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5,526 results for “information”
Dataset from "Matthieu Delescluse and Christophe Pouzat (2006) Efficient spike-sorting of multi-state neurons using inter-spike intervals information Journal of Neuroscience Methods 150: 16-29."
<p>The dataset (in HDF5 format) used in Delescluse and Pouzat (2006) Efficient spike-sorting of multi-state neurons using inter-spike intervals information Journal of Neuroscience Methods 150: 16-29. arXiv:q-bio/0505053. See this reference for recording details. Data collected by Matthieu Delescluse. Briefly, 4 channels (data sets Channel_0,1,2,3, organized in a group called 'ExtracellularData'; extracellular recordings along the Purkinje cell layer of a young rat cerebellar cortex slice) of a linear 'Michigan' (now Neuronexus) probe and a loose cell-attached recording (data set Reference, in group 'CellAttached') from one of the Purkinje cells that is also extracellularly recorded: a 'ground truth' for spike sorting algorithms. Each group has three attributes: SamplingRate, HighPass and LowPass. The last two are the filter settings used prior to A/D conversion. These attributes have identical values for the 5 traces (2 groups): the data were sampled at 15 kHz, high-passed at 300 Hz and low-passed at 5 kHz.</p>
INFORMATE Project - CHORUS Report Summaries - 20231106
<p>These data provide a summary of the All, Author Affiliation, and Dataset Reports generated by the <a href="https://dashboard.chorusaccess.org/">CHORUS Dashboard</a> for three agencies: the U.S. National Science Foundation, U.S. Geological Survey, and the U.S. Agency for International Development. The reports summarized here was collected on November 6-7, 2023 as part of the INFORMATE Project funded by NSF.</p><p>The columns are:</p><p>Column Definition</p><p>agency The funding agency [NSF, USGS, or USAID]</p><p>date. The date of data retrieval (YYYYMMDD)</p><p>report. The report [all, authors, datasets]</p><p>Property Name of the column in the input file</p><p>count Number of values (rows) of the property</p><p>unique Number of unique values of the property</p><p>top Most common value of the property</p><p>freq Number of occurrences (frequency) of the most common value</p><p>Count % The percentage of rows that include the property</p>
[Supplementary Information] Can LCA be FAIR? – Assessing the status quo and opportunities for FAIR data sharing
<p>This is the supplementary information related to a the manuscript - 'Can LCA be FAIR?' - Assessing the status quo and opportunities for FAIR data sharing. The purpose of this study is to assess the status quo of data sharing in LCA in relation to the FAIR data principles (Findability, Accessibility, Interoperability and Re-use).</p><p>The supplementary information consists of three files:</p><p><strong>SI 1</strong> - How the life cycle inventory is shared in relation to the FAIR data principles in 25 peer reviewed LCA journal articles between 2018 -2022.</p><p><strong>SI 2</strong> - Review of ten data management plans of EU Horizon Europe projects in relation to LCA to assess the recommendations on the implementation of FAIR principles.</p>
Collection of spatial information and maps of human past and environment in the Uralic languages speaker area
<p>The collection of spatial information and maps of the past and environment in the Uralic languages speaker area consists excessive amount of multidisciplinary data related to the vast region extending from Eastern Europe to Siberia, encompassing countries like Russia, Finland, and parts of Scandinavia. Uralic speakers are predominantly found in this region, with historical roots in areas around the Ural Mountains and adjacent territories. These datasets can be integrated for multidisciplinary purposes, allowing to explore human-environment interactions, migration patterns, and cultural evolution over time. Datasets are collected initially by the BEDLAN team <a href="https://bedlan.net/">https://bedlan.net/</a> - a research group specialized in various disciplines - linguists, archaeologists, geneticists, and geographers. The data collection and mapmaking have grown beyond the initial stages (publications, applications, exhibitions), hence collaborative effort for data publishing is now crucial. As the data collections and mapmaking continue to evolve dynamically together with ongoing projects, the current repository will be updated accordingly.</p>
Supplemental Information - Allosteric activation of the co-receptor BAK1 by the EFR receptor kinase initiates immune signaling
<p>This folder contains</p> <p>1) maps of plasmids</p> <p>2) files of phylogenetic analysis </p> <p>3) Replication information</p> <p>4) Image cropping information</p> <p>5) Gene IDs and protein sequences</p> <p>that are part of the manuscript "Allosteric activation of the co-receptor BAK1 by the EFR receptor kinase initiates immune signaling"</p>
Local plot information observed on LandKlif plots during vegetation survey 2019
<p><span>LandKlif local plot information observed on site during vegetation survey 2019, including vegetation height, slope, aspect, proximity to hedge / forest edge / water, intensity of use (only for meadows), and further information on plot habitat.</span></p> <p><span>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.</span></p>
Swiss public's acceptance and sustainability perceptions of food produced with chemical, digital and mechanical weed control measures and the influence of information source on technology perception in agriculture
<p><span>This data was obtained from an online survey conducted with the Swiss public from the two biggest language regions (German and French) in Switzerland. The survey was conducted in February 2023. Participants were recruited through a professional panel provider and quotas were used for age, gender and language region. The final sample contained </span><span>542 respondents. </span><span>In the first part of the survey, respondents provided basic sociodemographic information. In the second part, their sustainability perceptions regarding four different weed management practices (full-surface spraying, hoeing machine, spot spraying and precise spraying) were investigated. Respondents were then assigned to one of five information source groups, in which information on a hoeing and a milking robot was presented, using 5 different information sources (male/female farmer, male/female scientist, no source). Technology perception was assessed using several questions and aspects. Finally, respondents answered several questions assessing their attitudes towards the perception of farmers, food technology neophobia, chemophobia and the importance of naturalness. The survey can be used and adapted to different contents, aiming to investigate public perception of smart farming technologies and the influence of information sources on technology perception. </span></p>
OpenCitations Meta RDF dataset of agent roles metadata and its provenance information
<p>This dataset is a specialized subset of the OpenCitations Meta RDF data, focusing exclusively on data related to <strong>agent roles</strong> of bibliographic resources<strong> </strong>(<a href="http://purl.org/spar/pro/RoleInTime" target="_blank" rel="noopener">http://purl.org/spar/pro/RoleInTime</a>). These agents can be authors, editors, or publishers. It contains all the metadata and its provenance information, structured specifically around agent roles, in JSON-LD format.</p> <p>The inner folders are named through the <strong>supplier prefix</strong> of the contained entities. It is a prefix that allows you to recognize the entity membership index (e.g., OpenCitations Meta corresponds to <strong>06*0</strong>).</p> <p>After that, the folders have <strong>numeric names</strong>, which refer to the range of contained entities. For example, the 10000 folder contains entities from 1 to 10000. Inside, you can find the <strong>zipped </strong>RDF data.</p> <p>At the same level, additional folders containing the <strong>provenance </strong>are named with the same criteria already seen. Then, the 1000 folder includes the provenance of the entities from 1 to 1000. The provenance is located inside a folder called <strong>prov</strong>, also in zipped JSON-LD format.</p> <p>For example, data related to the entity is located in the folder /ar/06250/10000/1000/1000.zip, while information about provenance in /ar/06250/10000/1000/prov/se.zip</p> <p>Additional information about OpenCitations Meta at the <a href="https://opencitations.net/meta" target="_blank" rel="noopener">official webpage</a>.</p>
OpenCitations Meta RDF dataset of page numbers metadata and its provenance information
<p>This dataset is a specialized subset of the OpenCitations Meta RDF data, focusing exclusively on data related to <strong>page numbers</strong> of bibliographic resources, known as <strong>manifestations </strong>(<a href="http://purl.org/spar/fabio/Manifestation" target="_new">http://purl.org/spar/fabio/Manifestation</a>). It contains all the bibliographic metadata and its provenance information, structured specifically around manifestations (page numbers), in JSON-LD format.</p> <p>The inner folders are named through the <strong>supplier prefix</strong> of the contained entities. It is a prefix that allows you to recognize the entity membership index (e.g., OpenCitations Meta corresponds to <strong>06*0</strong>).</p> <p>After that, the folders have <strong>numeric names</strong>, which refer to the range of contained entities. For example, the 10000 folder contains entities from 1 to 10000. Inside, you can find the <strong>zipped </strong>RDF data.</p> <p>At the same level, additional folders containing the <strong>provenance </strong>are named with the same criteria already seen. Then, the 1000 folder includes the provenance of the entities from 1 to 1000. The provenance is located inside a folder called <strong>prov</strong>, also in zipped JSON-LD format.</p> <p>For example, data related to the entity is located in the folder /br/06250/10000/1000/1000.zip, while information about provenance in /br/06250/10000/1000/prov/se.zip</p> <p>Additional information about OpenCitations Meta at the <a href="https://opencitations.net/meta" target="_blank" rel="noopener">official webpage</a>.</p>
OpenCitations Meta RDF dataset of identifiers metadata and its provenance information
<p>This dataset is a specialized subset of the OpenCitations Meta RDF data, focusing exclusively on data related to <strong>identifiers </strong>(<a href="http://purl.org/spar/datacite/Identifier" target="_blank" rel="noopener">http://purl.org/spar/datacite/Identifier</a>) of bibliographic resources. It contains all the metadata and its provenance information, structured specifically around identifiers, in JSON-LD format.</p> <p>The inner folders are named through the <strong>supplier prefix</strong> of the contained entities. It is a prefix that allows you to recognize the entity membership index (e.g., OpenCitations Meta corresponds to <strong>06*0</strong>).</p> <p>After that, the folders have <strong>numeric names</strong>, which refer to the range of contained entities. For example, the 10000 folder contains entities from 1 to 10000. Inside, you can find the <strong>zipped </strong>RDF data.</p> <p>At the same level, additional folders containing the <strong>provenance </strong>are named with the same criteria already seen. Then, the 1000 folder includes the provenance of the entities from 1 to 1000. The provenance is located inside a folder called <strong>prov</strong>, also in zipped JSON-LD format.</p> <p>For example, data related to the entity is located in the folder /id/06250/10000/1000/1000.zip, while information about provenance in /id/06250/10000/1000/prov/se.zip</p> <p>Additional information about OpenCitations Meta at the <a href="https://opencitations.net/meta" target="_blank" rel="noopener">official webpage</a>.</p>
OpenCitations Meta RDF dataset of bibliographic resources metadata and its provenance information
<div> <p>This dataset is a specialized subset of the OpenCitations Meta RDF data, focusing exclusively on data related to <strong>bibliographic resources </strong>(<a href="http://purl.org/spar/fabio/Expression" target="_blank" rel="noopener">http:///purl.org/spar/fabio/Expression</a>). It contains all the metadata and its provenance information, structured specifically around bibliographic resources, in JSON-LD format.</p> <p>The inner folders are named through the <strong>supplier prefix</strong> of the contained entities. It is a prefix that allows you to recognize the entity membership index (e.g., OpenCitations Meta corresponds to <strong>06*0</strong>).</p> <p>After that, the folders have <strong>numeric names</strong>, which refer to the range of contained entities. For example, the 10000 folder contains entities from 1 to 10000. Inside, you can find the <strong>zipped </strong>RDF data.</p> <p>At the same level, additional folders containing the <strong>provenance </strong>are named with the same criteria already seen. Then, the 1000 folder includes the provenance of the entities from 1 to 1000. The provenance is located inside a folder called <strong>prov</strong>, also in zipped JSON-LD format.</p> <p>For example, data related to the entity is located in the folder /br/06250/10000/1000/1000.zip, while information about provenance in /br/06250/10000/1000/prov/se.zip</p> <p>Additional information about OpenCitations Meta at the <a href="https://opencitations.net/meta" target="_blank" rel="noopener">official webpage</a>.</p> <p> </p> </div>
Occurrences records of Herichthys labridens (Cichliformes: Cichlidae), with associated habitat information, in the Media Luna spring, San Luis Potosí, Mexico
<h2><strong>Introduction</strong></h2> <blockquote> <p>Occurrence records of the endemic cichlid <em>Herichthys labridens</em>, by adult and juvenile life stages, during three summer events (years of 1999, 2009, and 2019), in the Media Luna spring, San Luis Potosí Mexico. </p> </blockquote> <h2><strong>Material and Methods </strong></h2> <blockquote> <p>The occurrence records, ordered by adult and juvenile life stages, were obtained from two sources. For the summer of 1999, data were downloaded from the literature (Palacio-Núñez et al., 2010). For subsequent events, we recorded new data from 66 underwater transects distributed among 14 sectors (S1 to S14) in the Media Luna spring. We followed the method of Palacio-Núñez (2007), which maintained the transect location and sector boundaries of the summer of 1999 (Fig. 1a). The 20 m² transects were placed transversely to the current, from the edge to the central part of the canal (Fig. 1b). This sampling design was selected to meet two basic assumptions for studies of spatial distribution and habitat suitability: (1) the observations within the area are true and, (2) these observations delimit the initial position of the recorded individuals (Buckland & Elston, 1993). The analysis of the spatial information of the sectors, the underwater transects, and the delimitation of the water surface was performed using the QGIS® software version 3.4.8 (Menke, 2019).</p> <p> </p> <p><strong>Figure 1</strong>. <a href="https://zenodo.org/api/records/14231104/draft/files/Sector%20boundary_Transect%20location%20and%20sampling_Media%20Luna%20spring.jpeg/content" target="_blank" rel="noopener noreferrer">Sector boundary_Transect location and sampling_Media Luna spring.jpeg</a>. (a) Location of the transects in the Media Luna spring, Mexico. (b) Design scheme of the sampling transect; a CPVC pipe was used to give width to the edges of the transect and a nylon rope was attached to each side of the pipes to demarcate the length of the transect. Floating rubber buoys were added to the transects (at the edge towards the center of the canal) to prevent them from sinking into the sediment and to locate them among the vegetation. Transect scheme: Jorge Palacio-Núñez.</p> <p><br>In the summer events where we worked in field, we recorded the spatial location (i.e., GPS coordinates) of each individual and its life stage by direct observation with snorkel equipment and using a Garmin etrex device. The recorded information included the data of water depth and related underwater coverage. It is important to mention that, to prevent a repeat observation of the same organism or to ommit any individual, the transect was swaped slowly and in one direction only (i.e., from the center of the canal to the shore). We also used underwater cameras to validate the information. In adittion, the characterization of <em>H. labridens</em> individuals by life stage was performed by approximate size. For this purpose, previous studies on the life history and biology of the species were reviewed (Miller et al., 2005; De La Maza-Benignos & Lozano-Vilano, 2013). It is worth mentioning that, during fieldwork, we avoided manipulation, damage, or unnecessary capture of the fish (e.g., Prchalová et al., 2009).</p> <p><br>The databases by life stage were organized for each summer event, where, each observation record was included along with the associated habitat conditions. Subsequently, we depurated each database to remove atypical spatial data, data without information, incomplete data, or data with duplicate coordinates (García-Roselló et al., 2014). Then, we performed spatial filtering of the remaining records to validate those that were within the study area, and to prevent that two or more points were within 0.1 m of each other. These steps of our analysis were performed using the software Qgis® version 3.28.4 and Rstudio® (Rstudio team, 2020). Subsequently, with the data set that included fish records, water depth, and underwater coverage variables, we performed a final environmental filter to rule out atypical records. This exploration was performed in Rstudio ® using the outliers function, starting from the lowest and highest quantiles.</p> </blockquote> <h2><strong>Results</strong></h2> <blockquote> <p>The final filtered databases were organized by life stage and summer event:</p> <p><strong>Adult: </strong></p> <table> <tbody> <tr> <td>Summer event</td> <td>Database</td> </tr> <tr> <td>1999</td> <td><a href="https://zenodo.org/api/records/14231104/draft/files/Occurrences_records_H_labridens_Adult_1999_Palacio-N%C3%BA%C3%B1ez%20et%20al.,%202010.csv/content" target="_blank" rel="noopener noreferrer">Occurrences_records_H_labridens_Adult_1999_Palacio-Núñez et al., 2010.csv</a></td> </tr> <tr> <td>2009</td> <td><a href="https://zenodo.org/api/records/14231104/draft/files/Occurrences_records_H_labridens_Adult_2009_Field%20work.csv/content" target="_blank" rel="noopener noreferrer">Occurrences_records_H_labridens_Adult_2009_Field work.csv</a></td> </tr> <tr> <td>2019</td> <td><a href="https://zenodo.org/api/records/14231104/draft/files/Occurrences_records_H_labridens_Adult_2019_Field%20work.csv/content" target="_blank" rel="noopener noreferrer">Occurrences_records_H_labridens_Adult_2019_Field work.csv</a></td> </tr> </tbody> </table> <p><strong> Juvenile:</strong></p> <table> <tbody> <tr> <td>Summer event</td> <td>Database</td> </tr> <tr> <td>1999</td> <td><a href="https://zenodo.org/api/records/14231104/draft/files/Occurrences_records_H_labridens_Juvenile_1999_Palacio-N%C3%BA%C3%B1ez%20et%20al.,%202010.csv/content" target="_blank" rel="noopener noreferrer">Occurrences_records_H_labridens_Juvenile_1999_Palacio-Núñez et al., 2010.csv</a></td> </tr> <tr> <td>2009</td> <td><a href="https://zenodo.org/api/records/14231104/draft/files/Occurrences_records_H_labridens_Juvenile_2009_Field%20work.csv/content" target="_blank" rel="noopener noreferrer">Occurrences_records_H_labridens_Juvenile_2009_Field work.csv</a></td> </tr> <tr> <td>2019</td> <td><a href="https://zenodo.org/api/records/14231104/draft/files/Occurrences_records_H_labridens_Juvenile_2019_Field%20work.csv/content" target="_blank" rel="noopener noreferrer">Occurrences_records_H_labridens_Juvenile_2019_Field work.csv</a></td> </tr> </tbody> </table> </blockquote> <p> </p> <blockquote> <p>These occurrence records for <em>H. labridens </em>are ready to be used in ecological niche modeling and spatial distribution studies. Also, these records can be used for other ecological and spatial studies, because each record (i.e., individual) included geoespatial coordinates, sector, location, and transect number. Also, we recorded information about the conditions of underwater coverage and water depth, which were asociated to each ocurrence record.</p> <p>For more information about several R codes where the previous databases can be used, visit the following repository URL: <a href="https://doi.org/10.5281/zenodo.7603557">https://doi.org/10.5281/zenodo.7603557</a>.</p> <p>Also, to download the UC and WDp variables to run the spatial and ecological modeling, visit the following repository URL: <a href="https://doi.org/10.5281/zenodo.7603890">https://doi.org/10.5281/zenodo.7603890</a>.</p> </blockquote>
DeepOrchidSeries: A Sentinel-2 Dataset to inform convolutional SDMs with twelve-month Sentinel-2 image time-series, Orchid family
<p><strong>Deep Species Distribution Modelling from Sentinel-2 Image Time-series: a Global Scale Analysis on the Orchid Family</strong> </p> <ul> <li><strong><em>DeepOrchidSeries</em></strong> dataset gathers Sentinel-2 image time-series around geolocated orchid occurrences. Seasonal evolutions of the habitats are captured in the twelve-month RGB/IR time-series with 640x640m spatial resolution. It allows novel Species Distribution Models (SDMs) coupled with convolutional networks to take advantage of both spatial and temporal information.</li> <li>Our <strong>associated article</strong> is describing the modeling choices made to shape this ambitious dataset. It is submitted to <a href="https://www.frontiersin.org/research-topics/18336/plant-biodiversity-science-in-the-era-of-artificial-intelligence">https://www.frontiersin.org/research-topics/18336/plant-biodiversity-science-in-the-era-of-artificial-intelligence</a>. We believe such global data, methods and scripts are valuable to the conservation ecology community and especially deep-SDMs users. To our knowledge, no similar ready-to-use dataset is available. In the article, the dataset's temporal dimension is proven to significantly improve SDMs performances.</li> <li><strong><em>sen2patch</em></strong> is the gitlab project gathering the code to create such dataset. It is available at <a href="https://gitlab.inria.fr/jestopin/sen2patch">https://gitlab.inria.fr/jestopin/sen2patch</a>.</li> <li><strong><em>DeepOrchidSeries.csv</em></strong> contains all occurrences-level information. <ul> <li>We advice to load it with: <pre><code class="language-python">import pandas as pd df = pd.read_csv("path/to/DeepOrchidSeries.csv", sep=';') df.columns ['gbifid', 'canonical_name', 'decimallatitude', 'decimallongitude', 'speciesKey', 'cell_index', 'bot_country', 'bot_code', 'lvl2_code', 'continent_code']</code></pre> <ul> <li>'gbifid' is the occurrences GBIF ID</li> <li>'canonical_name', is the species canonical name</li> <li>'decimallatitude', 'decimallongitude' are the species coordinates in decimal degrees</li> <li>'speciesKey' is the species GBIF unique identifier</li> <li>'cell_index' is a unique cell ID in a 0.0025° lon/lat grid partitioning the Earth (used to stratify train/val/test set by geographic blocks)</li> <li>'bot_country', 'bot_code', 'lvl2_code', 'continent_code' are geographic subdivisions defined in <a href="https://github.com/tdwg/wgsrpd">https://github.com/tdwg/wgsrpd</a> (code and string for WGSRPD level 1, the botanical countries)</li> </ul> </li> </ul> </li> <li> <p>Initial <a href="https://www.gbif.org/">GBIF</a> query DOI is <a href="http://https://doi.org/10.15468/dl.4bijtu">https://doi.org/10.15468/dl.4bijtu</a> (26 August 2019).</p> </li> <li><strong><em>DeepOrchidSeries.tar</em></strong> file contains the satellite image time-series and is available at <a href="https://lab.plantnet.org/deeporchidseries/">https://lab.plantnet.org/deeporchidseries/</a> <ul> <li><em>.tar</em> archive measure 286 GB and extends to 432 GB once decompressed.</li> <li>Image time-series relative tree paths are constructed from the occurrences unique GBIF IDs.</li> <li>For a given occurence <em>gbifid</em>, matching patches are located in: <em>final_dataset_by_gbifid/gbifid[-2:]/gbifid[-4:-2]</em>, <em>i.e.</em> in a first folder named with the <em>gbifid</em> last two numbers and a subfolder with the previous two ones. Example: the time-series files matching occurrence 2236837714 are located at <em>final_dataset_by_gbifid/14/77/</em>. </li> <li>Image time-series are composed of twelve 16 bits RGB <em>.png</em> and twelve 16 bits IR <em>.png</em> files containing data identical to the original L1C products, no lossy compression was made. There are one RGB and one IR .png file per month.</li> <li>Patches from month MM/YYYY of occurrence <em>gbifid</em> are named<em> </em><em>RGB_YYYY_MM_gbifid_.png</em> and <em>IR0_YYYY_MM_gbifid_.png</em>.</li> </ul> </li> <li><em><strong>models.zip</strong></em> is the archive containing the four PyTorch models weights described in our article and<strong><em> </em></strong><em><strong>inception_env.py</strong></em> the used Inception V3 architecture. <em><strong>index.json</strong></em> contains the dictionnary linking the models class indexes from 0 to 14128 with our labels <em>speciesKey</em>: {"class_index":speciesKey}.</li> </ul> <p> </p> <ul> <li><strong>ACKNOWLEDGMENTS</strong>: We warmly thank Alexander Zizka et al. for providing us the geographically and taxonomically curated set of Orchids occurrences. This dataset contains modified Copernicus Sentinel data and Copernicus Service information (2018). Sentinel-2 MSI data used were available at no cost from ESA Sentinels Scientific Data Hub.</li> </ul>
Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors: V4.NA.02.MAPLE
<p>We estimate ground-level fine particulate matter (PM<sub>2.5</sub>) total and compositional mass concentrations over North America by combining Aerosol Optical Depth (AOD) retrievals from the NASA MODIS, MISR, and SeaWIFS instruments with the GEOS-Chem chemical transport model, and subsequently calibrated to regional ground-based observations of both total and compositional mass using Geographically Weighted Regression (GWR) as detailed in the provided reference for V4.NA.02. V4.NA.02.MAPLE further modified the V4.NA.02 GWR method with additional developments as part of the MAPLE (Mortality–Air Pollution Associations in Low-Exposure Environments) project. This adjustment was of particular value over low concentrations. The GWR method of individual components remains unchanged from V4.NA.02, but are provided are percentages to ensure mass closure and recommended to be applied to the V4.NA.02.MAPLE total PM<sub>2.5</sub>.</p> <p>Annual datasets are provided in NetCDF [.nc]. Gridded files use the WGS84 projection. Compositional estimates are provided for sulfate (SO4), nitrate (NO3), ammonium (NH4), organic matter (OM), black carbon (BC), mineral dust (DUST), and sea-salt (SS). Percentages are denoted with a ‘p’ after component identifiers within filenames. A slight change in file name has been included for 2017, corresponding to minor internal changes compared to earlier years. Overall, however, the dataset is consistent throughout its entire time period and can be appropriately used for trend analysis.</p> <p><strong>Reference:</strong><br> van Donkelaar, A., R. V. Martin, et al. (2019). <strong>Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors.</strong> Environmental Science & Technology, 2019, doi:10.1021/acs.est.8b06392.</p>
Supporting Information for 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'
<p><strong>Supporting Information of 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'</strong></p> <p>This dataset contains the Supporting Information of the publication </p> <p>Rühr PT & Blanke A <strong>(2022)</strong>: 'forceX and forceR: a mobile setup and R package to measure and analyse a wide range of animal closing forces'. doi: <a href="https://doi.org/10.1111/2041-210X.13909">10.1111/2041-210X.13909</a>.</p> <p>It includes</p> <ul> <li>validation measurements the forceX setups (1 Ruehr Blanke 2022 validation measurements.zip)</li> <li>all CAD files to build the forceX setup (3D-printed or metal-turned) (2 Ruehr Blanke 2022 forceX CAD files.zip)</li> <li>forceX assembly instructions in HTML format, including schematics of custom electronics (3 Ruehr Blanke 2022 forceX Assembly instructions.html)</li> <li>forceX assembly instructions as video (4 Ruehr Blanke 2022 forceX assembly video 03.mp4)</li> <li>R code that produced all validation-related figures used in the original publication and that functions as a forceR v.1.0.13 example workflow (5 Ruehr Blanke 2022 forceR_workflow_example.R)</li> <li>Python code to take videos of force measurements using the forceX camera module (6 Ruehr Blanke 2022 forceX_RPi_camera_code.py)</li> <li>bundled version of forceR v.1.0.15 (forceR_1.0.15.tar.gz)</li> </ul> <p>The CAD files and assembly instructions are also available on <a href="https://www.thingiverse.com/thing:4961834">Thingiverse</a>. The forceR package is available on <a href="https://cran.r-project.org/web/packages/forceR/index.html">CRAN</a> (stable version) and <a href="https://github.com/Peter-T-Ruehr/forceR">GitHub</a> (development version).</p>
The soil province geodatabase of Italy, storing information of soil typological units and broad soil regions at the 1:1,000,000 and 1:10,000,000 scales
<p>The Soil Map of Italy at 1:1,000,000 scale, was the result of the work of Edoardo AC Costantini, Giovanni L'Abate, Roberto Barbetti, Maria Fantappié, Romina Lorenzetti, and Simona Magini affiliated to Research Centre for agrobiology and soil science (CREA-ABP), in collaboration with several regional institutions, universities and other research centers of the CREA - Consiglio per la ricerca in agricoltura e l'analisi dell'economia agraria. The map, was printed by S.EL.CA. of Florence. The map is an informative and educational work of general scientific interest, which updates the previous one edited by prof. Fiorenzo Mancini and collaborators in 1966 both in terms of knowledge and of the adopted methods. It was produced processing of all data within a geographical and soil geodatabase, collected by the CREA-ABP and other institutions collaborating in over ten years of work and using the latest international methods. The soil map shows the distribution of major soils in the country and constitutes a milestone in the process launched in 1999 as part of the project the Soil Map of Italy at a scale of 1: 250,000, funded by MIPAAF and implemented in collaboration with the regional institutions. Both broad soil regions and soil provinces (reference scale 1:10,000,000 and 1:1,000,000) are reported.</p> <p>Most small-scale soil maps report dominant typological units and allow only a partial appraisal of pedodiversity since territories with similar dominant soils can actually possess different pedodiversity. This is particularly true at the national scale, where a great wealth of soil information collected at more detailed scales is generalized.</p> <p>A methodology was set up, which aimed at preserving pedodiversity in upscaling soil maps by using geomatic techniques and the World Reference Base for soil resources (WRB). The main source of information was the soil system geodatabase of Italy, storing information of soil typological units and soilscapes at the 1:500,000 reference scale. Qualitative aggregation of soil taxa followed upscaling rules aimed at (i) maintaining the information about pedogenetic processes and (ii) grouping soilscapes showing recurrent patterns of soil forming processes. The upscaling methodology can be summarized in seven steps as follows: (1) soil forming processes selection, retrieved from soil typological units stored in the national database; (2) upscaling soil systems and creation of broad soil regions at 1:10,000,000 reference scale; (3) semantic upscaling of typological units to form taxa showing different soil forming processes; (4) ranking and associating soil forming processes; (5) geography upscaling of soil systems geometry to form polygons at 1:1,000,000 reference scale, called subregions; (6) ranking subregions according to their extension; (7) naming subregions by ranking the taxa according to the number of soil typological units.</p> <p>The soil subregion map reported 47 map unit and 148 taxa, belonging to 22 reference soil group of WRB and showing from one to four qualifiers. Each map unit had from 2 to 18 taxa, for a total of 317 occurrences. Thirty taxa had 3 or more occurrences, while the remaining took place in one or two subregions only.</p>
A NICER View of the Massive Pulsar PSR J0740+6620 Informed by Radio Timing and XMM-Newton Spectroscopy: Nested Samples for Millisecond Pulsar Parameter Estimation
<p>Posterior sample files associated with the preprint "A <em>NICER</em> View of the Massive Pulsar PSR J0740+6620 Informed by Radio-Timing and <em>XMM-Newton</em> Spectroscopy" by Riley et al. (2021; <a href="https://arxiv.org/abs/2105.06980">arXiv:2105.06980 [astro-ph.HE]</a>; submitted to ApJL).</p> <p>Also included are: the data products; the numeric model files including the telescope calibration products; model modules in the Python language using the X-PSI framework; and Jupyter analysis notebooks.</p> <p>Please refer to the README for detailed information.</p> <p> </p> <p> </p>
Database of weeds in cultivation fields of France and UK, with ecological and biogeographical information
<p>The database includes a list of 1577 weed plant taxa found in cultivated fields of France and UK, along with basic ecological and biogeographical information.<br> The database is a CSV file in which the columns are separated with comma, and the decimal sign is ".".<br> It can be imported in R with the command "tax.discoweed <- read.csv("tax.discoweed_18Dec2017_zenodo.csv", header=T, sep=",", dec=".", stringsAsFactors = F)"</p> <p>Taxonomic information is based on TaxRef v10 (Gargominy et al. 2016),<br> - 'taxref10.CD_REF' = code of the accepted name of the taxon in TaxRef,<br> - 'binome.discoweed' = corresponding latine name,<br> - 'family' = family name (following APG III),<br> - 'taxo' = taxonomic rank of the taxon, either 'binome' (species level) or 'infra' (infraspecific level),<br> - 'binome.discoweed.noinfra' = latine name of the superior taxon at species level (different from 'binome.discoweed' for infrataxa),<br> - 'taxref10.CD_REF.noinfra' = code of the accepted name of the superior taxon at species level.</p> <p>The presence of each taxon in one or several of the following data sources is reported:<br> - Species list from a reference flora (observations in cultivated fields over the long term, without sampling protocol),<br> * 'jauzein' = national and comprehensive flora in France (Jauzein 1995),<br> - Species lists from plot-based inventories in cultivated fields,<br> * 'za' = regional survey in 'Zone Atelier Plaine & Val de Sèvre' in SW France (Gaba et al. 2010),<br> * 'biovigilance' = national survey of cultivated fields in France (Biovigilance, Fried et al. 2008),<br> * 'fse' = Farm Scale Evaluations in England and Scotland, UK (Perry, Rothery, Clark et al., 2003),<br> * 'farmbio' = Farm4Bio survey, farms in south east and south west of England, UK (Holland et al., 2013)<br> - Reference list of segetal species (species specialist of arable fields),<br> * 'cambacedes' = reference list in France (Cambacedes et al. 2002)</p> <p>Life form information is extracted from Julve (2014) and provided in the column 'lifeform'.<br> The classification follows a simplified Raunkiaer classification (therophyte, hemicryptophyte, geophyte, phanerophyte-chamaephyte and liana). Regularly biannual plants are included in hemicryptophytes, while plants that can be both annual and biannual are assigned to therophytes.</p> <p>Biogeographic zones are also extracted from Julve (2014) and provided in the column 'biogeo'.<br> The main categories are 'atlantic', 'circumboreal', 'cosmopolitan, 'Eurasian', 'European', 'holarctic', 'introduced', 'Mediterranean', 'orophyte' and 'subtropical'.<br> In some cases, a precision is included within brackets after the category name. For instance, 'introduced(North America)' indicates that the taxon is introduced from North America.<br> In addition, some taxa are local endemics ('Aquitanian', 'Catalan', 'Corsican', 'corso-sard', 'ligure', 'Provencal').<br> A single taxon is classified 'arctic-alpine'.</p> <p>Red list status of weed taxa is derived for France and UK:<br> - 'red.FR' is the status following the assessment of the French National Museum of Natural History (2012),<br> - 'red.UK' is based on the Red List of vascular plants of Cheffings and Farrell (2005), last updated in 2006.<br> The categories are coded following the IUCN nomenclature.</p> <p>A habitat index is provided in column 'module', derived from a network-based analysis of plant communities in open herbaceous vegetation in France (Divgrass database, Violle et al. 2015, Carboni et al. 2016).<br> The main habitat categories of weeds are coded following the Divgrass classification,<br> - 1 = Dry calcareous grasslands<br> - 3 = Mesic grasslands<br> - 5 = Ruderal and trampled grasslands<br> - 9 = Mesophilous and nitrophilous fringes (hedgerows, forest edges...)<br> Taxa belonging to other habitats in Divgrass are coded 99, while the taxa absent from Divgrass have a 'NA' value.</p> <p>Two indexes of ecological specialization are provided based on the frequency of weed taxa in different habitats of the Divgrass database.<br> The indexes are network-based metrics proposed by Guimera and Amaral (2005),<br> - c = coefficient of participation, i.e., the propensity of taxa to be present in diverse habitats, from 0 (specialist, present in a single habitat) to 1 (generalist equally represented in all habitats),<br> - z = within-module degree, i.e., a standardized measure of the frequency of a taxon in its habitat; it is negatve when the taxon is less frequent than average in this habitat, and positive otherwise; the index scales as a number of standard deviations from the mean.</p>
Supporting Material for "Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review"
<p>This dataset contains all supporting material for the paper "Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review", published in the journal Swiss Psychology Open:</p> <p><em>Mack, M., Scarampi, C., Joly-Burra, E., Zuber, S., de Freitas, C., Teixeira, R. and Kliegel, M. (2025) ‘Enhancing Mental Health and Cognitive Function in Older Adults: A Swiss Perspective on Public Health Interventions and Stigma Mitigation Strategies Informed by a Desk Review’, Swiss Psychology Open, 5(1), p. 2. Available at: <a href="https://doi.org/10.5334/spo.81.">https://doi.org/10.5334/spo.81</a>.</em></p> <p>It includes the following documents and files:</p> <p><strong>S1. Protocol:</strong> ADVANCE Protocol for desk reviews</p> <p><strong>S2. Search strategy</strong></p> <p><strong>S3. Guidelines for title and abstract screening:</strong> Guidelines for the selection of articles included in the desk review</p> <p><strong>S4. Guidelines full-text screening:</strong> Guidelines for the selection of articles included in the desk review</p> <p><strong>S5. Guidelines data extraction:</strong> ADVANCE Guidelines/codebook data extraction</p> <p><strong>data extraction_desk review_switzerland.xlsx</strong></p> <p>This desk review was conducted as part of the ADVANCE project, which aims to enhance our understanding of mental health promotion and prevention. This desk review evaluates the current state of interventions for mental health and cognitive functioning among older adults in Switzerland focusing on the features of these interventions as well as on Swiss-specific contextual factors that contribute to vulnerability and stigma. This results of the desk review has been submitted for publication to 'LIVES Working Papers' and 'Swiss Psychology Open' . The two versions of the desk review differ slightly. The version for LIVES Working Papers, included the results of the Delphi survey and the resulting intervention scenarios. The version for Swiss Psychology Open, did not include the Delphi survey results and the resulting intervention scenarios, but included a more detailed discussion of the review results.</p>
Survey questions and raw data for the study in the paper "Educational Technology for Tutors – What are Useful Tools and Information?"
<p>The data include the questions data set, the answers dataset and the codebook for the questions conducted with soscisurvey (https://www.soscisurvey.de/de/index). The survey itself can be imported in soscisurvey (via the XML data) and reused.</p> <p>The answers are unedited.</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.