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2,721 results for “Connectivity”
The effectiveness of freshwater connectivity as a predictor of species distribution
<p>The attached dataset contains three dataframes used in the affiliated papers.</p> <p>1) DirectSlopeData.rda - Recolonisation success of two species, northern pike and European perch, in rotenone-treated lakes in Sweden, alongside connectivity parameters for the associated lakes.</p> <p>2) HPD.rda - Credible intervals for the beta estimates generated by the BORAL model in 3.</p> <p>3) Presence/absence data for seven species in lakes throughout the Kautokeino catchment in Northern Norway, alongside selected environmental covariates for associated lakes.</p>
Connectivity networks for Acropora corals on the GBR to investigate split spawning
<p>Connectivity networks for Acropora corals on the GBR to investigate split spawning.</p> <p>If using these outputs please cite the article:</p> <p>Hock K, Doropoulos C, Gorton R, Condie SA, Mumby PJ. (2019). <strong>Split spawning increases robustness of coral larval supply and inter-reef connectivity</strong>. Nature Communications <strong>10</strong>, 3463.</p> <p>Link to the paper:</p> <p>https://rdcu.be/bOW1x</p> <p> </p> <p> </p> <p> </p>
VRTogether Pilot 2 connection with the reporter video
<p>Video of the reporter in stereo format 180º.</p> <p>Codec: H264 - MPEG-4 AVC</p> <p>Dimensions: 5760x2880</p> <p>Framerate: 29.97 fps</p> <p>Bitrate: 8805kbps</p> <p>Audio: Stereo 48000 kHz</p>
Data and code for 'Connectivity over a disease gradient enables recovery of rainforest frogs'
<p>This repository provides all data and R code from the analysis presented in the following paper:</p> <p>Bell, S.C., Heard, G.W., Berger, L. and Skerratt, L.F. (in review). Connectivity over a disease risk gradient enables recovery of rainforest frogs. </p> <p>The data are provided as a series of .csv files. R code is provided separately for each of the following components:</p> <p>1. A script to collate nightly air temperature data from stream-side temperature loggers, plus daily maximum air temperature data from the nearest Australian Bureau of Meteorology recording station to each site.</p> <p>2. A script to fit a hierarchical linear model to the stream-side air temperature, with the aid of the R2OpenBUGS package.</p> <p>3. A script to fit a hierarchical logistic regression model to the chytrid infection data, with the aid of the R2OpenBUGS package.</p> <p>4. A script to fit logistic regression models to the frog occupancy data, with the aid of the R2OpenBUGS package. </p> <p>These files allow all analyses from the paper to be reproduced. </p>
Data and code for 'Connectivity over a disease risk gradient enables recovery of rainforest frogs'
<p>This repository provides all data and R code from the analysis presented in the following paper:</p> <p>Bell, S.C., Heard, G.W., Berger, L. and Skerratt, L.F. (in review). Connectivity over a disease risk gradient enables recovery of rainforest frogs. </p> <p>The data are provided as a series of .csv files. R code is provided separately for each of the following components:</p> <p>1. A script to collate nightly air temperature data from stream-side temperature loggers, plus daily maximum air temperature data from the nearest Australian Bureau of Meteorology recording station to each site.</p> <p>2. A script to fit a hierarchical linear model to the stream-side air temperature, with the aid of the R2OpenBUGS package.</p> <p>3. A script to fit a hierarchical logistic regression model to the chytrid infection data, with the aid of the R2OpenBUGS package.</p> <p>4. A script to fit logistic regression models to the frog occupancy data, with the aid of the R2OpenBUGS package. </p> <p>These files allow all analyses from the paper to be reproduced. </p>
Larval dispersal histogram data used for ATLAS deliverable D1.6: Biologically realistic Lagrangian dispersal and connectivity
<p>Larval dispersal histogram data for ATLAS deliverable D1.6 "Biologically realistic Lagrangian connectivity" (https://www.eu-atlas.org/resources/atlas-partners-document-area/atlas-deliverables/455-d1-6-biologically-realistic-lagrangian-connectivity/file). Tar archive files are ordered by ATLAS case study source region and with folders by larval behaviour type. The numbered behaviour types are described in deliverable D1.6. Each netcdf histogram file, e.g. hists_age_21.nc, contains the histogram for larvae of a single age in 5-day steps, from 00 (0 days) to 37 (185 days).</p> <p>Within each file histogram file, particle counts in each Viking20 model grid-cell are contained in a 4-d array with dimensions (launch month, lauch year, model gridsquare y index, model gridsquare x index). The Viking20 grid in the North Atlantic is the ORCA tripolar grid. Details of the model mesh are in the included file viking20_mesh_mask.tgz</p> <p>Histograms are in netcdf files:</p> <p>============================</p> <p>$ ncdump -h hists_age_00.nc<br> netcdf hists_age_00 {<br> dimensions:<br> coordinate = 4 ;<br> coordinate_1 = 50 ;<br> coordinate_2 = 1719 ;<br> coordinate_3 = 1784 ;<br> variables:<br> int64 coordinate(coordinate) ;<br> coordinate:units = "month" ;<br> coordinate:long_name = "Launch month" ;<br> int64 coordinate_1(coordinate_1) ;<br> coordinate_1:units = "year" ;<br> coordinate_1:long_name = "Launch year" ;<br> int64 coordinate_2(coordinate_2) ;<br> coordinate_2:units = "index" ;<br> coordinate_2:long_name = "J index" ;<br> int64 coordinate_3(coordinate_3) ;<br> coordinate_3:units = "index" ;<br> coordinate_3:long_name = "I index" ;<br> int64 data(coordinate, coordinate_1, coordinate_2, coordinate_3) ;<br> data :long_name = "particle count" ;</p> <p>// global attributes:<br> :Conventions = "CF-1.6" ;<br> }</p> <p>==========================================</p> <p> </p> <p> </p>
Labor flows and input-output connections in Hungary
<p>Aggregated data on labor flows and input-output connections in Hungary (2015-2017).</p> <p>Related preprint: https://arxiv.org/abs/2405.07071</p> <p>Interactive visualization site: https://vis.csh.ac.at/colocation-suppliers/</p> <p>The related research by Sándor Juhász was supported by the European Union’s Marie Sklodowska-Curie Postdoctoral Fellowship Program (SUPPED, grant number 101062606).</p> <p>The data preparation was done with the help of the Databank of HUN-REN Centre for Economic and Regional Studies. The shared datasets are based on the value-added tax return data files of the Hungarian Central Statistical Office. The calculations and conclusions drawn from them are the sole intellectual property of the authors.</p> <p> </p>
Supplementary Data for "Climate change may induce connectivity loss and mountaintop extinction in Central American forests"
<p>Supplementary data underlying the main figures presented in the publication "<strong>Climate change may induce connectivity loss and mountaintop extinction in Central American forests</strong>"</p>
About ERIGrid 2.0 - Connecting European Smart Grid Research Infrastructures (IEA version)
<p>This video provides a brief overview of the activities and services of the <a href="https://ec.europa.eu/programmes/horizon2020/en">H2020</a> <a href="https://erigrid2.eu/">ERIGrid 2.0</a> research infrastructure project as well its links with the <a href="https://www.iea.org/">IEA</a>, especially its technology collaboration programme <a href="https://www.iea-isgan.org/">ISGAN</a> - Annex 5 <a href="https://www.iea-isgan.org/our-work/annex-5/">SIRFN</a>.</p>
Dataset: Environmental Impact on the Long-Term Connectivity and Link Quality of an Outdoor LoRa Network
<p>This repository contains the long-term connectivity and link quality dataset collected on <a href="https://chirpbox.github.io/">ChirpBox</a> over 4 months (May -- September 2021) in the city of Shanghai, China. </p> <p>In addition to the dataset itself, we provide evaluation scripts for data analysis and visualization, in order to facilitate data exploration and re-use. To make it clear how to use the scripts, we provide a <em>Jupyter notebook -- </em> <strong>dataset.ipynb</strong> for dataset visualization.</p> <p><strong>List of files:</strong></p> <ol> <li><em>dataset_03052021_15092021.csv</em> <ul> <li>The dataset includes LoRa connectivity and link quality, as well as environmental information, collected from May 3 to September 15, 2021.</li> </ul> </li> <li><em>data_analysis.py</em> <ul> <li>The script for dataset analysis and visualization. One can use the functions in this script to derive network-level statistics (e.g., in terms of average number of correctly-exchanged packets), link-level statistics (e.g., in terms of SNR, RSS, and PRR), and node-level statistics(e.g., in terms of number of neighbours and temperature evolution over time).</li> </ul> </li> <li><em>metadata_processing.py</em> <ul> <li>The script for pre-processing metadata into CSV files. One can use the functions in this script to convert metadata for each measurement saved in TXT and JSON formats to CSV files that include attributes such as link quality, connectivity, and environmental information, an example of which is <strong>dataset_03052021_15092021.csv</strong>.</li> </ul> </li> <li><em>dataset.ipynb </em> <ul> <li>The Jupiter notebook contains examples of visualization and metadata pre-processing of datasets with functions in <strong>data_analysis.py</strong> and <strong>metadata_processing.py</strong>.</li> </ul> </li> <li><em>topology_map.png</em> <ul> <li>The node deployment map used to create topology figures. A usage example is <strong>Figure 1</strong> shown in the notebook <strong>dataset.ipynb</strong>.</li> </ul> </li> <li><em>dataset_metadata.zip</em> <ul> <li>The dataset metadata is stored in TXT and JSON formats. Among them, link quality, connectivity and on-board sensor data are stored in TXT files and weather information are stored in JOSN files.</li> </ul> </li> <li><em>README.md</em> <ul> <li>The README.md explains all the files in this repository and gives some examples of how to use the provided scripts to analyze the dataset.</li> </ul> </li> </ol>
PAsCAL WP6 Pilot 4 Shared Connected Transport
<p>These two datasets were collected within the context of the PAsCAL research project between November 2021 and June 2022. Each of the surveys were dedicated to two different pilot scenarios, settings and vehicles:</p> <ol> <li>Shared Connected Vehicle Fleet: An existing rental service for a vehicle fleet for employees and students of the University of Luxembourg was enhanced by adding an advances Level-2+ vehicle to the fleet. The users were already familiar with the functionality of the service (booking process, etc.) and were asked to take a realistic trip including urban areas but also a strip of highway and were invited to test the autonomous features of the vehicle (removing hands from steering wheel, automatic parking and many more). The pilot took place in the Belval area of Luxembourg;</li> <li>Bus shuttle: An autonomous bus shuttle with Level-4 autonomy was piloted, which connects a train station to a business park. Participants were workers or visitors of the business park and the objective of this pilot was to observe the adequacy of the shuttle in the commuting context.</li> </ol> <p>In order to analyse the answers given to the questions, it is recommended to consult also the "PAsCAL WP6 Pilots Surveys" dataset, which contains all questions and possible answers.</p>
Green Infrastructure Connectivity in Valladolid City
<p>Calculation of the shortest distance (linear) between the population in the NBS (line type), and the NBS location centroid. This social indicator evaluates the accessibility of urban green spaces for population in terms of total distance or time.</p> <p>Exante file shows the baseline. Expost, after the implementation of the UrbanGreenUP project. </p>
Air mass trajectory and connectivity data generated with tropolink (Richard et al., 2023)
<p>Archive containing trajectory and connectivity data generated with tropolink for the preparation of the manuscript Richard et al. (2023, <a href="https://doi.org/10.1029/2023GH000885">https://doi.org/10.1029/2023GH000885</a>), as well as the corresponding specifications (node coordinates, dates and other tropolink options). The archive contains specifications, trajectories and connectivities for the three applications presented in the manuscript:</p><p>- the study of airborne connectivity between areas of production of sugar beet, with starting altitude equal to 250m, 500m and 750m above ground level;</p><p>- the study of airborne connectivity between potyvirus populations;</p><p>- the study of invasion risk of Spodoptera frugiperda in Europe, North Africa and western Asia;</p><p> </p><p>Web application tropolink: https://tropolink.fr/</p><p>Associated gitlab: https://forgemia.inra.fr/tropo-group</p><p>Accompanying wiki: https://forgemia.inra.fr/tropo-group/tropolink/-/wikis</p><p>R code for analyzing tropolink output: https://forgemia.inra.fr/tropo-group/tropolink/-/wikis/Examples</p><p>Richard H., Martinetti D., Lercier D., Fouillat Y., Hadi B., Elkahky M., Ding J., Michel L., Morris C.E., Berthier K., Maupas F., <br>Soubeyrand S. (2023). Computing geographical networks generated by air-mass movement. GeoHealth 7:e2023GH000885. <a href="https://doi.org/10.1029/2023GH000885">https://doi.org/10.1029/2023GH000885</a>.</p>
Cortical cell assemblies and their underlying connectivity: an in silico study
<p>Dataset linked to the article with the same title</p> <ul> <li>simulation_config.zip: contains SONATA config files needed to re-run an exemplary simulation (after downloading the circuit from <a href="https://zenodo.org/record/7930275">10.5281/zenodo.7930275</a>). In order to run it, paths in circuit_config.json, and simulation_config.json have to be updated!</li> <li>assemblies.h5 is a dataset produced (and can be easily opened) by: <a href="https://zenodo.org/record/8112725">assemblyfire</a> (see GitHub README for more documentation) and serves as a basis for the manuscript. As the assemblies are the results of an unsupervised clustering (of high activity time bins) the resulting labels are not necessary meaningful. In the manuscript we have ordered the assemblies (from early to late responding ones, and from pattern A to J responsive ones) but the HDF5 file still stores the original labels. The mapping from the "random" labels to the ones presented in our article is stored in the config files on GitHub.</li> </ul> <p><em>The development of this dataset was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.</em></p>
Connecting the multiple dimensions of global soil fungal diversity
<p>How the multiple facets of soil fungal diversity vary worldwide remains virtually unknown, hindering the management of this essential species-rich group. By sequencing high-resolution DNA markers in over 4000 topsoil samples from natural and human-altered ecosystems across all continents, we illustrate the distributions and drivers of different levels of taxonomic and phylogenetic diversity of fungi and their ecological groups. We show the impact of precipitation and temperature interactions on fungal local species richness (alpha diversity) across different climates. Our findings reveal how temperature drives fungal compositional turnover (beta diversity) and phylogenetic diversity, linking them with regional species richness (gamma diversity). Our work integrates fungi into the principles of global biodiversity distribution and presents detailed maps for biodiversity conservation and modeling of global ecological processes.</p> <p><strong>### Data overview</strong></p> <p>These datasets contain comprehensive estimates of alpha, beta, and gamma diversity. The data are provided in two formats: TIFF (Tagged Image File Format) and GeoPackage formats, which are commonly used to store geospatially-referenced data.</p> <p><strong>Alpha Diversity</strong>:</p> <ul> <li>`<em>Alpha_S_</em>*` files: These files contain estimates of alpha diversity (local species diversity) for each grid cell of a raster file.</li> <li>`<em>Alpha_AOA_</em>*` files: These files outline the 'Area of Applicability' for the alpha diversity estimates.</li> <li>`<em>Alpha_Uncertainty_</em>*` files: These files contain data related to the uncertainty of the alpha diversity predictions. Uncertainty here represents the range or degree of error associated with the diversity estimates.</li> <li> `<em>Alpha_Hotspots_and_ProtectedAreas</em>` contains information on fungal diversity hotspots and their area under protection (based on IUCN classification). 'Hotspots' are areas with exceptionally high alpha diversity.</li> </ul> <p><strong>Beta Diversity</strong>:</p> <ul> <li>`<em>Beta_</em>*` files: These files include results of beta diversity analyses: maps of global compositional dissimilarity among soil fungal communities and maps of compositional turnover rate.</li> </ul> <p><strong>Other files</strong>:</p> <ul> <li>`<em>EcM_and_AM_GlobalDistribution</em>`: the global distribution of areas with high richness of ectomycorrhizal and arbuscular mycorrhizal fungi.</li> <li>`<em>Ecoregions_Alpha,Beta,Gamma_Diversities</em>`: estimates of alpha, beta, and gamma diversity at the level of ecoregion cf. Tedersoo et al., 2022 (DOI:10.1111/gcb.16398).</li> </ul> <p> </p> <p><strong>### Data description</strong></p> <p>Alpha diversity, which is a measure of local species richness (number of Operational Taxonomic Unit (OTU) representing distinct taxa, roughly corresponding to species level). Alpha diversity is represented by the residuals from a model adjusting for sequencing depth, with zero equating to the average OTU richness in the training data set.</p> <p><br> `<strong>Alpha_S_AllFungi_Consensus.tif</strong>`: This file provides consensus estimates for total fungal alpha diversity.<br> Within the file, there are two types of consensus estimates:</p> <ul> <li> <em>AvgW</em> - weighted consensus estimates for alpha diversity. The weighting takes into account both the area of applicability and the goodness-of-fit for the model used to generate the estimates.</li> <li> <em>Avg</em> - non-weighted consensus estimates for alpha diversity. Unlike <em>AvgW</em>, these estimates give equal weight to all models regardless of their goodness-of-fit or area of applicability.</li> </ul> <p><br> `<strong>Alpha_AOA_*</strong>`: Files containing Area of Applicability information:</p> <ul> <li> A raster value of '1' represents areas that are outside the Area of Applicability</li> <li> A raster value of '2' denotes areas that are inside the Area of Applicability</li> </ul> <p><br> In the files containing prediction uncertainties (`<strong>Alpha_Uncertainty_*</strong>`), two types of data are presented to quantify the amount of uncertainty in model predictions, each represented by a different band:</p> <ul> <li>The SD band represents the standard deviation of predictions based on different folds of cross-validation. A larger standard deviation indicates greater variability in the predictions.</li> <li>The IQR band represents the interquartile range (the difference between the upper and lower quartiles) of predictions. The wider the IQR, the greater variability in the predictions.</li> </ul> <p><br> `<strong>Alpha_Hotspots_and_ProtectedAreas.tif</strong>`: This file provides information on regions of exceptionally high species richness, referred to as 'hotspots', along with information about protected areas. Hotspots are identified as the top 2.5% quantiles of the richest grid cells on the map in terms of OTU richness.</p> <ul> <li><em>IUCN_1_4</em> - terrestrial protected areas that fall into categories I-IV, as classified by the International Union for Conservation of Nature (IUCN). These categories typically represent areas with high levels of protection, often prohibiting extractive and destructive activities to preserve biodiversity.</li> <li><em>IUCN_all</em> - all terrestrial protected areas as recorded in the World Database on Protected Areas (WDPA) database v.1.6. It includes a wider range of protected areas beyond the categories I-IV.</li> <li><em>All_Avg</em> - Hotspots of total fungal alpha diversity, based on the consensus map</li> <li><em>GSM_All</em> - Hotspots of total fungal alpha diversity, based on the GSMc dataset</li> <li><em>GSM_EcM</em> - Hotspots of ectomycorrhizal alpha diversity</li> <li><em>GSM_AM</em> - Hotspots of arbuscular mycorrhizal alpha diversity</li> <li><em>GSM_AgarNM</em> - Hotspots of non-EcM Agaricomycetes alpha diversity</li> <li><em>GSM_Mold</em> - Hotspots of mold alpha diversity</li> <li><em>GSM_Pathog</em> - Hotspots of opportunistic human parasitic fungal alpha diversity</li> <li><em>GSM_OHP</em> - Hotspots of putative pathogenic fungal alpha diversity</li> <li><em>GSM_Unicel</em> - Hotspots of unicellular, non-yeast fungal alpha diversity</li> <li><em>GSM_Yeast</em> - Hotspots of yeast alpha diversity</li> <li><em>GSMc_PD</em> - Hotspots of phylogenetic alpha diversity</li> <li><em>GSM_PDst</em> - Hotspots of phylogenetic dispersion</li> </ul> <p><br> `<strong>EcM_and_AM_GlobalDistribution.tif</strong>`: To illustrate the worldwide distribution of ectomycorrhizal (EcM) and arbuscular mycorrhizal (AM) fungi, we have categorized their richness into three distinct groups with low (1), medium (2), and high (3) alpha diversity. These categories have been encoded in the raster file using a bitcode system. Specifically, a value of '9' indicates that both EcM and AM fungal communities have low alpha diversity, while a value of '27' signifies that both groups of fungi are OTU-rich To assist with interpretation, a color legend has been provided in a separate QML style file (`<strong>EcM_and_AM_GlobalDistribution.qml</strong>`). This should be automatically recognized by geographic information system software, such as QGIS, to aid in visual analysis.</p> <p><br> `<strong>Beta_Taxonomic_AllFungi.tif</strong>` and `<strong>Beta_Phylogenetic_AllFungi.tif</strong>`: These files quantify the degree of difference in OTU composition of fungal communities. The measurements are based on the Generalized Dissimilarity Modelling (GDM) framework, as described by Mokany et al., 2022 (DOI:10.1111/geb.13459). Each file provides a different perspective on beta diversity: taxonomic (which is the change in species composition between different locations), and phylogenetic (the change in phylogenetic lineage composition between different locations). Each of these raster files contains three bands, with each band representing a scaled axis from a Principal Component Analysis (PCA) of the GDM-transformed environmental predictors.</p> <p><br> `<strong>Beta_LocalTurnover.tif</strong>`: This file contains estimates of local turnover in fungal communities composition estimated as the median expected compositional dissimilarity (taxonomic or phylogenetic) between each location and its closest neighbors within a 150 km radius. In addition, interquartile range (IQR) of dissimilarities is also provided.</p> <p> </p> <p>`<strong>Ecoregions_Alpha,Beta,Gamma_Diversities.gpkg</strong>`: Median alpha, beta, and gamma diversity estimates within ecoregions.</p> <ul> <li><em>Ecoregion</em> - Ecoregion name (cf. Tedersoo et al., 2022, DOI:10.1111/gcb.16398)</li> <li><em>area</em> - Ecoregion area, m<sup>2</sup></li> <li><em>Alpha_S_AllFungi_Consensus</em> - Richness of all fungi (S'<sub>tot</sub>), consensus map</li> <li><em>Alpha_S_AllFungi_GSMc</em> - Richness of all fungi (S'<sub>GSMc</sub>), based on GSMc dataset</li> <li><em>Alpha_S_EcM_GSMc</em> - Richness of ectomycorrhizal fungi (S'<sub>ecm</sub>)</li> <li><em>Alpha_S_AM_GSMc</em> - Richness of arbuscular mycorrhizal fungi (S'<sub>am</sub>)</li> <li><em>Alpha_S_NMA_GSMc</em> - Richness of non-EcM Agaricomycetes (S'<sub>nma</sub>)</li> <li><em>Alpha_S_Mold_GSMc</em> - Richness of molds (S'<sub>mold</sub>)</li> <li><em>Alpha_S_OHP_GSMc</em> - Richness of opportunistic human parasitic fungi (S'<sub>ohp</sub>)</li> <li><em>Alpha_S_Path_GSMc</em> - Richness of putative pathogenic fungi (S'<sub>path</sub>)</li> <li><em>Alpha_S_Ucel_GSMc</em> - Richness of unicellular, non-yeast fungi (S'<sub>ucel</sub>)</li> <li><em>Alpha_S_Yeast_GSMc</em> - Richness of yeasts (S'<sub>yeast</sub>)</li> <li><em>Alpha_SESPD_GSMc</em> - Phylogenetic dispersion of fungal communities (SES<sub>PD</sub>)</li> <li><em>Beta_Taxonomic_Median</em> - Median taxonomic dissimilarity of fungal communities (Simpson's index)</li> <li><em>Beta_Taxonomic_IQR</em> - Interquartile range of taxonomic dissimilarities of fungal communities</li> <li><em>Beta_Phylogenetic_Median</em> - Median phylogenetic dissimilarity of fungal communities</li> <li><em>Beta_Phylogenetic_IQR</em> - Interquartile range of phylogenetic dissimilarities of fungal communities</li> <li><em>Gamma_AllFungi</em> - Gamma diversity (regional species richness) for all fungi (G<sub>tot</sub>)</li> <li><em>Gamma_EcM</em> - Gamma diversity of ectomycorrhizal fungi (G<sub>ecm</sub>)</li> <li><em>Gamma_AM</em> - Gamma diversity of arbuscular mycorrhizal fungi (G<sub>am</sub>)</li> <li><em>Gamma_NMA</em> - Gamma diversity of non-EcM Agaricomycetes (G<sub>nma</sub>)</li> <li><em>Gamma_Mold</em> - Gamma diversity of molds (G<sub>mold</sub>)</li> <li><em>Gamma_Path</em> - Gamma diversity of opportunistic human parasitic fungi (G<sub>ohp</sub>)</li> <li><em>Gamma_OHP</em> - Gamma diversity of putative pathogenic fungi (G<sub>path</sub>)</li> <li><em>Gamma_Ucel</em> - Gamma diversity of unicellular, non-yeast fungi (G<sub>ucel</sub>)</li> <li><em>Gamma_Yeast</em> - Gamma diversity of yeasts (G<sub>yeast</sub>)</li> </ul> <p> </p> <p><strong>### Source code</strong></p> <p>The code used for data analysis and visualization of the main results of the study are available at GitHub:</p> <p><a href="https://github.com/Mycology-Microbiology-Center/Global_fungal_diversity">https://github.com/Mycology-Microbiology-Center/Global_fungal_diversity</a></p> <p> </p>
Public perceptions of an avian reintroduction aiming to connect people with nature
<p>Full raw dataset (quantitative and qualitative) and R code for quantitative analyses, associated with the publication "Public perceptions of an avian reintroduction aiming to connect people with nature" <em>People and Nature</em>. Survey responses are presented here anonymised. Please note that where the term 'Proactive' sample is used this is referred to as the 'Self selecting' sample in the associated journal article.</p>
"Outgassing Composition of the Murchison Meteorite: Implications for Volatile Depletion of Planetesimals and Interior-Atmosphere Connections for Terrestrial Exoplanets" Data Repository
<p>This repository contains the data files, analysis Jupyter notebooks and figures from Thompson et al. 2023 "Outgassing Composition of the Murchison Meteorite: Implications for Volatile Depletion of Planetesimals and Interior-Atmosphere Connections for Terrestrial Exoplanets"</p>
NDC-SDG Connections: Data on first NDC submissions (V1)
<p>NDC-SDG Connections is a joint initiative of the German Institute of Development and Sustainability (IDOS) and the Stockholm Environment Institute (SEI). The research and visualisation project aims at illuminating synergies between the 2030 Agenda for Sustainable Development and the Paris Agreement, and at identifying entry points for coherent policies that promote just, sustainable and climate-smart development.</p> <p>The objective of the NDC-SDG Connections is to: foster a dialogue on meaningful interaction between the 2030 Agenda and the Paris Agreement, globally and at the national level; to increase transparency with easy accessibility to all climate activities; and to cultivate learning and catalyse partnerships between countries and other actors to raise the ambition of future NDCs.<br> <br> With its second version, the NDC-SDG Connections project opened its data for public re-use. The data on first NDC submissions (V1) is provided in the following formats:</p> <ul> <li>single .csv files (per data per SDG)</li> <li>zip .csv file (data per SDG for all SDG in one zip)</li> <li>.xlxs file (Excel)</li> </ul> <p><strong>Visit the Online Data Visualisation to interact directly with the data: www.NDC-SDG.info</strong></p> <p>Additional files:</p> <ul> <li>.pdf file documenting the methodological framework including the coding and data validation process of the NDC-SDG Connections project</li> <li>.csv file with all NDCs included into the analysis (V1)</li> </ul> <p><br> <strong>Note: This data set contains data for first NDC submissions (V1). </strong>The terms ‘First’ and ‘Updated’ do not fully follow the UNFCCC nomenclature. For most countries, updated NDCs are called ‘First updated NDC’ or ‘Enhanced NDCs’, while some countries call their updated NDCs for ‘Second NDC’. In order to make it comprehensible, the tool developers have chosen to distinguish between ‘First’ and ‘Updated’. Detailed description of which version is counted as ‘First’ and which as ‘Updated’ has been documented in the data.</p> <p> </p>
Structural and Molecular Analysis of Adult Mouse Astrocytes and Vascular Connectivity in the Cortex and Hippocampus
<p>After image acquisition (0-RAW_CL230331_E2_serie1) and deconvolution (1-Deconvolved_CL230331_E2_serie1) using confocal microscopy and the SVI Huygens software,respectively, the image processing was conducted using Imaris, Fiji, and Matlab software. This process involved a sequence of manual operations (2-Imaris_surfaces_CL230331_E2_serie1) and custom Groovy scripts (5-Groovy scripts).</p> <p>The dataset analysis (3-Imaris_final_CL230331_E2_serie1_ims) allowed for a deeper investigation of morphological and molecular properties of adult mouse astrocytes (4-Image analysis_CL230331_E2_serie1) in two brain regions, the Isocortex and the Hippocampus, known to be interconnected to support multiple cognitive functions.</p>
Riparian disturbance restricts connectivity of Appalachian stream salamander populations at the Coweeta Hydrologic Laboratory
Human populations are rapidly expanding and encroaching on previously undisturbed habitats. Stream salamanders in the southern Appalachian Mountains are a diverse and abundant group threatened by rapid exurban development in high-elevation watersheds. Previous research has demonstrated the sensitivity of salamanders to urbanization, but little research exists describing the mechanisms behind population declines and extirpations. Appalachian stream salamanders are adapted to forested streams with dense overstory and little light, yet following urbanization, light gaps associated with land clearing emerge. Light avoidance behaviors may alter movement behaviors of salamanders, fragmenting populations on opposite sides of light gaps. To study the effects on riparian disturbance on salamanders we established 6 experimental sites with canopy gaps ranging from 13m to 85m in stream length and 2 control sites lacking canopy gaps in May of 2010. Animals were collected within these plots, marked, and translocated to the plot on the opposite side of the gap. To establish detection probabilities in the absence of translocation, we established an additional 10m plot in the forest at each site where individuals were captured, marked, and re-released within this area. Recaptured individuals were measured and in some cases re-marked if those individuals had returned to their capture location.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.