</td> <td>mean plane normal orientation vector magnitude</td> </tr> <tr> <td>num_valid</td> <td>Integer64</td> <td>number of valid planes in section</td> </tr> <tr> <td>meanc</td> <td>Real</td> <td>mean stratigraphic position</td> </tr> <tr> <td>medianc</td> <td>Real</td> <td>median stratigraphic position</td> </tr> <tr> <td>stdc</td> <td>Real</td> <td>standard deviation of stratigraphic index</td> </tr> <tr> <td>stec</td> <td>Real</td> <td>standard error of stratigraphic index</td> </tr> <tr> <td>was_monotonic_increasing_layer_id</td> <td>Integer(Boolean)</td> <td>monotonic layer_id after projection to stratigraphic index</td> </tr> <tr> <td>was_monotonic_increasing_meanc</td> <td>Integer(Boolean)</td> <td>monotonic meanc after projection to stratigraphic index</td> </tr> <tr> <td>was_monotonic_increasing_z</td> <td>Integer(Boolean)</td> <td>monotonic z increasing after projection to stratigraphic index</td> </tr> <tr> <td>meanc_l3sigma_std</td> <td>Real</td> <td>lower 3-sigma meanc standard deviation</td> </tr> <tr> <td>meanc_u3sigma_std</td> <td>Real</td> <td>upper 3-sigma meanc standard deviation</td> </tr> <tr> <td>meanc_l2sigma_sem</td> <td>Real</td> <td>lower 3-sigma meanc standard error</td> </tr> <tr> <td>meanc_u2sigma_sem</td> <td>Real</td> <td>upper 3-sigma meanc standard error</td> </tr> <tr> <td>thickness</td> <td>Real</td> <td>difference in meanc</td> </tr> <tr> <td>thickness_fromz</td> <td>Real</td> <td>difference in Z value</td> </tr> <tr> <td>dip_cor</td> <td>Real</td> <td>dip correction</td> </tr> <tr> <td>dc_thick</td> <td>Real</td> <td>thickness after dip correction</td> </tr> <tr> <td>dc_thick_fromz</td> <td>Real</td> <td>z thickness after dip correction</td> </tr> <tr> <td>dc_thick_dev</td> <td>Integer(Boolean)</td> <td>dc_thick <= total mean dc_thick</td> </tr> <tr> <td>dc_thick_fromz_dev</td> <td>Integer(Boolean)</td> <td>dc_thick <= total mean dc_thick_fromz</td> </tr> <tr> <td>thickness_fromz_dev</td> <td>Integer(Boolean)</td> <td>dc_thick <= total mean thickness_fromz</td> </tr> <tr> <td>dc_thick_dev_bg</td> <td>Integer(Boolean)</td> <td>dc_thick <= section mean dc_thick</td> </tr> <tr> <td>dc_thick_fromz_dev_bg</td> <td>Integer(Boolean)</td> <td>dc_thick <= section mean dc_thick_fromz</td> </tr> <tr> <td>thickness_fromz_dev_bg</td> <td>Integer(Boolean)</td> <td>dc_thick <= section mean thickness_fromz</td> </tr> <tr> <td>slr</td> <td>Real</td> <td>slope in radians</td> </tr> <tr> <td>azr</td> <td>Real</td> <td>azimuth in radians</td> </tr> <tr> <td>meanslr</td> <td>Real</td> <td>mean slope in radians</td> </tr> <tr> <td>meanazr</td> <td>Real</td> <td>mean azimuth in radians</td> </tr> <tr> <td>angular_error_r</td> <td>Real</td> <td>angular error of section in radians</td> </tr> <tr> <td>pca_ev_1_ok</td> <td>Integer(Boolean)</td> <td>pca_ev_1 < 99.5%</td> </tr> <tr> <td>pca_ev_2_3_ratio</td> <td>Real</td> <td>pca_ev_2/pca_ev_3</td> </tr> <tr> <td>pca_ev_2_3_ratio_ok</td> <td>Integer(Boolean)</td> <td>pca_ev_2_3_ratio > 15</td> </tr> <tr> <td>xyz_wkb_hex</td> <td>String</td> <td>hex encoded wkb geometry for all points used in regression</td> </tr> </tbody> </table> <p><strong>Geological Model input files (.gpkg).</strong></p> <p>Four geopackage (.gpkg) files represent the input dataset for the geological models, one per study site as specified in the name of the file. The files contain most of the columns described above in the Beds geopackage file, with the following additional columns. The final seven columns (azimuth, dip, polarity, formation, X, Y, Z) constituting the actual parameters used by the geological model (GemPy). </p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>azimuth_mean</td> <td>String</td> <td>Mean section dip azimuth </td> </tr> <tr> <td>azimuth_indi</td> <td>Real</td> <td>Individual bed azimuth</td> </tr> <tr> <td>azimuth</td> <td>Real</td> <td>Azimuth of trace used by the geological model</td> </tr> <tr> <td>dip</td> <td>Real</td> <td>Dip for the trace used by the geological mode</td> </tr> <tr> <td>polarity</td> <td>Real</td> <td>Polarity of the dip vector normal vector </td> </tr> <tr> <td>formation</td> <td>String</td> <td>String representation of layer_id required for GemPy models</td> </tr> <tr> <td>X</td> <td>Real</td> <td>X position in the CRS of the sampled point on the trace</td> </tr> <tr> <td>Y</td> <td>Real</td> <td>Y position in the CRS of the sampled point on the trace</td> </tr> <tr> <td>Z</td> <td>Real</td> <td>Z position in the CRS of the sampled point on the trace</td> </tr> </tbody> </table> <p><strong>Stratigraphic Column Files (.gpkg).</strong></p> <p>Stratigraphic columns computed from the Geological Models come in three kinds of Geopackage vector files indicated by the postfixes <code>_sc</code>, <code>rbsc</code>, and <code>rbssc</code>. File names include the wkn site name.</p> <p><strong>sc (_sc.gpkg).</strong></p> <p>Geopackage vector data file containing measured bed thicknesses from Geological Model joined with corresponding Beds Geopackage file, subsetted partially. The columns largely overlap with the the list above for the Beds Geopackage but with the following additions</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>X</td> <td>Real</td> <td>X position of thickness measurement</td> </tr> <tr> <td>Y</td> <td>Real</td> <td>Y position of thickness measurement</td> </tr> <tr> <td>Z</td> <td>Real</td> <td>Z position of thickness measurement</td> </tr> <tr> <td>formation</td> <td>String</td> <td>Model required string representation of bed index</td> </tr> <tr> <td>bed thickness (m)</td> <td>Real</td> <td>difference of bed elevations</td> </tr> <tr> <td>azimuths</td> <td>Real</td> <td>azimuth as measured from model in degrees</td> </tr> <tr> <td>dip_degrees</td> <td>Real</td> <td>dip as measured from model in degrees</td> </tr> <tr> <td>Dip corrected bed thickness (m)</td> <td>Real</td> <td>dip corrected bed thickness in meters</td> </tr> <tr> <td>lower_point</td> <td>Real</td> <td>lower bed elevation in meters</td> </tr> <tr> <td>upper_point</td> <td>Real</td> <td>upper bed elevation in meters</td> </tr> <tr> <td>_formation</td> <td>Real</td> <td>integer number of formation string</td> </tr> <tr> <td>layer_iid</td> <td>Integer64</td> <td>integer number of layer_id</td> </tr> <tr> <td>bascom_baryte_diff_bt</td> <td>Real</td> <td>diff. in thickness from geomodel measurements</td> </tr> <tr> <td>bascom_baryte_diff_dcbt</td> <td>Real</td> <td>diff. in dip cor. thicknesses ’’</td> </tr> </tbody> </table> <p><strong>rbsc (rbsc.gpkg)</strong></p> <p>Geopackage vector file containing virtual boreholes with high resolution vertical sampling placed in a regular grid in the spatial extent of the DEM with the following columns.</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>formation</td> <td>String</td> <td>Model required string representation of bed index</td> </tr> <tr> <td>pred_z</td> <td>Real</td> <td>Z value of bedding plane</td> </tr> <tr> <td>layer_id</td> <td>Integer64</td> <td>Bed Index from Beds Geopackage file</td> </tr> <tr> <td>section</td> <td>Real</td> <td>section number</td> </tr> <tr> <td>thickness</td> <td>Real</td> <td>thickness of the layer predicted by model</td> </tr> <tr> <td>geom</td> <td>Point</td> <td>contains X,Y,Z of point in dem CRS</td> </tr> </tbody> </table> <p><strong>rbssc (rbssc.gpkg)</strong></p> <p>Geopackage vector file containing virtual boreholes with high resolution vertical sampling placed at the centroids for each section within the spatial extent of the DEM with the following columns.</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>formation</td> <td>String</td> <td>Model required string representation of bed index</td> </tr> <tr> <td>pred_z</td> <td>Real</td> <td>Z value of bedding plane</td> </tr> <tr> <td>layer_id</td> <td>Integer64</td> <td>Bed Index from Beds Geopackage file</td> </tr> <tr> <td>section</td> <td>Real</td> <td>section number</td> </tr> <tr> <td>thickness</td> <td>Real</td> <td>thickness of the layer predicted by model</td> </tr> <tr> <td>geom</td> <td>Point</td> <td>contains X,Y,Z of point in dem CRS</td> </tr> </tbody> </table> <p><strong>crescent_shapes.gpkg</strong></p> <p>Geopackage vector file containing the measurements of the crescent features.</p> <table> <thead> <tr> <th>Column</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>azimuth</td> <td>Real</td> <td>Azimuth of the crescent feature in degrees</td> </tr> </tbody> </table>
Hydrogen peroxide in the upper tropical troposphere over the Atlantic Ocean and western Africa during the CAFE-Africa aircraft campaign
<p>We provide here the supporting dataset for our study on airborne measurements of oh hydrogen peroxide in the upper tropical troposphere over the Atlantic Ocean and western Africa during the CAFE-Africa aircraft campaign in 2018.</p> <p> </p>
Data from: Mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India
<p>This dataset contains data from the following publication:</p> <p>Sridhar, H., Raman, T. R. S. & Mudappa, D. 2008. <a href="https://www.currentscience.ac.in/Volumes/94/06/0748.pdf">Mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India</a>. <em>Current Science</em> 94: 748-757.<br> URL: <a href="https://www.currentscience.ac.in/Volumes/94/06/0748.pdf">https://www.currentscience.ac.in/Volumes/94/06/0748.pdf</a><br> URL2: <a href="https://www.jstor.org/stable/24100628">https://www.jstor.org/stable/24100628</a></p> <p><em>Corrigendum:</em></p> <p>Sridhar, H., Raman, T. R. S. & Mudappa, D. 2009. <a href="https://www.currentscience.ac.in/Volumes/97/05/0612.pdf">Corrigendum: mammal persistence and abundance in tropical rainforest remnants in the southern Western Ghats, India</a>. <em>Current Science</em> 97: 612-613.<br> URL: <a href="https://www.currentscience.ac.in/Volumes/97/05/0612.pdf">https://www.currentscience.ac.in/Volumes/97/05/0612.pdf</a></p> <p><strong>Description of dataset:</strong></p> <p>The data contains detections of mammals and hornbills (and few incidental records of other species) made along line transect surveys and opportunistic surveys in the Valparai Plateau and Anamalai Tiger Reserve, Tamil Nadu, India. Further details of the Study Area and methods are available in Sridhar et al. (2008), but methods are briefly described below.</p> <p>Five rainforest patches were chosen within IGWLS and four privately-owned rainforest fragments in the Valparai plateau. Fifteen line transects, ranging in length from 1 to 3 km were laid across the nine sites, with the three largest sites having 2–4 transects each. The total distance covered by all transects was 32.02 km. Each transect was walked five times between September 2005 and April 2006 following standard distance sampling protocol. Two observers walked each transect at 0.75–1 km/h. For each detection, we recorded species, group size and perpendicular distance (measured using a rangefinder) from the transect. For animals which occurred in groups, perpendicular distances were measured to group centres. Apart from detections on transects, attempts were made to obtain group sizes of mammal species whenever incidentally detected. All transects were walked between 0630 and 1000 h. Indirect evidence (scat, tracks) on transects and incidental sightings (direct and indirect) of mammals were also recorded.</p> <p><strong>AUTHOR #1</strong></p> <p>1. Name: Hari Sridhar<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Current Work Address: Konrad Lorenz Institute for Evolution and Cognition Research, A-3400 Klosterneuburg, Austria<br> 4. Email address: harisridhar1982@gmail.com<br> 5. ORCID: https://orcid.org/0000-0003-3286-0120</p> <p><strong>AUTHOR #2</strong></p> <p>1. Name: T. R. Shankar Raman<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: trsr@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0002-1347-3953</p> <p><strong>AUTHOR #3</strong></p> <p>1. Name: Divya Mudappa<br> 2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br> 3. Work Phone: +91 821 2515601<br> 4. Email address: divya@ncf-india.org<br> 5. ORCID: https://orcid.org/0000-0001-9708-4826</p> <p><br> <strong>Keywords:</strong> tropical rainforest, tea plantation, coffee plantation, line transect, population density, distance sampling, Anamalai Tiger Reserve, Valparai Plateau, Anamalai Hills, Western Ghats, mammals, hornbills</p> <p><br> <strong>Geographic Coverage:</strong></p> <p>1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India</p> <p>2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p> <p><br> <strong>Temporal Coverage:</strong></p> <p>1. Begins: 2005-09-01 (Year, Month, Day)</p> <p>2. Ends: 2006-10-31 (Year, Month, Day)</p> <p> </p> <p><strong>Dataset files:</strong></p> <p>Besides the <strong>00_README.txt</strong> file, the dataset includes 4 comma-delimited text (csv) files with the data in columns as explained below:</p> <p><strong>01_Transect_locations.csv</strong> — contains transect location details and descriptions</p> <p><strong>02_Transects_and_opportunistic_surveys.csv</strong> — contains main dataset of observations on line transect and opportunistic surveys</p> <p><strong>03_Opportunistic_observations_locations.csv</strong> — contains location details of opportunistic surveys</p> <p><strong>04_Lion-tailed_macaque_counts.csv</strong> — contains counts of lion-tailed macaque (<em>Macaca silenus</em>) troops</p> <p><strong>05_allmammals_raw.xls</strong> — raw data NOT for use, for reference only in original Microsoft Excel format</p> <p> </p> <p><strong>Data variables and descriptions:</strong></p> <p><strong>01_Transect_locations.csv</strong><br> TransectCode: Unique transect code (as used in Appendix 1 of Sridhar et al. 2008 paper), labelled as EXTRA for opportunistic surveys and incidental observations<br> TransectLength_m: Length of line transect in metres<br> decimalLatitude: approximate midpoint latitude in decimal degrees (N), WGS84 datum<br> decimalLongitude: approximate midpoint longitude in decimal degrees (E), WGS84 datum<br> StartLat: transect starting point latitude in decimal degrees (N), WGS84 datum<br> StartLon: transect starting point longitude in decimal degrees (E), WGS84 datum<br> EndLat: transect ending point latitude in decimal degrees (N), WGS84 datum<br> EndLon: transect ending longitude in decimal degrees (E), WGS84 datum<br> MidLat: approximate mid-way location latitude in decimal degrees (N), WGS84 datum<br> MidLon: approximate mid-way longitude in decimal degrees (E), WGS84 datum<br> ExtraLatLon: additional pairs of latitude and longitude points along transect in decimal degrees (E, N), WGS84 datum<br> RouteDescription: description of transect route</p> <p><br> <strong>02_Transects_and_opportunistic_surveys.csv</strong><br> eventID: unique ID of sampling event corresponding to a single on-foot survey of a line transect, with elements separated by colons and last two elements referring to TransectCode and replicate survey number<br> occurrenceID: unique ID assigned to each occurrence (detection) along line transect<br> Sno: serial number<br> TransectCode: Unique transect code (as used in Appendix 1 of Sridhar et al. 2008 paper), labelled as EXTRA for opportunistic surveys and incidental observations<br> locality: name of transect<br> Transectno: unique number assigned to each transect survey or resurvey<br> Replicate: number indicating repeat survey of same transect<br> SiteCategory: Category indicating whether transcet was in Protected Area or Rainforest Fragment<br> Date: date of transect survey or opportunistic observation<br> Weather: Weather at time of survey<br> Habitat: Habitat where observation was made<br> Time: time in 24 h HH:MM format<br> verbatimIdentification: Identification as originally entered<br> scientificName: Scientific name of species or taxon observed<br> vernacularName: Common English name of species or taxon observed<br> Perpdist: Perpendicular distance in metres<br> Freshness: rating of freshness of faeces found (d=day, wk=week, mt=month)<br> individualCount: number of individuals counted, taken as minimum 1 if not noted in field<br> rawNumber: number as originally entered<br> DetectionType: type of observation classified as Call, Faeces, Indirect, Sighting, Track<br> verbatimDetection: raw entry corresponding to previous column<br> Height: height of observed animal above ground in metres<br> occurrenceRemarks: remarks on occurrence</p> <p><br> <strong>03_Opportunistic_observations_locations.csv</strong><br> locality: name of place or transect where opportunistic observation was made<br> decimalLatitude: approximate midpoint latitude in decimal degrees (N), WGS84 datum<br> decimalLongitude: approximate midpoint longitude in decimal degrees (E), WGS84 datum<br> coordinateUncertaintyInMeters: approximate/estimated uncertainty in location coordinates (in metres)</p> <p><br> <strong>04_Lion-tailed_macaque_counts.csv</strong><br> Sno: Serial number of entry<br> Place_or_Transect: Transect (TransectCode) or place where lion-tailed macaques were counted<br> Date: Date of observation<br> Time: Time of observation in 24h HH:MM format<br> Weather: Weather<br> Groupid: ID of Lion-tailed macaque troop, if known<br> Total: Total number of individuals recorded<br> AM: number of adult males<br> AF: number of adult females<br> A: number of adults (unsexed)<br> SA: number of sub-adults (unsexed)<br> SAM: number of sub-adult males<br> SAF: number of sub-adult females<br> JUV: number of juveniles<br> INF: number of infants<br> CARINF: number of infants carried by mother<br> UNID: number of unclassified<br> Remarks: other notes</p> <p> </p> <p><strong>05_allmammals_raw.xls</strong></p> <p>Raw data file in Microsoft Excel format -- for reference only (not advised for use)</p> <p><br> <strong>ADDITIONAL NOTES</strong><br> General notes taken about survey:<br> Pannimade transect 3/11/05 - Most giant squirrel detections were made after squirrel alarm called on seeing a soaring raptor.<br> 36TH hpb transect - very poor visibility on one side as it is very steep<br> Giant squirrels present within LTM troops might go undetected. Need to look carefully and check every movement<br> KO transect 20/01/06 - 1 GS which wasn’t detected when walking transect detected when measuring at less than 20 metres<br> SHK transect 23/01/06 - Abandoned 100 metres from end because of elephants<br> BAN - Ignore detections after 2.05 KM for first two replicates<br> KSPV 26/01/2006 - 1 GS not detected on transect detected while returning at < 40 m<br> Anaigundi - Transect in december strayed slightly from correct path<br> Var 30/01/2006 transect Do not include for indirect signs encounter rate since replicates were done on consecutive days<br> Is there a difference in visibility between wet and dry months; atleast in the more deciduous forests like varagaliar that is the case<br> should I consider only january and afterwards for MGH numbers since vocal activity is much higher then?<br> TF transect 12/02/06 4 GS heard calling from coffee estate adjoining TF; could fewer detections on last transect be because they are moving into coffee, maybe because some tree is fruiting<br> Do NL individuals move solitarily; what average group size to use<br> visibility in BAN and VAR is much better than other sanctuary sites such as IYAK, AN, MA<br> rained on 1st & 2nd of March after a long dry spell<br> KSPV 31/03/06 - Could have missed some calls because of cicada noise<br> KSWT 01/04/06 - Could have missed some calls because of cicada noise<br> KSPV 02/04/06 - Could have missed some calls because of cicada noise<br> KSPV 02/04/06 Do not include for indirect signs encounter rate since replicates were done on consecutive days<br> Great hornbills seem to be more vocal during april. To do with end of nesting??<br> malabar grey hornbills more vocal from february onwards</p>
Morphological cladogenesis and terminal dwarfing in extinct Late Miocene through Pliocene menardiform globorotalids: New complementary data to «Evolutionary prospection in the Neogene planktic foraminifer Globorotalia menardii and related forms from ODP Hole 925B (Céara Rise, western tropical Atlantic): evidence for gradual evolution superimposed by long distance dispersal ?, Swiss J. Palaeontology, 135:205-248»
<p>A complementary morphometric data set is provided to the study of Knappertsbusch (2016) about the shell evolution of menardiform globorotalids (Neogene planktic foraminifera) at ODP Hole 925B from Céara Rise in the the western tropical Atlantic. The new measurements confirm splitting of extinct <em>Globorotalia multicamerata</em> from the <em>G. menardii</em> stock via the intermediate form <em>G. limbata</em> between about 6 Ma to 5 Ma ago. After splitting both <em>G. limbata</em> and <em>G. multicamerata</em> show gradual divergence from <em>G. menardii</em> in several shell parameters illustrating morphological cladogenesis. Between 2.88 Ma and 2.59 Ma the same parameters show a concerted trend towards reduced values indicating pre-extinction dwarfing. A comparison with published literature data of Delta<sup>18</sup>O trends between species, that populated the mixed layer (<em>Globigerinoides sacculifer</em>) and the thermocline layer (<em>Neogloboquadrina dutertrei</em>) at this location during those times suggests, that both divergence and subsequent dwarfing trends were probably the results of changes in upper watermass stratification.</p> <p>The complementary data set is provided in six zipped archives APPENDIX A, B, C, D, E and F (zipped with free software 7-Zip 22.00 (x64), 2022-06-15 from 1999-2022 Igor Pawlow), together with a description of the data in file Report_925B_suppl_1.pdf.</p>
Wordlist files of lexical data from Papua New Guinea and western Solomons Oceanic languages collated for Ross's 1986 PhD thesis and 1988 publication thereof
<p>It occurs to me that the files containing Western Oceanic lexical data that I collected in the late 70s/early 80s for my PhD (Ross 1988) might be useful to someone. They are also used in the volumes of <em>The lexicon of Proto Oceanic </em>(Ross, Pawley & Osmond 1998, 2003, 2011, 2016, 2023). In any case, it is right that they be made publicly available, something that wasn't so easy back then. Most of the material is from wordlists that I collected during fieldwork in Papua New Guinea from around 1978 to 1982. The file cor06 is omitted because it contains SE Solomonic data (outside Western Oceanic) drawn from Tryon & Hackman 1983.</p> <p>I keyed the data into text files in a format such that each line was the entry for a single word, and each field within an entry was marked by a backslash code (I adapted this format from SIL's conventions at the time), then arranged them in cognate sets, each set separated from the next by an empty line. This work was done between 1983 and 1985, when text files were the best way to store data. They were entered on a terminal connected to a mainframe computer at the ANU. I have converted the ASCII symbols used in the original files into UTF-8 here in the interests of readability. The conversion was largely automatic, and I have not done a full check of each file, so there may be glitches.</p> <p>Each file contains languages from a region, as listed below (and the regions sometimes cut across subgroups determined by the comparative method). Three-letter abbreviations are used for language names, and two key files are also provided, one (COR-abbrevs) ordered by regions (determined by the numerals that start each line), the other by alphabetical order of language name (COR-abbrevs-alph). Some three-letter codes are followed by a hyphen and an extra letter. These are dialects. For example, MUM stands for Mumeng and MUM-P for the Patep dialect of Mumeng.</p> <p>Data files are labelled with COR (for 'correspondence sets') plus a numeral. The numerals are: 1-3 New Ireland; 4 Willaumez Peninsula (New Britain) area; 5 NW Solomonic; 7+8 Papuan Tip; 9 Vitiaz Strait area and NG north coast; 10 Huon Gulf and Markham Valley; 11 South and west New Britain. 7+8 are partial only. When I keyed the files, I had to rely on a mainframe's nightly back-up onto tape spools. One night the system failed, and so did the restore, and I lost some data.</p> <p>The backslash codes in the data files are: \l language; \p protolanguage; \w word; \g gloss; \n note; \s source. The formatting of these files is a little odd, since they served as input to routines I wrote to pull out sound correspondences. Anything after '%' is the elicited form: what immediately precedes '%' has had something 'undone', e.g. metathesis.</p> <p>The orthography of the files is phonemic and largely obvious. The conventions are set out in the introductions to the volumes of <em>The lexicon of Proto Oceanic.</em></p> <p>Finally, the files also contain reconstructions at various interstages at the top of a cognate set. These were inserted for heuristic reasons during my research. Many of them did not survive into my PhD thesis, and they should preferably be ignored. The reader who is interested in current Oceanic reconstructions should turn to the volumes of <em>The lexicon of Proto Oceanic.</em></p>
Data for "Climate change is shifting and narrowing prescribed fire windows in the Western United States"
<p><strong>The data archived here represent two types of information used in the publication “Climate change is shifting and narrowing prescribed fire windows in the Western United States” by Swain et al. 2023:</strong></p> <p>1) The meteorological and vegetation dryness/fuel moisture data (as described within the file) extracted from prescribed fire burn plans around the Western United States (drawn from entities such as the U.S. Forest Service, U.S. National Park Service, and The Nature Conservancy) between 2002 and 2022. This data is provided in tabular form, both as an .xlsx file and a .csv file for the convenience of the user. In addition to the specific values from each burn plan, summaries of median values for forested and non-forested landscapes are provided as well.</p> <p>2) The number of days on which environmental conditions (i.e., weather and vegetation fuel moistures) are acceptable for prescribed fire according to the composite metric described in Swain et al. 2023 (known as “RxDays”). Underlying RxDay definitions are different for forest and non-forested landscapes. This data is provided in geospatially explicit (gridded) form as NetCDF files, which are a self-describing file format. Each file is provided as a single 3-dimensional hypercube (i.e., dimensions of time, latitude, and longitude, respectively; units and details described within the file) corresponding to the number of RxDays per calendar month.</p> <p>One file is provided for each climate model iteration (as identified in each filename); these represent projected RxDays at monthly scale between 1981 and 2060 using an RCP 4.5 climate forcing trajectory. An additional file (rx_gridMET.nc) is provided that represents the same values from an atmospheric reanalysis dataset, which represents a best estimate of observed RxDay values (1981-2020).</p>
Tracks of western disturbances (1950-2022) impacting South Asia
<p>WDs are identified using the feature-tracking algorithm described in Hunt et al (2018). Relative vorticity is averaged across the 450-300 hPa layer, and then spectrally truncated to T42 to remove high-frequency noise that hinders tracking. For each region of positive vorticity, the centroid is located and labelled as a candidate WD. These centroids are connected between timesteps using a nearest-neighbour algorithm, biased to take into account the steering winds of the subtropical jet. Systems that do not on average travel eastward, last fewer than 48 hours, or do not pass through the box [20-42.5°N, 60-80°E] are rejected.<br> Applied to ERA5, this gives over seventy years of track data (1950-2022). The method followed here is identical to Nischal et al (2022), except the northern edge of the catching box is extended from 36.5°N to 42.5°N, to ensure that all WDs that potentially impact North India are included.<br> <br> Column titles are:<br> <strong>timestep</strong>: a counter indicating the number of 3-hourly timesteps that have passed since 1950-01-01 00:00<br> <strong>track_id</strong>: a unique identifier linking points into tracks<br> <strong>time</strong>: string describing the date and time<br> <strong>lon</strong>: longitude<br> <strong>lat</strong>: latitude<br> <strong>vort</strong>: vorticity measured at the centre of the WD averaged over the 450-300 hPa layer. Can be used for intensity filtering.<br> <strong>eccentricity</strong>: eccentricity of the region of positive vorticity. Can be used to understand local dynamics.<br> <br> <br> <br> Hunt, K. M. R., Turner, A. G., & Shaffrey, L. C. (2018). The evolution, seasonality and impacts of western disturbances. <em>Quarterly Journal of the Royal Meteorological Society</em>, <em>144</em>(710), 278-290.<br> <br> Nischal, Attada, R., & Hunt, K. M. (2022). Evaluating winter precipitation over the western Himalayas in a high-resolution Indian regional reanalysis using multisource climate datasets. <em>Journal of Applied Meteorology and Climatology</em>, <em>61</em>(11), 1613-1633.</p>
Integrated ground-based data for wildfires occurred in the Western US in September 2020
<p>Data set used in paper Kassianov <em>et al</em>. <strong>Radiative impact of record-breaking wildfires from integrated ground-based data</strong> to be submitted to <em>Sci. Rep.</em></p> <p>For details of data file formats see attached Readme file</p>
Documentary practices and uses in rural context of north-western Iberia
<p>Abstract, slides, datasets, and draft transcript of the presentation given at the XXIIe Colloque de paléographie latine, Prague (14-16 September).</p> <p><strong>Brief abstract: </strong></p> <p>Our proposal intends to examine the documentary practices and uses found in the context of rural, secular communities in the north-western Iberian Peninsula from the late eleventh to the thirteen centuries. These practices will be considered in contrast with those from urban, mostly ecclesiastical, communities. We will assess the form and function of secular diplomas made by rural scribes, before considering the graphic systems and styles used in communities of professional scribes with different levels of literacy. Finally, the mobility of practices and their authors will be evaluated, taking the parish church and the rural monastery as meeting points.</p> <p>Ultimately, this will provide a clearer picture of the connections between rural and urban scribes and their communities, mutual exchanges, reception of new palaeographical and codicological developments and cultural interaction. This innovative assessment of the evolution of documentary practices in north-western Iberia will lead to a better understanding of writing practices across all levels of society.</p> <p><strong>Proposal:</strong></p> <p>Within the context of the transition from Visigothic to Caroline minuscule from the end of the eleventh century until well into the twelfth century in north-western Iberia, and alongside the progressive increase in the use of Romance that will result in the emergence of vernacular languages by the thirteenth century, our proposal intends to address documentary practices and uses in the context of secular rural communities in contrast with those communities culturally located at the centre of change and which are mostly ecclesiastical. With that end in mind, this paper combines three of the perspectives proposed for this congress. We will depart from the analysis of the form and function of secular diplomas made by scribes from rural contexts, before considering the graphic systems and styles used in communities of professional scribes with different levels of literacy. Finally, we will assess the mobility of practices and their authors, taking the parish church and the rural monastery as meeting points.</p> <p>Form and function: We will examine the interaction between the visual and material aspects typical of framed charters, their production and content, in the context of rural secular communities (what distinguishes the secular documentary corpus in terms of its materiality and use?) and in opposition to the charters associated with central ecclesiastical communities. A comparison will be made between the processes of documentary creation in both environments, their raison d’être and usefulness, and the effect on the resulting product as a material object to be read but also seen.</p> <p>The scripts and styles of writing communities: We will study the graphic typologies and documentary practices of professional scribes working for central ecclesiastical institutions (cathedrals and monasteries) in contrast to those in centres of a lower cultural level (rural parishes and minor monasteries), examining their possible interaction with regards to the process of absorption and internalization of new palaeographic and diplomatic developments. In this sense, we propose to delve into the life and development of graphic activity of ecclesiastical and lay agents with limited literacy and to evaluate their role in the configuration of identities.</p> <p>Individual mobility and encounters: The scope of action of the rural scribes will be evaluated alongside their relationship with the secular communities they served. Similarly, we will consider their role as integrating agents as well as their possible itinerancy within (certainly) parochial and, possibly, monastic contexts.</p> <p>As a result of the study of these three perspectives, we will provide a clearer picture of the encounters in the written culture between rural and central scribes and their respective communities, pondering mutual exchanges, reception of new developments and how cultural interaction could affect the evolution of documentary practices in north-western Iberia. This will bring about a better understanding of the practice of writing across all levels of society.</p>
PatagoniaMet: A multi-source hydrometeorological dataset for Western Patagonia
<p><strong>PatagoniaMet v1.0</strong> (PMET from here on) is a new dataset for Western Patagonia that consists of two datasets: i) PMET-obs, a compilation of quality-controlled ground-based hydrometeorological data, and ii) PMET-sim, a daily gridded product of precipitation, and maximum and minimum temperature. PMET-obs was developed using a 4-step quality control process applied to 523 hydro-meteorological time series (precipitation, air temperature, potential evaporation, streamflow and lake level stations) obtained from eight institutions in Chile and Argentina. Based on this dataset and currently available uncorrected gridded products (in this case ERA5), PMET-sim was developed using statistical bias correction procedures (i.e. quantile mapping), spatial regression models (random forest) and hydrological methods (Budyko framework). Details are given below.</p> <p><strong>- PMET-obs </strong>is a compilation of five hydrometeorological variables obtained from eight institutions in Chile and Argentina. The daily quality controlled data of each variable are stored in separate .csv files with the following naming convention: variable_PMETobs_timeperiod_version/timestep.csv. Each column represents a different gauge with its "gauge_id". Each variable has an additional .csv file containing the metadata for each station (variable_PMETobs_version_metadata.csv). In order to make transparent the possible erroneous data that were discarded from the quality-controlled version, a .zip file with the raw data of all variables is attached. The metadata file (final and raw versions) contains the station name (gauge_name), the institution, the station location (gauge_lat and gauge_lon), the NASADEM elevation (gauge_alt) and the total number of daily records (length). In addition, the precipitation and temperature metadata include the number of monthly outliers (step Nº3 in the methods) and the number of changepoints (step Nº4 in the methods).</p> <p>The streamflow metadata file (Q_PMETobs_version_metadata.csv) contains more than just the location data. Following current guidelines for hydrological datasets, the upstream area corresponding to each stream gauge was delimited (.shp file in Basins_PMETobs_version.zip), and several climatic and geographic attributes were derived. The details of the attributes can be found in the README file. For the basins that were part of the hydrological modelling (and that achieved a Kling-Gupta efficiency greater than 0.5), the file Q_PMETobs_version_water_balance.csv is attached, which contains the water balance for each basin estimated for the period 1985-2019. </p> <p><strong>-</strong> <strong>PMET-sim</strong> is a daily gridded product with a spatial resolution of 0.05° covering the period 1980-2020. The data for each variable (precipitation and maximum and minimum temperature) are stored in separate netcdf files with the following naming convention: variable_PMETsim_1980_2020_v10d.nc.</p> <p><strong>Citation: </strong> Aguayo, R., León-Muñoz, J., Aguayo, M., Baez-Villanueva, O., Fernandez, A. Zambrano-Bigiarini, M., and Jacques-Coper, M. (2023) PatagoniaMet: A multi-source hydrometeorological dataset for Western Patagonia. <em>Sci Data</em> 11, 6 (2024). https://doi.org/10.1038/s41597-023-02828-2</p> <p><strong>Code repository: </strong>https://github.com/rodaguayo/PatagoniaMet</p>
Supplementary files for the manuscript "Elevation-dependent periglacial and paraglacial processes modulate tectonically-controlled erosion of the Western Southern Alps, New Zealand", submitted to JGR Earth Surface
<p>This repository contains supplementary files to the manuscript ""Elevation-dependent periglacial and paraglacial processes modulate tectonically-controlled erosion of the Western Southern Alps, New Zealand" submitted to JGR: Earth Surface. It contains: </p> <p>- The Matlab script used to find the optimal distance-from-fault and elevation windows ("elevation_distance_window_optimization"), and 3 text files used for input in this script ("data_erates" contains the erosion rates, "data_elev" the number of pixels in each elevation bin, "data_distAF" the number of pixels in each distance-from-fault bin). </p> <p>- An Excel spreadsheet with the same information that the input text files contain, but specifiying the elevation or distance from fault bin values ("elevation and distance from fault with bins")</p> <p>- A shapefile of catchment outlines ("WSAcatch") for the catchments sampled for CRN denudation rates</p> <p>- Raw CRN data ("Table 2_new_CRN_data")</p> <p>- Excel spreadsheet with the compilation of themochronometric cooling ages used in the age2exhume code (van der Beek & Schildgen, 2023; <a href="https://doi.org/10.5281/zenodo.7341603">https://doi.org/10.5281/zenodo.7341603</a>).</p> <p>CRN data and catchment outlines will also be uploaded to the OCTOPUS database (<a href="https://octopusdata.org/">https://octopusdata.org/</a>) after manuscript acceptance.</p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) July 2021 - April 2022
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from July 2021 to April 2022 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C]; Salinity @ 6 m [psu]; Salinity @ 20 m [psu], Salinity @ 36 m [psu].</p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) October 2020 - July 2021
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from October 2020 to July 2021 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C]; Salinity @ 6 m [psu]; Salinity @ 20 m [psu], Salinity @ 36 m [psu].</p>
Physical oceanography and meteorological data from the W1M3A observatory, Ligurian Sea (North Western Mediterranean) May 2017 - June 2018
<p>Time series data of physical oceanography (salinity, temperature) and meteorology (atmospheric pressure, wind speed and direction, air temperature and humidity, shortwave radiation, longwave radiation and rain) collected from May 2017 to June 2018 by observatory W1M3A at 1h interval. The file contains tabular data (tab delimited) with the following columns: Day; Month; Year; UTC Hour; Minute ; Longitude [deg]; Latitude [deg]; Atmospheric Pressure [hPa]; Wind speed [m/s]; Wind direction [deg]; Air Temperature [°C]; Relative air humidity [%]; Short wave Radiation [W/m2]; Long wave radiation [W/m2]; Rainfall [mm/h]; Sea temperature @ 6 m [°C]; Sea temperature @ 20 m [°C]; Sea temperature @ 36 m [°C]; Salinity @ 6 m [psu]; Salinity @ 20 m [psu], Salinity @ 36 m [psu] </p>
Dataset for Disturbance: a double-edged sword for restoration in a changing climate: Western Oregon and Washington upland prairies 2019-2020
Data associated with the paper submitted to Restoration Ecology in November 2021: Disturbance: a double-edged sword for restoration in a changing climate Alejandro Brambila1, Paul B. Reed1, Scott D. Bridgham1, Bitty A. Roy1, Bart R. Johnson2, Laurel Pfeifer-Meister1 and Lauren M. Hallett1 1. Institute of Ecology and Evolution, University of Oregon 2. Department of Landscape Architecture, University of Oregon In this project, we used this data to test how fire disturbance, designed to enhance restoration seeding success, combines with climate and initial vegetation conditions to shift perennial versus annual grass dominance and overall community diversity in Pacific Northwest grasslands. We seeded both native and introduced perennial grasses and native forbs in paired, replicated burned-unburned plots in three sites along a latitudinal climate gradient from southern Oregon to Washington. Past restoration and climate manipulations at each site had increased the variation of starting conditions between plots. This data is to be used with the script, full_disturbance_script.R, which can be accessed at https://github.com/HallettLab/hops. Includes the tables: plotkey.csv spkey.csv mixkey.csv vegplot.csv vegplot2020.csv
Weather data for the period 2009 to 2022 from the Open Field location at University Farms, Case Western Reserve University
Data from the Open Field weather station at University Farms of Case Western Reserve University include observations from 2009 to 2022. University Farms is located in Hunting Valley, Ohio. From 10/20/2009 to 10/30/2014, the weather station was located at N 41.496883, W 81.436117, when it was relocated to N 41.49759, W81.43738. Data include date/time (in 15-minute intervals), wind speed, wind gust speed, wind direction, air temperature, relative humidity, solar radiation, rainfall, soil moisture, soil temperatures at 0, 2, and 5 cm soil depth, and data logger battery charge.
Weather data for the period 2009 to 2022 from the North Woodlot location at University Farms, Case Western Reserve University
Data from the North Woodlot weather station at University Farms of Case Western Reserve University include observations from 2009 to 2022. University Farms is located in Hunting Valley, Ohio. The North Woodlot weather station is at N 41 29.969, W 81 25.234. Data include Date and time (in 15-minute intervals), wind speed, wind gust speed, air temperature, relative humidity, solar radiation, soil moisture, soil temperatures at 0, 2, and 5 cm soil depth, and data logger battery charge.
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.