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3,030 results for “green”
Pond environmental and taxonomic data for Niwot Ridge and Green Lakes Valley, 2021 - ongoing.
This is a summary of basic environmental data and benthic macroinvertebrates from water in ponds in the vicinity of the Niwot Ridge LTER. Ponds were selected across a range of elevations, sizes, and positions relative to glacial, stream, and lake water sources. Ponds sampled occurred on Niwot Ridge and throughout the Green Lakes Valley.
Benthic macroinvertebrate, water temperature, and stream environmental data for Green Lakes Valley, 2021.
This dataset contains stream benthic macroinvertebrate community structure data from nine sites in the Green Lakes Valley ranging from below treeline near Albion Camp to the outflow of the Arikaree Glacier, all sampled in summer of 2021. The dataset also contains files with stream environmental data related to substrate stability, periphyton chlorophyll a concentrations, and water temperatures.
Arbuscular mycorrhizal fungi and dark septate endophytes root colonization in Upper Green Lakes Valley, 2007-2016
Arbuscular mycorrhizal fungi (AMF) and dark septate endophytes (DSE) are two fungal groups that colonize plant roots and can benefit plant growth, but little is known about their landscape distributions. We performed sequencing and microscopy on a variety of plants across a high-elevation landscape featuring plant density, snowpack, and nutrient gradients. Percent colonization by both AMF and DSE varied significantly among plant species, and DSE colonized forbs and grasses more than sedges. AMF were more abundant in roots at lower elevation areas with lower snowpack and lower phosphorus and nitrogen levels, suggesting increased hyphal recruitment by plants to aid in nutrient uptake. DSE colonization was highest in areas with less snowpack and higher inorganic nitrogen levels, suggesting an important role for these fungi in mineralizing organic nitrogen. Both of these groups of fungi are likely to be important for plant fitness and establishment in areas limited by phosphorus and nitrogen.
Green Lakes Valley land cover classification, Niwot Ridge LTER, Colorado
Land cover data generated by Don Cline (graduate student, CU Boulder Geography), as part of suite of spatial maps made for Green Lakes Valley (see Williams et al. 1999).
10-meter elevation contours, Green Lakes Valley, Niwot Ridge LTER, Colorado
10-meter contours clipped with a box made from extents of the Green Lakes Valley 1999 high-resolution orthorectified imagery dataset (glv.tif). This dataset was made to support hierarchical GIS databases at the Niwot Ridge LTER. Additional information concerning the Niwot Ridge LTER hierarchical GIS can be found in Walker et al. (1993).
NDVI Raster maps of Scotland for 2013-2016 used to analyse correlations between greenness, mortality and mental health.
<p>These files were used in the analysis for "Greenness, mortality and mental health prescription rates in urban Scotland - a population level, observational study" Hyam. Submitted to RIO 2020.</p> <p><strong>Extract on construction of data</strong></p> <p>NDVI data was downloaded from the United States Geological Survey (USGS) Land Satellites Data System (LSDS) Science Research and Development (LSRD) (United States Geological Survey 2018). Which produces Level 2 and Level 3 data products from the Level 1 data of instruments aboard Landsat Satellites. For this study Surface Reflectance data generated by the Landsat Surface Reflectance Code (LaSRC) from the Operational Land Imager (OLI) instrument aboard the Landsat 8 satellite was used (United States Geological Survey 2018). The Surface Reflectance NDVI (sr_ndvi) product and Level-2 Pixel Quality Assessment band (pixel_qa) were downloaded for Landsat scenes 204/21, 205/21, 206/21, 204/20, 205/20, 206/20 WRS-2 (NASA 2018) for the calendar years 2013 to 2016. These scenes cover most of Scotland and include all the major urban areas. A full list of the 333 products is given in supplemental material. Suppl. material 2</p> <p>All of Scotland is over 54° North and so for many satellite images the sun is at too low an angle to give reliable surface reflectance data especially in the winter months. Scotland also has an oceanic climate so the ground is often obscured by cloud or mist. To build a detailed, contiguous NDVI map of the whole country therefore requires combining images taken on many satellite passes especially if points are to be sampled multiple times to overcome measurement errors. The images downloaded from USGS were therefore combined. A cloud free version of each NDVI image was created by setting the pixels that corresponded to cloud, snow or water in the Quality Assurance Assessment band to NA. These cloud free images were then combined into a single, mosaic stack of images to cover all of the study area and then averaged down to a single layer as a tiff image. This was done for two seasonal periods, Winter (October, November, December of 2013, 2014, 2015 and 2016 combined with January, February, March of 2014, 2015, 2016) and summer (April, May, June, July, August, September of 2014, 2015, and 2016). The resulting two images covering most of Scotland for winters and summers between 2013 and 2016 and formed the basis of subsequent analysis.</p> <p>These two files are included here along with a list of the Landsat products used to produce them.</p>
Yearly pageviews of English Wikipedia articles with potential links to green open access scholarly articles
<p>Number of visits in 2019 for a sample of 23462 English Wikipedia articles which contain references to academic sources which have a green open access copy available but not yet used. The consultation statistics were retrieved from the Wikimedia pageviews API using the Python client (script also included). The sample was selected among articles which in April 2020 had at least one citation of an academic paper (using the "cite journal" template) for which OAbot (through Unpaywall data) had found a green open access URL to add (gratis open access, not necessarily libre open access). Data shows that the top 1 % most visited articles received 30 % of the visits: over 500 million in the year, corresponding to 1 million potential citation link clicks to distribute across all references assuming a 0.2 % click-through rate per Piccardi et al. (2020).</p>
GREEN-VARAN scores resources (DANN GRCh37)
<p>Processed DANN scores to be used with GREEN-VARAN</p> <p>This dataset contains the GRCh37 version for DANN.</p> <p>See: <a href="https://academic.oup.com/bioinformatics/article/31/5/761/2748191">https://academic.oup.com/bioinformatics/article/31/5/761/2748191</a></p> <p>If you use DANN score annotations with GREEN-VARAN don't forget to cite also the original DANN paper.</p>
Observed runoff time series from a green roof field campaign in Hannover-Herrenhausen
<p>This dataset includes a csv file comprising observed runoff time series from a green roof field campaign, which was conducted by Prof. Dr.-Ing. Hans-Joachim Liesecke. The csv file provides runoff from 11 green roof variants (10 was excluded, since it has a different design). Rows include daily runoff totals (collected each morning, excluding weekends). Please refer to this article, which describes the dataset in more detail: </p> <p><strong>Iffland, R., Förster, K., Westerholt, D., Pesci, M. H., & Lösken, G. Robust vegetation parameterization for green roofs in EPA SWMM. Hydrology. </strong><a href="https://doi.org/10.3390/hydrology8010012">https://doi.org/10.3390/hydrology8010012</a></p> <p>The field campaign involved a total of 11 superstructures in triple repetition. In the csv file, each column represents average values computed out of three independent measurements (in mm*d<sup>-1</sup>)</p> <p>The individual test plots were 2 m x 2 m with a slope of 2 % and a drainage opening in the middle of the lowest point of the slope. The outflowing water was collected in non-weighable lysimeters (rain barrels) that were read and emptied at 8 A.M. every day. On weekends, readings were taken the following workday.</p> <p> </p>
Data products and software for `X-ray diagnostics of Cassiopeia A's "Green Monster": evidence for dense shocked circumstellar plasma`
<div> <h2>Data Reproduction Package for the publication ‘X-ray diagnostics of Cassiopeia A’s “Green Monster”: evidence for dense shocked circumstellar plasma’</h2> </div> <div> <h3>Authors: Jacco Vink, Manan Agarwal, Patrick Slane, Ilse De Looze, Dan Milisavljevic, Daniel Patnaude, and Tea Temim.</h3> </div> <div> <h3>Link to paper: <a href="https://doi.org/10.3847/2041-8213/ad2fc5">https://doi.org/10.3847/2041-8213/ad2fc5</a> </h3> <p> </p> </div> <div> <h4>This package was prepared by Jacco Vink and Manan Agarwal (University of Amsterdam)</h4> </div> <div> <h3>Summary</h3> </div> <div> <p>This data reproduction package contains the data files in FITS format used to<br>generate the figures in the paper. The data files concern the revised manuscript, which incorporates changes made in response to the journal’s referee report.</p> </div> <div> <p>The paper is based on Chandra X-ray Observatory (CXO) data of Cassiopeia A taken in 2004. The raw archival data used, maintained by the Chandra Data Archive, can be retrieved using the following DOI link: <a href="https://doi.org/10.25574/cdc.209">https://doi.org/10.25574/cdc.209</a>.</p> </div> <div> <p>Additional James Webb Space Telescope (JWST) data are stored at the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute. The data used in the paper can be downloaded through DOI link <a href="https://doi.org/10.17909/szf2-bg42">https://doi.org/10.17909/szf2-bg42</a>.</p> </div> <div> <p>The data produced from the above raw data are stored in the files:</p> </div> <div> <ul> <li>green_monster_image_data.tar.gz</li> <li>spectral_files_and_models.tar.gz</li> <li>imaging_and_pca_code.tar.gz</li> <li>green_monster_pca_input_output.tar.gz</li> </ul> <p>The repository contains JWST/MIRI mosaics of Cassiopeia A which are described in detail in the paper "A JWST Survey of the Supernova Remnant Cassiopeia A", by D. Milisavljevic, T. Temim, I. De Looze, et al.; see https://arxiv.org/abs/2401.02477, to be published in ApJ letters.<br> </p> </div>
Research data supporting "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains"
<p>Research data supporting the peer-reviewed article "Impact of global heterogeneity of renewable energy supply on heavy industrial production and green value chains" by the same authors.</p>
Data from a cross-sectional study of fifth grade children in a sample of primary schools in Belgium that differ in amount of greenness at school and landscape level
<p>The data in this deposit were collected as part of the <code>B@SEBALL</code> project (Biodiversity at School Environments - Benefits for All). </p> <p>The project investigated how biodiversity in the school environment can positively affect children’s health and mental well-being. <code>B@SEBALL</code> also investigated the opportunities for reducing health inequalities among children via biodiversity at school environments.</p> <p>The data are organized according to the <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Package standard</a>. All child-level and school-level data have been anonymized. Each data package is a collection of <code>csv</code> files and a <code>json</code> file. The <code>json</code> file holds descriptive information for all variables in all <code>csv</code> files. The <code>zip</code> file contains two frictionless data packages. The data packages contain information on 37 primary schools and 513 children. </p> <p>The data package, <code>data_package_an_zenodo_cleaned_data</code>, contains the original data in a tidied and cleaned format. It consists of 46 <code>csv</code> files. The files relate to the following contents:</p> <table> <tbody> <tr> <td><strong>contents</strong></td> <td><strong>filename</strong></td> </tr> <tr> <td>metadata file</td> <td>datapackage.json</td> </tr> <tr> <td>landscape level variables</td> <td>wp1_landscape_level_data.csv</td> </tr> <tr> <td>metadata about participants</td> <td>wp2_participants_metadata.csv</td> </tr> <tr> <td>general school level data</td> <td>wp2_school_data.csv</td> </tr> <tr> <td>pollution data at school level</td> <td>wp3_ua_sirm_data.csv</td> </tr> <tr> <td>classroom data about air quality</td> <td>wp3_ucl_classroom_airquality.csv</td> </tr> <tr> <td>area of ecotopes in the school environment</td> <td>wp3_ucl_ecotope_categories.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenness_indicators.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenness_key.csv</td> </tr> <tr> <td>greenness indicators for the school environment derived from ecotopes</td> <td>wp3_ucl_greenpatches.csv</td> </tr> <tr> <td>playground biodiversity indicators</td> <td>wp3_ucl_playground_biodiversity.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_child.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_line.csv</td> </tr> <tr> <td>d2-test of attention data</td> <td>wp4_d2_data_by_linegroup.csv</td> </tr> <tr> <td>Self-reported allergy data</td> <td>wp4_isaac_data.csv</td> </tr> <tr> <td>Self-reported allergy data</td> <td>wp4_isaac_questions.csv</td> </tr> <tr> <td>Self-reported well-being data</td> <td>wp4_kidscreen_data.csv</td> </tr> <tr> <td>Self-reported well-being data</td> <td>wp4_kidscreen_questions.csv</td> </tr> <tr> <td>Self-reported attitude toward outdoor play</td> <td>wp5_atop_data.csv</td> </tr> <tr> <td>Self-reported attitude toward outdoor play</td> <td>wp5_atop_questions.csv</td> </tr> <tr> <td>Guardian-reported general questions</td> <td>wp5_guardians_general_questions_data.csv</td> </tr> <tr> <td>Guardian-reported general questions</td> <td>wp5_guardians_general_questions_key.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_data_part1.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_data_part2.csv</td> </tr> <tr> <td>Guardian-reported protection from risk</td> <td>wp5_guardians_risk_protection_key.csv</td> </tr> <tr> <td>Self-reported nature connectedness</td> <td>wp5_nc_data.csv</td> </tr> <tr> <td>Self-reported nature connectedness</td> <td>wp5_nc_key.csv</td> </tr> <tr> <td>Parent-reported allergy data</td> <td>wp5_parents_allergy_related_questions_data.csv</td> </tr> <tr> <td>Parent-reported allergy data</td> <td>wp5_parents_allergy_related_questions_key.csv</td> </tr> <tr> <td>Parent-reported cultural background</td> <td>wp5_parents_cultural_background_data.csv</td> </tr> <tr> <td>Parent-reported cultural background</td> <td>wp5_parents_cultural_background_key.csv</td> </tr> <tr> <td>Parent-reported general questions</td> <td>wp5_parents_general_questions_data.csv</td> </tr> <tr> <td>Parent-reported general questions</td> <td>wp5_parents_general_questions_key.csv</td> </tr> <tr> <td>Parent-reported independent mobility data</td> <td>wp5_parents_independent_mobility_data.csv</td> </tr> <tr> <td>Parent-reported independent mobility data</td> <td>wp5_parents_independent_mobility_key.csv</td> </tr> <tr> <td>Parent-reported living environment</td> <td>wp5_parents_living_environment_data.csv</td> </tr> <tr> <td>Parent-reported living environment</td> <td>wp5_parents_living_environment_key.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part1.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part2.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part3.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_data_part4.csv</td> </tr> <tr> <td>Parent-reported outdoor play characteristics</td> <td>wp5_parents_outdoor_play_key.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_data_part1.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_data_part2.csv</td> </tr> <tr> <td>Parent-reported risk protection data</td> <td>wp5_parents_risk_protection_key.csv</td> </tr> <tr> <td>Parent-reported data relating to socio-economic status</td> <td>wp5_parents_ses_questions_data.csv</td> </tr> <tr> <td>Parent-reported data relating to socio-economic status</td> <td>wp5_parents_ses_questions_key.csv</td> </tr> </tbody> </table> <p> </p> <p>The <code>data_package_an_zenodo_derived_data</code> data package, contains derived data that was calculated based on input from <code>data_package_an_zenodo_cleaned_data</code> at either child-level or at school-level.</p> <table> <tbody> <tr> <td><strong>contents</strong></td> <td><strong>filename</strong></td> </tr> <tr> <td>metadata file</td> <td>datapackage.json</td> </tr> <tr> <td>derived data at child level</td> <td>wp1_child_level_key_variables.csv</td> </tr> <tr> <td>derived attention score based on d2-test data, aggregated to line-level</td> <td>wp1_d2_by_line_attention_score.csv</td> </tr> <tr> <td>derived data at school level</td> <td>wp1_school_level_key_variables.csv</td> </tr> </tbody> </table> <p>These data packages only store information for participants that gave consent for a particular part of the study and that gave consent for long-term storage of the data. There may therefore be slight differences between results published as part of the project consortium, which could make use of participant data that did not give consent for long-term data storage, and reproduction of these results based on the data in this data repository. We also note that the derived variables in the derived data package were calculated with these participants included and removal of participants for which we had no long-term storage consent was done after these calculations.</p> <p>As part of the project, microbiome data were also collected (both from cheek swabs on the children and from environmental samples), but this part of the data are not a part of this deposit and will be deposited in the European Nucleotide Archive (ENA).</p>
Latent infection of an active giant endogenous virus in a unicellular green alga
<p>Additional data for Latent infection of an active giant endogenous virus in a unicellular green alga.</p>
Dose and administration time of indocyanine green in near-infrared fluorescence cholangiography during laparoscopic cholecystectomy (DOTIG) Dataset.
<p><strong><span>Introduction </span></strong></p> <p><span>Different techniques have been described to reduce the incidence of the intraoperative bile duct injury during laparoscopic cholecystectomy (LC), and Near-Infrared Fluorescence Cholangiography (NIFC) with Indocyanine Green (ICG) is one of the latest additions. Currently, there are great disparities in the usage or administration protocols of ICG.</span></p> <p><strong><span>Methods</span></strong></p> <p><span>The aim of this randomised multicenter clinical trial (RCT) is to analyse whether there are differences between the dose and administration ICG intervals to obtain good-quality NIFC during LC. In addition, different factors were analysed that may have an influence on the results of this technique. </span><span>This trial was approved by the local institutional Ethics Committee</span><span>.</span></p> <p><strong><span>Results </span></strong></p> <p><span>From June 2022 to June 2023, 200 patients were randomised in the four arms (G1: </span><span>2.5 mg ICG >3 hours prior to surgery, G2: 2.5 mg ICG 15-30 minutes prior to surgery, G3: 0.05 mg/kg ICG >3 hours prior to surgery and G4: 0.05 mg/kg ICG 15-30 minutes prior to surgery)</span><span>. We found differences in the DISTURBED score between the groups (<em>p</em><0.001), suggesting that ICG administration 15-30 minutes before surgery was worse than administration >3 hours after LC (<em>p</em>=0.02). We also observed that body mass index, gender, ASA Classification System, previous liver and biliary disease and the type of surgery had influence on NIFC. Finally, the NIFC had impact in intraoperative and postoperative complications, operative time and hospital length of stay. </span></p> <p><strong><span>Conclusion </span></strong></p> <p><span>The time of ICG administration was related to NIFC results, as well as different preoperative predictors. NIFC may also influence in surgical outcomes of LC.</span></p> <p><strong><span>Documentation in ZENODO</span></strong></p> <p>Files stored in this repository correspond to the data extracted from the CRDe RedCAP used in this study. The file 'DOTIG_DATA_NoAA_LABELS_2023-10-31' corresponds to the data collected during the study, and the file 'DOTIG_DATA_AA_LABELS_2023-10-31' corresponds to the adverse events recorded during the study. Additionally, there is one more file which is the recoding of adverse events to "MEDDRA Terms (PT) vs 25.1" and "SOC".</p> <p>IBSAL uses the REDCap system for database creation. A detailed description of the information to be captured in the database is documented in the "DOTID_PGD_Data Dictionary Codebook" document. The structure of this data in the CRDe is described in the "DOTIG_PGD_Annotated CRDe document". The Annotated CRDe details the names of input objects in the CRDe with the name, format, and type of variables that will be used during the data entry process. The database must capture all the elements included in the "DOTIG_PGD_Variable List" document.</p> <p>The data management throughout the study is documented in the document DOTIG_PGD_Data Management Report.</p>
Thirty-eight years of CO 2 fertilization have outpaced growing aridity to drive greening of Australian woody ecosystems
<p>Data and code for "Thirty-eight years of CO 2 fertilization have outpaced growing aridity to drive greening of Australian woody ecosystems"</p>
Raw and analyzed data for manuscript "The synergistic effect of microwave drying and plasma surface treatments on the wettability of green wood"
<p><strong>Abstract:</strong></p> <p>In spite of being a one-step solution to several problems associated with woodworking and being energy efficient, the application of microwave (MW) modification in wood research remained very limited and the promising method has practically no use in wood industries across the globe. Research done so far in this field primarily sheds light on its potential in enhancing wood permeability, treatability and uniform wood drying. While MW treatments are mostly used on wet or green wood, another modification technique, plasma, has potential benefits to synergistically enhance effects of MW treatment, but has not been applied on wet or green wood specimens, so far. This study takes a first step to investigate effects of plasma treatments (PT) on green wood specimens as well as combinations of MW and plasma treatments. As a preliminary study, the methodology focuses on water contact angle measurements, since these are most commonly used as indicators for surface modifications in industrial applications. On the investigated samples of Norway spruce (<em>Picea abies</em> Karst.), an exponential time dependence was found for the contact angle. Initial contact angle after droplet deposition increased due to drying and migration of organic molecules during treatments. In comparison to the literature, the effect of plasma was significantly less pronounced on wet wood specimens. The initial contact angle showed lowest statistical variations after MW treatment, whereas plasma increased inhomogeneities. The final contact angle on treated specimens was lowest for PT-only specimens as well as specimens treated with plasma after MW. In contrast to the initial contact angle, the final contact angle showed lowest variations after PT. Wetting rates were insignificantly improved by plasma, with reduced statistical variations after all treatments.</p>
HadCM3 passive tracer Green's functions
<p>HadCM3 passive tracer Green's functions simulated in the pre-industrial control experiment. Two configurations of Green's functions are included. G_c propagates concentration boundary conditions. G_f propagates air-sea tracer fluxes.</p>
Dataset: Green Mark certified buildings metadata from Singapore
<p>In many countries, there are local efforts and incentives to further increase the adoption of greener buildings. This highlights the critical need for buildings to be green and tools to facilitate their transformation for new and existing building owners. Thus, this work curates a dataset regarding the actions and features implemented by building stakeholders to attain the Green Mark (GM) certification which promotes the adoption of numerous green building technologies. Public data from 3,583 entries over 17 years was extracted and pre-processed. Green features of each certified entry were identified and labeled accordingly using a list of keywords created from the GM certification assessment criteria. We present an overview of the dataset and key insights into the building retrofitting landscape. The dataset is available at <a href="https://github.com/buds-lab/data-driven-greenmark">https://github.com/buds-lab/data-driven-greenmark</a> as well as the feature descriptions, raw data processing and raw files. </p>
DOIs linked by the English Wikipedia which could be made available in green Open Access
<p>List of citations from the English Wikipedia articles extracted from the enwiki-20170720-pages-articles XML dump via https://pypi.org/project/mwcites/ , DOIs cleaned with custom regular expressions.</p> <p>The list of scholarly publications identified by the DOIs has been filtered to exclude those which are already available in Open Access and those which may not be depositable according to SHERPA/RoMEO policy summaries, first via the Dissemin API and then by the oaDOI API, with the attached Python script (https://github.com/nemobis/bots/blob/master/doi-doai-openaccess.py ).</p> <p>This produced a list of 194913 DOIs available in open access and 430230 DOIs unavailable and depositable (as of 2017-08-22 data, which for oaDOI was partly v1 and partly v2).</p>
Steve Green (g2512)
<b>-- <a href="https://doi.org/10.5281/zenodo.11582199">Documentation</a> --</b><br><br><u>Name</u>: Steve Green<br><u>musiXplora-ID</u>: g2512<br><u>musiXplora-URI</u>: <a href="https://musixplora.de/mxp/g2512">https://musixplora.de/mxp/g2512</a><br><u>Gender</u>: m<br><u>Date of Birth</u>: 1949<br><u>Place of Birth</u>: West Virginia<br><u>Date of Death</u>: 2013<br><u>Place of Death</u>: Undefined<br><u>First Mentioned</u>: 1974<br><u>Sectors</u>: Zupfinstrumentenbau<br><u>Professions (Musical)</u>: Harfenbauer<br><u>Other Places of Activity</u>: USA<br><br><br><u>Portfolio:</u><br><table><tbody><tr><th>Group</th><th>Role</th><th>Name</th><th>mXp-ID</th></tr><tr><td>Sortimente</td><td>Sortiment</td><td>Harfe</td><td><a href="https://musixplora.de/mxp/2001474">2001474</a></td></tr></tbody></table><br><br><u>Changelog</u>:<br> - v0.0.1: Initial Upload.<br>
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.