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6,281 results for “Landscape”
Landscape composition and shannon diversity of landuse classes aggregated from corine 2018 in 100 - 5000 meter radius areas around Landklif study plots
<p><span>Based on CORINE land cover data from 2018, we aggregated the original land use classes into 8 classes: urban, agriculture, grassland, Broad-leaved forest, Coniferous forest, Mixed forest, natural/seminatural vegetation, and water (see clc_legend.txt). We calculated the landscape composition (percentage of each land use type according to corine land type) for 100 - 5000 meters (100-1000 meter with 100 meter intervals, 1000 - 5000 meter with 500 meter interval) buffer area around landklif plots</span>.</p> <p>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</p>
Forest-related landscape metrics in LandKlif project
<p>We calculated forest-related landscape metrics which have influence on insect diversity. Based on the detailed Landklif map (dataset 11560 at LandKlif database, https://www.landklif.biozentrum.uni-wuerzburg.de), we classify coniferous forest, decideous forest, mixed forest, small wood, and transitional woodland-shrub as forest features. Sub land use class and origical classification was kept as well. This dataset includes the area percentage (landscape composition) of these classes as well as edge length between forest features and non-forest features, in a scale of 100, 200, 500, 1000, 1500 meter radius around the study plots, as well as in TK 25 quadrant scale. TK 25 quadrant is common name in Germany for the topographical map unit at a scale of 1:25000 designated by four-digit numbers, which has a long history (from 1875) and is been used as unit for geographical survey and biodiversity mapping (http://maps.snsb.info/TK25/).</p> <p>Detailed Landklif map was created by combining 3 different land cover maps to create a detailed land cover map for 6 km buffer area around landklif study plots. We used ATKIS 2019 land cover as basis, added details from Invekos 2019 and Corine 2018. We categorized the land cover into 6 classes, further subcategorized them into sub land use classes. The original classification from different sources are kept. In case of overlapping, the priority goes (from high to low): natural > forest > grassland > arable > urban > water. In case of overlapping between data source: transitional woodland-shrub from Corine > Invekos > ATKIS. Areas outside of Bayern are filled with only Corine data. The coordinate system of the shapefile is ETRS89 / UTM zone 32N (EPSG:25832). This dataset is not open access due to its sensitivity but can be reached (https://www.landklif.biozentrum.uni-wuerzburg.de/Download/ShowXml.aspx?DatasetId=11560) and requested via the LandKlif database.</p> <p>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</p>
Bioinformatic pipeline: Genomic diversity landscape of the honey bee gut microbiota
<p>This data-set describes the full bioinformatic pipeline used to analyze 54 metagenomic samples of the honey bee gut microbiota. Each sample was isolated from an individual honey bee, and all samples originate from two colonies of the Engel laboratory at the University of Lausanne, Switzerland. The full raw data-set is available from the sequence-read archive: SRP150166.</p> <p>A publication based on this analysis is currently under review, with the title: "Genomic diversity landscape of the honey bee gut microbiota", and an upload to Biorxiv is also underway.</p> <p>The data-set contains tar-balls for the different main workflows of the analysis. Dowload and unpack to view the contents (tar -zxvf filename.tar.gz). For each workflow, all directories contain README.txt files, describing the contents of the directory. Due to size constraints, some intermediate files have been omitted, and some workflows are demonstrated for a subset of the data. However, the full analysis can be reproduced from the raw data, using the provided scripts.</p> <p>Scripts are included within workflow directories, and are also provided as a separate tar-ball for convenience. All perl-scripts come with documentation, which can be viewed by typing: "perl script_name.pl -h". For R scripts, the usage is indicated as a comment in the top lines of each script. Note that many of the scripts require specific input-files to be present in the run-directory. Their usage is demonstrated within the workflow directories in bash-scripts (*.sh). Commands used for generating plots and some statistics are given within workflow directories in text-files "R.commands" when applicable.</p> <p>Aside from custom code, the pipeline also utilizes various open-source Software packages, which are detailed in the file "software_dependencies.txt". Note, while many of the scripts will run fast on any computer, some steps of the pipeline are computationally demanding, and will require significant computing time, as well as storage space. When scripts are known to be time-consuming, this is indicated in the script help message.</p> <p> </p> <p> </p> <p> </p>
Map of 1992-2015 landscape change trajectories
<p>Map of 1992-2015 landscape change trajectories. Landscapes are colored depending on their change trajectories and a percentage of changed area; small < 10%, medium (10% to 30%), and large (> 30%).</p> <p>To access and visualize the map use: <a href="https://landgis.opengeohub.org/#/?base=OpenTopoMap&opacity=80&layer=ldg_landscape.degradation_sil.9km_c"><strong>https://landgis.opengeohub.org/#/?base=OpenTopoMap&opacity=80&layer=ldg_landscape.degradation_sil.9km_c</strong></a></p> <p>Creation of this map is explained in details at <a href="https://www.sciencedirect.com/science/article/pii/S0303243418305841">https://www.sciencedirect.com/science/article/pii/S0303243418305841</a> (preprint at <a href="https://eartharxiv.org/k3rmn/">https://eartharxiv.org/k3rmn/</a>).</p>
Landscape and habitat data for Tetramorium ant species from Cordonnier et al. 2019 Landscape Ecology
<p>This README accompanies the file "data_Cordonnier_LandEcol.txt"</p> <p>Associated publication : </p> <p>Multi-scale impacts of urbanization on species distribution within the genus <br> <em>Tetramorium </em>- Landscape Ecology<br> M. Cordonnier, C. Gibert, A. Bellec, B. Kaufmann, G. Escarguel</p> <p> <br> ********************************** CONTENTS ***********************************<br> The data are in table form with TABs as variables field delimiters so they can <br> be readily imported in any statistical package or spreadsheet program. Please, <br> contact me if you need the file formatted otherwise. </p> <p>This file includes a description of the variables.</p> <p>The individuals described in this file were identified to species and analyzed for climate variables in</p> <p>Cordonnier, M., Bellec, A., Dumet, A., Escarguel, G., & Kaufmann, B. (2019). <br> Range limits in sympatric cryptic species: a case study in Tetramorium pavement <br> ants (Hymenoptera: Formicidae) across a biogeographical boundary. Insect <br> Conservation and Diversity, 12(2), 109-120.<br> </p> <p>*******************************************************************************<br> Variable names and descriptions</p> <p>ID Sample name<br> X Longitude in WGS 84 (World Geodetic System 1984) decimal degrees rounded to 5 decimal places<br> Y Latitude in WGS 84 (World Geodetic System 1984) decimal degrees rounded to 5 decimal places<br> SZ Name of the sampling area sensu Cordonnier et al. (2019)<br> SP Species identification based on mtDNA COI gene<br> PI10 Percentage of impervious surfaces within a 10 m buffer around the sample<br> PI30 Percentage of impervious surfaces within a 30 m buffer around the sample<br> PI500 Percentage of impervious surfaces within a 500 m buffer around the sample<br> MH1 Presence / absence of full soil with vegetation <br> MH2 Presence / absence of pavement <br> MH3 Presence / absence of unstabilized material (sand. gravel. compacted soil <br> with pebbles or small rocks) <br> MH4 Presence / absence of wood or root <br> MH5 Presence / absence of litter (woodchips or dead leaves) <br> MH6 Presence / absence of curb <br> MH7 Presence / absence of building <br> MH8 Presence / absence of feature (p.ex. lamp post. elec. pole. large rock) <br> MH9 Presence / absence of ditch or strong slope</p> <p>********************************* CONTACT **********************************<br> Please contact me at:</p> <p>Marion Cordonnier<br> e-mail: marion.cordonnier@hotmail.com</p> <p>*******************************************************************************<br> </p>
ArcGIS Map Packages and GIS Data for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al. (2019)
<p><strong>ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)</strong></p> <p>**When using the GIS data included in these map packages, please cite all of the following:</p> <blockquote> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. <a href="http://doi.org/10.1371/journal.pone.0220457">http://doi.org/10.1371/journal.pone.0220457</a></p> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. <a href="https://doi.org/10.5281/zenodo.2572018">https://doi.org/10.5281/zenodo.2572018</a></p> </blockquote> <p><strong>OVERVIEW OF CONTENTS</strong></p> <p>This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:</p> <ul> <li>Raw DEM and Soils data <ul> <li>Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the <a href="https://viewer.nationalmap.gov/basic/">National Elevation Dataset</a>) <ul> <li><strong>DEM_Individual_Tiles</strong>: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.</li> <li><strong>DEMs_Merged</strong>: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM. </li> </ul> </li> <li> Soils Data (Map services and data available from <a href="https://data.nal.usda.gov/dataset/natural-resources-conservation-service-web-soil-survey">Natural Resources Conservation Service Web Soil Survey</a>, U.S. Department of Agriculture) <ul> <li><strong>Animas-Dolores_Area_Soils</strong>: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).</li> <li><strong>Cortez_Area_Soils</strong>: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).</li> </ul> </li> </ul> </li> <li>ArcGIS Map Packages <ul> <li><strong>Goodman_Watershed_Full_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).</li> <li><strong>Goodman_Watershed_Mesa-Only_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.</li> <li><strong>Crow_Canyon_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).</li> <li><strong>Coffey_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).</li> </ul> </li> </ul> <p>For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."</p> <p><strong>LICENSES</strong></p> <p>Code: <a href="http://opensource.org/licenses/MIT">MIT</a> year: 2019 <br> Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton</p> <p><strong>CONTACT</strong></p> <p><strong>Andrew Gillreath-Brown, PhD Candidate, RPA</strong><br> <a href="https://anthro.wsu.edu/">Department of Anthropology</a>, Washington State University<br> <a href="mailto:andrew.brown1234@gmail.com">andrew.brown1234@gmail.com</a> – Email<br> <a href="https://andrewgillreathbrown.wordpress.com/">andrewgillreathbrown.wordpress.com</a> – Web</p>
Data archive associated with "Landscape age as a major control on the geography of soil weathering" (https://doi.org/10.1029/2019GB006266)
<p>(1) Table including parameter values and weathering model outputs associated with NASGLP sampling locations (SLP_data.csv). </p> <p>(2) List of rivers used for calibrating erosion estimates (river_list.csv).</p> <p>(3) R workspace with same data as (1), plus a data frame of global parameter values ("gm") and spatial polygons giving continent boundaries ("con").</p> <p>(4) Scripts with functions for running the single-compartment weathering model at individual point locations or running a global sample of locations and computing summary statistics by continent (run_soilgenesis.R; soilgenesis.R). </p>
Patch metrics and landscape patterns of forest disturbances at the beginning of the 20th Century
<h1>Summary:</h1> <p>The database consists of a compressed .CSV file containing structural information of forest disturbance patches identified between 2002 and 2014 using the Global Forest Change Tree Cover Loss Year dataset version 1.6 (Hansen et al, 2013) available at https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.6.html. Each row in the database represents a patch (249,149,911 in total). The columns (15) represent the structural metrics calculated for each patch, as well as the landscape patterns identified using kmeans cluster analysis. </p> <p>The methods used for building this database are published in the paper: Acil, N., Sadler, J.P., Senf, C. <em>et al.</em> Landscape patterns in stand-replacing disturbances across the world’s forests. <em>Nat Sustain</em> <strong>8</strong>, 86–98 (2025). <a href="https://doi.org/10.1038/s41893-024-01450-3">https://doi.org/10.1038/s41893-024-01450-3</a></p> <p>Aggregated global maps of the patch metrics can be visualised in <a href="https://ee-treemort-disturbances-nacil.projects.earthengine.app/view/patchmetrics2002-2014">Google Earth Engine</a> and accessed in the asset "http://projects/ee-treemort-disturbances-nacil/assets/PatchMetrics_Means_nonLU_2002-2014/". </p> <p>Some of the scripts associated with this project are hosted in <a href="https://github.com/N-Acil/GlobalForestDisturbances_PatchMetrics">GitHub</a> and <a href="https://code.earthengine.google.com/?accept_repo=users/NXA807/%20GlobalForestDisturbances_PatchMetrics">Google Earth Engine</a>.</p> <p>Additional scripts and data will be made available upon request.</p> <p> </p> <p> </p> <h1>Database structure: </h1> <h2>Patch metrics</h2> <h3>Occurrence: </h3> <p>Patch form and year were retrieved from the Global Forest Change tree cover loss year dataset version 1.6 (Hansen et al, 2013).</p> <table> <tbody> <tr> <td><strong>Column name</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> <td><strong>Format</strong></td> <td><strong>Valid values</strong></td> </tr> <tr> <td><strong>PID</strong></td> <td>Patch unique identifier in the format Tile_Year_PatchNumber (e.g. 01U_02_00000001).</td> <td> </td> <td>Characters</td> <td> </td> </tr> <tr> <td><strong>X_INT_deg</strong></td> <td>Longitude of the patch's internal centroid</td> <td>Degrees</td> <td>Float</td> <td>[-180-180]</td> </tr> <tr> <td><strong>Y_INT_deg</strong></td> <td>Latitude of the patch's internal centroid</td> <td>Degrees</td> <td>Float</td> <td>[-90-90]</td> </tr> <tr> <td><strong>YEAR_maj</strong></td> <td>Year of patch majority occurrence. </td> <td> </td> <td>Integer</td> <td>[2-14]</td> </tr> <tr> <td><strong>YEAR_n</strong></td> <td>Number of years over which the patch exhibited continuous growth.</td> <td> </td> <td>Integer</td> <td>>0</td> </tr> </tbody> </table> <h3>Metrics: </h3> <p>These patch and landscape metrics were calculated from the patch delineated.</p> <table> <tbody> <tr> <td><strong>Column name</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> <td><strong>Format</strong></td> <td><strong>Valid values</strong></td> </tr> <tr> <td><strong>AREA_G_ha</strong></td> <td>Patch geodesic area</td> <td>Hectares</td> <td>Float</td> <td>>0</td> </tr> <tr> <td><strong>PERIM_G_m</strong></td> <td>Patch geodesic perimeter</td> <td>Meters</td> <td>Float</td> <td>>0</td> </tr> <tr> <td><strong>PARA</strong></td> <td>Perimeter-area ratio</td> <td> </td> <td>Float</td> <td>>0</td> </tr> <tr> <td><strong>SHAPE</strong></td> <td>Shape index</td> <td> </td> <td>Float</td> <td>>=1</td> </tr> <tr> <td><strong>ELONG</strong></td> <td>Elongation index</td> <td> </td> <td>Float</td> <td>[0-1[</td> </tr> <tr> <td><strong>FRAC</strong></td> <td>Fractal dimension index</td> <td> </td> <td>Float</td> <td>[1-2]</td> </tr> <tr> <td><strong>NN5000_T0_n</strong></td> <td>Number of patches assigned the same year within 5 km radius.</td> <td> </td> <td>Integer</td> <td>>0</td> </tr> <tr> <td><strong>NN5000_AREA_T0_perc</strong><strong><br></strong></td> <td>Percent of the total area disturbed over the period 2001-2018 within 5 km radius from the focal patch centroid.</td> <td>%</td> <td>Float</td> <td>[0-100]</td> </tr> </tbody> </table> <h3>Clusters:</h3> <p>Cluster identification was performed using AREA_G_ha, YEAR_n, SHAPE, ELONG, NN5000_T0_n and NN5000_AREA_T0_perc. </p> <table> <tbody> <tr> <td><strong>Column name</strong></td> <td><strong>Description</strong></td> <td><strong>Unit</strong></td> <td><strong>Format</strong></td> <td><strong>Valid values</strong></td> </tr> <tr> <td><strong>CLUSTER_CODE</strong></td> <td>Code assigned to each cluster</td> <td> </td> <td>Integer</td> <td>[1-4]</td> </tr> <tr> <td><strong>CLUSTER_LABEL</strong></td> <td>Name given to the cluster identified. </td> <td> </td> <td>Character</td> <td> <ul> <li>Small-isolated</li> <li>Clustered</li> <li>Complex</li> <li>Large-multiyear</li> </ul> </td> </tr> </tbody> </table> <p> </p>
Navigating the complex policy landscape for carbon farming in The Netherlands and the EU -- Open Research Europe Extended Data-- Tables 1-6, Figures 1-2
<p>This is extended data for the article entitle 'Navigating the complex policy landscape for carbon farming in The Netherlands and the EU' submitted to Open Research Europe by Eise Spijker. </p>
Data and code from: "Building multidimensional tolerance landscapes to predict the population dynamics of bacteria exposed to antibiotics in urban sewers"
<p>City sewers harbor diverse bacterial communities exposed to various antibiotic residues resulting from human consumption and excretion. Although these residues typically occur at sub-inhibitory concentrations, they can still impact the growth rate and yield of susceptible wastewater bacteria. Many bacteria exhibit antibiotic tolerance through transient phenotypic changes. Antibiotic residues, combined with complex environmental factors like temperature and salinity, especially in coastal cities, contribute to non-additive interactions that modulate antibiotic tolerance and affect population dynamics.</p> <p>To better understand these interactions, we developed continuous multivariate tolerance landscapes for three bacterial species: <strong><em><span>Escherichia coli</span></em></strong>, the emerging pathogen <strong><em><span>Streptococcus suis</span></em></strong>, and the sewer-inhabiting <strong><em><span>Arcobacter cryaerophilus</span></em></strong>. We modeled their intrinsic growth rates and carrying capacities across complex environments, incorporating temperature, salinity, and concentrations of two antibiotics (ciprofloxacin and azithromycin).<span> Using</span> these multivariate tolerance curves, we predicted microbial population dynamics in two sewers of Barcelona, highlighting the importance of environmental complexity in shaping microbial responses to antibiotic stressors.</p> <p> </p> <p><strong>Usage</strong></p> <p>Users can perform the analysis by running the R script (TC3D.R) after the installation of all</p> <p>package mentioned in the preamble,<span> </span></p> <p>This folder contains:</p> <p>- 3 datasets with OD measures for the 3 species:</p> <p><span> </span>* data_acrya.xlsx</p> <p><span> </span>* data_ecoli.xlsx</p> <p><span> </span>* data_ssuis.xlsx</p> <p>- 1 excel files with metadata (plate, well, species, environmental conditions)</p> <p><span> </span>* map_plate_all.xlsx</p> <p>- 4 datasets giving time series of the flow and several measures including <span> </span>conductivity and <span> </span>temperaturefor 2 sewers of Barcelona obtained from sample cabines <span> </span>set during the implementation of SCOREWATER (ID:820751)</p> <p><span> </span>* carmel_flow.csv</p> <p><span> </span>* carmel_quality.csv</p> <p><span> </span>* poblenou_flow.csv</p> <p><span> </span>* poblenou_quality.csv</p> <p>- 1 C++ script compiled and run with the R TMB package:</p> <p><span> </span>* fit_growth_r_K_SS_treatment.cpp : computes the negative loglikelihood for r and K, and state DOs, given the observed DO, for the populations under one same environmental treatment (salinity * temperature * antibiotic), and computes the density-dependence parameter alpha from r and K using the Delta Method.</p> <p><br><br></p>
Genome data and resources on the recombination landscape and population history of the harlequin fly
<p>This dataset contains phased vcf files of <em>Chironomus riparius, </em>ouput files of RepeatMasker, MELT, RepeatOBserver, MSMC2, iSMC and bedtools, such as supporting files. </p> <p>For further details also check the GitHub page: <a href="https://github.com/lpettrich/Crip_Recombination_PopHistory_Cla_2024" target="_blank" rel="noopener">https://github.com/lpettrich/Crip_Recombination_PopHistory_Cla_2024</a></p> <ul> <li><strong>phased-vcfs: </strong>Artificially phased vcf-files of five populations with four individuals each. Needed to generate multihetsep files. Input files for iSMC.<br> <ul> <li>Hesse in Germany = MG</li> <li>Rhône-Alpes in France = MF</li> <li>Lorraine in France = NMF</li> <li>Piemont in Italy = SI</li> <li>Andalusia in Spain = SS</li> </ul> </li> <li><strong>multihetsep-files: </strong>Created with msmc-tools. Input files for MSMC2. </li> <li><strong>RepeatMasker: </strong>Raw output of RepeatMasker run. Summary file and file with filtered <em>Cla</em>-element (a transposable element) included.<strong><br></strong></li> <li><strong>MELT: </strong>MELT ouput with added info on population and numbered insertions reflecting all 441 detected <em>Cla </em>insertions.<strong><br></strong></li> <li><strong>RepeatOBserver: </strong>Summary files on centromere predictions based on histograms and Shannon Diversity from RepeatOBserver. Genome-wise Shannon Diversity per chromosome included. <strong><br></strong></li> <li><strong>MSMC2: </strong>Raw ouput of combined cross-coalescence and mean values if MSMC2 per populations. <strong><br></strong></li> <li><strong>iSMC: </strong>Recombination rate rho in 10 kb windows and 100 kb windows along the genome. <strong><br></strong></li> <li><strong>bedtools closest ismc 10 kb: </strong>Bedtools closest analysis of the distance of the next <em>Cla</em>-element to the recombination rate rho in 10 kb windows.<strong><br></strong></li> <li><strong>bedtools closest ismc 100 kb: </strong>Bedtools closest analysis of the distance of the next <em>Cla</em>-element to the recombination rate rho in 100 kb windows.</li> <li><strong>input-files figures: </strong>Supporting files needed to create figures.<strong><br></strong></li> </ul>
Data from: Polymorphic tandem repeats shape single-cell gene expression across the immune landscape
<p>This dataset contains the association summary statistics (v0.1) for genome-wide tandem repeat (TR) expression quantitative trait (eQTL) analysis of TenK10K Phase 1 (https://doi.org/10.1101/2024.11.02.621562). </p> <p>Please access the README for a detailed description of file contents. </p> <p> </p>
Raw data for Infrastructure and Awareness Landscape Analysis in sub-Saharan Africa
<p>Persistent identifiers that are well-connected are essential for enhancing research, researchers, and research institutions. The comprehensive raw data shared on PIDs infrastructure and awareness landscape analysis in sub-Saharan Africa is taken from service providers and organizations, including Open DOAR, the Registry of Open Access Repositories (ROAR), the Registry of Research Data Repositories (Re3data), UNESCO, Lyrasis (Dspace), Dataverse, Open Journal System (OJS), among others. The data shared here was collected in August 2023. The data shared are secondary data, and the position is strictly based on the primary source data author. The data is restricted to what is available on the internet and does not include locally hosted offline data.</p> <p>Further analysis of the raw data suggested some salient implications for PIDs awareness in the region. Variations were observed across the various data sources, while some interesting correlations emerged from the collected data. There are countries with some PIDs infrastructure, while others are yet to establish a visible presence in PIDs infrastructure. The visibility of PIDs is somewhat related to the awareness level as well as the policy established on open access in the represented countries across the region.</p>
MaMo online Webinar Cycle "Materializing Modernity - Landscape, Architecture and Anthropology intersections in 20th-century rurality"
<p>A dataset (WP2-B_Materials_1) containing the video recordings of the MaMo Webinar Cycle titled “Materializing Modernity: Landscape, Architecture and Anthropology intersections in 20th-century rurality” held on the ZOOM platform in April and May 2021. Activity developed under the Work Package 2 (WP2), Secondment period at Università degli Studi di Milano (UNIMI), Italy. All the events have been organized by Dr Federica Pompejano (MSCA-IF Fellow) in collaboration with the Laboratory of Ethnomusicology and Visual Anthropology (LEAV) of the Department of Cultural and Environmental Heritage (UNIMI) and the Institute of Cultural Anthropology and Art Studies (IAKSA) of the Akademia e Studimeve Albanologjike (ASA), Tirana, Albania.</p> <p>WP2-B_Materials_1 (PART 1) - Contents</p> <ul> <li>Programme of the MaMo Webinar Cycle "Materializing Modernity: Landscape, Architecture and Anthropology intersections in 20th-century rurality" held in April-May 2021 on ZOOM online platform</li> <li>Banner of the MaMo Webinar Cycle "Materializing Modernity: Landscape, Architecture and Anthropology intersections in 20th-century rurality" held in April-May 2021 on ZOOM online platform</li> <li>1st meeting - Introduction: MaMo - an introduction to Albanian Socialist and Post-Socialist rurality by Federica Pompejano, MSCA-IF Fellow, Department of Ethnology, Institute of Cultural Anthropology and Art Studies (IAKSA), Academy of Albanian Studies, Albania<br> Oral presentation: "The Albanian Village as an anthropological encounter of modernity" by Nebi Bardhoshi, Associate Professor and Director of the Institute of Cultural Anthropology and Art Studies (IAKSA), Academy of Albanian Studies, and Olsi Lelaj, Researcher, Department of Ethnology, IAKSA, Academy of Albanian Studies, Albania</li> <li>MaMo Webinar Cycle - 1st meeting banner</li> <li>MaMo Webinar Cycle - 1st meeting poster with oral presentation abstract</li> <li>MaMo Webinar Cycle - 1st meeting poster with oral presenters short bio</li> <li>MaMo Webinar Cycle - 1st meeting Instagram post</li> <li>2nd meeting - Oral presentation: "Exploring Rurality in Southern Italy: the experience of 'Sonic Ethnography'" by Nicola Scaldaferri, Associate Professor, Department of Cultural and Environmental Heritage, Università Statale di Milano, Italy, and Lorenzo Ferrarini, Lecturer in Social and Visual Anthropology, Granada Centre for Visual Anthropology, University of Manchester, United Kingdom</li> <li>MaMo Webinar Cycle - 2nd meeting banner</li> <li>MaMo Webinar Cycle - 2nd meeting poster with oral presentation abstract</li> <li>MaMo Webinar Cycle - 2nd meeting poster with oral presenters short bio</li> <li>MaMo Webinar Cycle - 2nd meeting Instagram post</li> </ul>
MaMo Webinar Cycle "Materializing Modernity: Landscape, Architecture and Anthropology intersections in 20th-century rurality" - Part 2
<p>A dataset (WP2-B_Materials_2) containing the video recordings of the MaMo Webinar Cycle titled “Materializing Modernity: Landscape, Architecture and Anthropology intersections in 20th-century rurality” held on the ZOOM platform in April and May 2021. Activity developed under the Work Package 2 (WP2), Secondment period at Università degli Studi di Milano (UNIMI), Italy. All the events have been organized by Dr Federica Pompejano (MSCA-IF Fellow) in collaboration with the Laboratory of Ethnomusicology and Visual Anthropology (LEAV) of the Department of Cultural and Environmental Heritage (UNIMI) and the Institute of Cultural Anthropology and Art Studies (IAKSA) of the Akademia e Studimeve Albanologjike (ASA), Tirana, Albania.</p> <p>WP2-B_Materials_2 (PART 2) - Contents</p> <ul> <li>3rd meeting - Oral presentation 1: "Embedding the Past into Modernist Rural Landscapes" by Cristina Pallini, Associate Professor, Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Italy - Oral presentation 2: "Figures in a landscape: notes for a history of rural planning and village design in the Eastern bloc" by Axel Fischer, Associate Professor p.t., Université libre de Bruxelles, School of Architecture La Cambre Horta, hortence lab for architectural history, theory and criticism, Belgium (WP2-B_Webinar_03W.mpeg)</li> <li>MaMo Webinar Cycle - 3rd meeting banner (WP2-B_Webinar-03W_Banner.jpg)</li> <li>MaMo Webinar Cycle - 3rd meeting poster with oral presentation abstract - 1 (WP2-B_Webinar_03W-Poster1.jpg)</li> <li>MaMo Webinar Cycle - 3rd meeting poster with oral presentation abstract - 2 (WP2-B_Webinar_03W-Poster2.jpg)</li> <li>MaMo Webinar Cycle - 3rd meeting poster with oral presenters short bio (WP2-B_Webinar_03W-Poster3.jpg)</li> <li>MaMo Webinar Cycle - 3rd meeting Instagram post - 1 (WP2-B_Webinar_03W_IG1.jpg)</li> <li>MaMo Webinar Cycle - 3rd meeting Instagram post - 2 (WP2-B_Webinar_03W_IG2.jpg)</li> </ul>
MaMo Webinar Cycle "Materializing Modernity: Landscape, Architecture and Anthropology intersections in 20th-century rurality" - Part 3
<p>A dataset (WP2-B_Materials_3) containing the video recordings of the MaMo Webinar Cycle titled “Materializing Modernity: Landscape, Architecture and Anthropology intersections in 20th-century rurality” held on the ZOOM platform in April and May 2021. Activity developed under the Work Package 2 (WP2), Secondment period at Università degli Studi di Milano (UNIMI), Italy. All the events have been organized by Dr Federica Pompejano (MSCA-IF Fellow) in collaboration with the Laboratory of Ethnomusicology and Visual Anthropology (LEAV) of the Department of Cultural and Environmental Heritage (UNIMI) and the Institute of Cultural Anthropology and Art Studies (IAKSA) of the Akademia e Studimeve Albanologjike (ASA), Tirana, Albania.</p> <p>WP2-B_Materials_3 (PART 3) - Contents</p> <ul> <li>4th meeting - Oral presentation: "Concepts integrated conservation as inroads to sustainable management of cultural landscapes" by Bosse Lagerqvist, Associate Professor and Senior Lecturer, Department of Conservation, Göteborgs Universitet, Sweden (WP2-B_Webinar_04W.mpeg)</li> <li>MaMo Webinar Cycle - 4th meeting banner (WP2-B_Webinar_04W_Banner.jpg)</li> <li>MaMo Webinar Cycle - 4th meeting poster with oral presentation abstract (WP2-B_Webinar_04W_Poster1.jpg)</li> <li>MaMo Webinar Cycle - 4th meeting poster with oral presenter short bio (WP2-B_Webinar_04W_Poster2.jpg)</li> <li>MaMo Webinar Cycle - 4th meeting Instagram post (WP2-B_Webinar_04W_IG.jpg)</li> <li>5th meeting - Oral presentation: "Socialist Modernism in the former Eastern Bloc (1955-1991), The Socialist Modernism map at socialistmodernism.com" by Dumitru Rusu, President of B.A.C.U. Association - Birou pentru Art si Cercetare Urbană (Bureau for Art and Urban Research), Romania (WP2-B_Webinar_05W.mpeg)</li> <li>MaMo Webinar Cycle - 5th meeting banner (WP2-B_Webinar_05W_Banner.jpg)</li> <li>MaMo Webinar Cycle - 5th meeting poster with oral presentation abstract (WP2-B_Webinar_05W_Poster1.jpg)</li> <li>MaMo Webinar Cycle - 5th meeting poster with oral presenter short bio (WP2-B_Webinar_05W_Poster2.jpg)</li> <li>MaMo Webinar Cycle - 5th meeting Instagram post (WP2-B_Webinar_05W_IG.jpg)</li> </ul>
Landscape composition drives the impacts of artificial light at night on insectivorous bats
<p>Abstract of the related publication :</p> <p>Among the most prevalent sources of biodiversity declines, Artificial Light At Night (ALAN) is an emerging threat<br> to global biodiversity. Much knowledge has already been gained to reduce impacts. However, the spatial variation<br> of ALAN effects on biodiversity in interaction with landscape composition remains little studied, though it is<br> of the utmost importance to identify lightscapes most in need of action. Several studies have shown that, at local<br> scale, tree cover can intensify positive or negative effects of ALAN on biodiversity, but none have – at landscape<br> scale – studied a wider range of landscape compositions around lit sites. We hypothesized that the magnitude of<br> ALAN effects will depend on landscape composition and species’ tolerance to light. Taking the case of insectivorous<br> bats because of their varying sensitivity to ALAN, we investigated the species-specific activity response to<br> ALAN. Bat activity was recorded along a gradient of light radiance. We ensured a large variability in landscape<br> composition around 253 sampling sites. Among the 13 bat taxa studied, radiance decreased the activity of two<br> groups of the slow-flying gleaner guild (Myotis and Plecotus spp.) and one species of the aerial-hawking guild<br> (Pipistrellus pipistrellus), and increased the activity of two species of the aerial-hawking guild (Pipistrellus kuhlii<br> and Pipistrellus pygmaeus). Among these five effects, the magnitude of four of them was driven by landscape composition.<br> For five other species, ALAN effects were only detectable in particular landscape compositions, making<br> the main effect of radiance undetectable without account for interactions with landscape. Specifically, effects<br> were strongest in non-urban habitats, for both guilds. Results highlight the importance to prioritize ALAN reduction<br> efforts in non-urban habitats, and how important is to account for landscape composition when studying<br> ALAN effects on bats to avoid missing effects.</p>
Research Data Repository Landscape infographic (European Research Data Landscape study)
<p>Infographic of the findings on research data repository landscape in the European Research Data Landscape study.</p>
Research Data Landscape infographic (European Research Data Landscape study)
<p>Infographic of the findings on research data landscape in the European Research Data Landscape study.</p>
FAIR Data Practices in Europe infographic (European Research Data Landscape study)
<p>Infographic of the findings on on FAIR data practices in Europe, part of the European Research Data Landscape study.</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.