Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
6,766
datasets available to search
ShareScore release 0.7.1
Dataset results
6,766 results for “project”
Data set for "Diverse long-range axonal projections of excitatory layer 2/3 neurons in mouse barrel cortex"
<p>Data set for: Yamashita T, Vavladeli A, Pala A, Galan K, Crochet S, Petersen SSA, Petersen CCH (2018) Diverse long-range axonal projections of excitatory layer 2/3 neurons in mouse barrel cortex. Front Neuroanat 12: 33. https://doi.org/10.3389/fnana.2018.00033</p> <p>There are 25 files in this data upload:</p> <p>1. '2018_Yamashita_FrontNeuroanat.pdf' - this a pdf version of the online publication.</p> <p>2. 'Yamashita_Figure2_Quantification.xlsx' - this is a Microsoft Excel file giving the locations of high density axonal projections from layer 2/3 pyramidal neurons in the mouse C2 barrel column in the coordinate frame of Paxinos & Franklin (2001) The mouse brain in stereotaxic coordinates. Academic Press. The data are plotted in Figure 2 of Yamashita et al., 2018.</p> <p>3. 'Yamashita_Figure7_Quantification.xlsx' - this is a Microsoft Excel file giving the dendritic length, number of dendrites, number of dendritic nodes and total axonal length, as well as the axonal length in the different projection zones for each reconstructed neuron. The data are plotted in Figure 7 of Yamashita et al., 2018.</p> <p>4. 'Yamashita_SupMov1_S2P_AP049.mov' - this is a QuickTime video file, showing the 3D structure of neuron AP049 featured in Figure 3 of Yamashita et al., 2018.</p> <p>5. 'Yamashita_SupMov2_M1P_TY308.mov' - this is a QuickTime video file, showing the 3D structure of neuron TY308 featured in Figure 5 of Yamashita et al., 2018.</p> <p>6. 'AV198.zip' - this zipped folder contains data relating to mouse AV198: a) 'AV198_stack.tif' the z-stack of whole-brain fluorescence images from expression of tdTomato in layer 2/3 neurons of the C2 barrel column of mouse AV198. b) 'AV198_ROI_Box.zip' can be loaded into FIJI (https://fiji.sc) and indicates projection regions by a box. c) 'AV198_ROI_Point.zip' can be loaded into FIJI (https://fiji.sc) and indicates projection regions by a point. d) 'AV198_Paxinos' is a folder showing the coronal fluorescent brain sections in pdf format overlaid on the equivalent drawing from Paxinos & Franklin (2001) The mouse brain in stereotaxic coordinates. Academic Press.</p> <p>7. 'AV199.zip' - same as 'AV198.zip' but for mouse AV199.</p> <p>8. 'AV201.zip' - same as 'AV198.zip' but for mouse AV201.</p> <p>9. 'AV202.zip' - same as 'AV198.zip' but for mouse AV202.</p> <p>10. 'AV203.zip' - same as 'AV198.zip' but for mouse AV203.</p> <p>11. 'AP042.ASC' - Neurolucida (http://www.mbfbioscience.com/neurolucida) data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP042. Brain contours are also traced.</p> <p>12. 'AP044.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP044. Brain contours are also traced.</p> <p>13. 'AP046.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP046. Brain contours are also traced.</p> <p>14. 'AP047.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP047. Brain contours are also traced.</p> <p>15. 'AP049.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse AP049. Brain contours are also traced.</p> <p>16. 'TY220.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY220. Brain contours are also traced.</p> <p>17. 'TY288.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY288. Brain contours are also traced.</p> <p>18. 'TY300.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY300. Brain contours are also traced.</p> <p>19. 'TY302.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY302. Brain contours are also traced.</p> <p>20. 'TY308.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY308. Brain contours are also traced.</p> <p>21. 'TY310.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY310. Brain contours are also traced.</p> <p>22. 'TY337.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY337. Brain contours are also traced.</p> <p>23. 'TY345.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY345. Brain contours are also traced.</p> <p>24. 'TY367.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY367. Brain contours are also traced.</p> <p>25. 'TY369.ASC' - Neurolucida data file of the 3D reconstruction of axon and dendrite from the single neuron labelled in mouse TY369. Brain contours are also traced.</p>
Identified Charcoal Hearths from "Slope Analysis of 'Digital Elevation Model for Blue Mountain Charcoal Research Project'"
<p>This is a GeoJSON file that lists all of the potential charcoal hearths along the Blue Mountain of eastern Pennsylvania. For a detailed description of how this data was produced, please see:</p> <p>Carter, Benjamin. (2018, May 29). Description of Methods for Identifying Charcoal Hearths along the Blue Mountain of Pennsylvania. (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1255101</p> <p>These hearths were identified using this data:</p> <p>Carter, Benjamin. (2018). Slope Analysis of "Digital Elevation Model for Blue Mountain Charcoal Research Project" (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1252977</p> <p>The above is derived from:</p> <p>Carter, Benjamin P. (2018). Digital Elevation Model for Blue Mountain Charcoal Research Project (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1252441</p> <p> </p>
Blue Mountain Charcoal Project Research Area
<p>This GEOJSON polygon identifies the area in which Dr. Benjamin Carter (Muhlenberg College) and students have focused their efforts in an attempt to use remote sensing and field work to identify charcoal hearths from the 19th century. The main destination for the charcoal was the furnaces and forge of the Balliet family (Lehigh and East Penn Furances and East Penn Forge) in East Penn, Carbon County and Washington, Lehigh County (Pennsylvania, USA). This polygon defines an area that includes much of Pennsylvania State Gamelands #217 but also surrounding privately owned areas that show limited recent impacts by people (especially avoiding homes and roads). It extends from the Lehigh Gap to Pennsylvania Route 309. While there is limited evidence of hearths to the east of the Lehigh Gap, there are definitely more hearths to the southwest of route 309.</p>
Dataset used to perform Focus Groups in Spain, Israel and Hungary (related to m-RESIST project)
<p>Dataset used to perform the following manuscripts: </p> <p>- Huerta-Ramos, E., Escobar-Villegas, M. S., Rubinstein, K., Unoka, Z. S., Grasa, E., Hospedales, M., … Usall, J. (2016). Measuring Users’ Receptivity Toward an Integral Intervention Model Based on mHealth Solutions for Patients With Treatment-Resistant Schizophrenia (m-RESIST): A Qualitative Study. <em>JMIR mHealth and uHealth</em>, <em>4</em>(3), e112. http://doi.org/10.2196/mhealth.5716</p> <p>rom March to June (2015), it was included opinions of patients, informal carers, and clinicians from the three countries concerning the services originally intended to be part of the solution. The activities related to the publication were the following: 9 focus groups (72 people) and 35 individual interviews were carried out in the 3 countries. All recorded data was analysed using discourse analysis as the framework. </p>
Supporting data: "Uncertainty in sea level rise projections due to the dependence between contributors"
<p>These files contain the data analyzed in Le Bars 2018. The paper is available on EarthArXiv (https://eartharxiv.org/uvw3s/) and was submitted to Earth's Future.</p> <p>The NetCDF files contain the Probability Density Functions output from the Probabilistic Sea Level Projection (PSLP) model version 1.</p> <p>Simulations are:<br> IPCC1: The control IPCC AR5 simulation<br> IPCC2: The same but assuming independence between sea level contributors<br> IPCC3: The same but assuming correlation of 1 between sea level contributors<br> Prob1: The control simulation from the probabilistic model<br> Prob2: Assuming independence<br> Prob3: Assuming correlation of 1 between sea level contributors<br> Prob4: Low dependence case<br> Prob5: High dependence case<br> Prob6 to Prob9: Sensitivity experiments replacing each contributor by its expected value.</p> <p>The matrices of Spearman correlation for year 2100 for all experiments are called: <br> SpearmanCorr_namelist*_*.txt</p> <p>The Table*.txt files contain the data used to make tables of sea level percentiles in the paper.</p> <p>The pdf files contain the figures used in the paper and additional pannels not included in the paper.</p> <p>Reference:<br> Le Bars, D. (2018, March 8). Uncertainty in sea level rise projections due to the dependence between contributors. http://doi.org/10.17605/OSF.IO/UVW3S</p>
(No) Influence of Continuous Integration on the Development Activity in GitHub Projects — Dataset
<p>This dataset is based on the TravisTorrent dataset released 2017-01-11 (https://travistorrent.testroots.org), the Google BigQuery GHTorrent dataset accessed 2017-07-03, and the Git log history of all projects in the dataset, retrieved 2017-07-16 and 2017-07-17.</p> <p>We selected projects hosted on GitHub that employ the Continuous Integration (CI) system Travis CI. We identified the projects using the TravisTorrent data set and considered projects that:</p> <ol> <li>used GitHub from the beginning (first commit not more than seven days before project creation date according to GHTorrent),</li> <li>were active for at least one year (365 days) before the first build with Travis CI (before_ci),</li> <li>used Travis CI at least for one year (during_ci),</li> <li>had commit or merge activity on the default branch in both of these phases, and</li> <li>used the default branch to trigger builds.</li> </ol> <p>To derive the time frames, we employed the GHTorrent Big Query data set. The resulting sample contains 113 projects. Of these projects, 89 are Ruby projects and 24 are Java projects. For our analysis, we only consider the activity one year before and after the first build.</p> <p>We cloned the selected project repositories and extracted the version history for all branches (see https://github.com/sbaltes/git-log-parser). For each repo and branch, we created one log file with all regular commits and one log file with all merges. We only considered commits changing non-binary files and applied a file extension filter to only consider changes to Java or Ruby source code files. From the log files, we then extracted metadata about the commits and stored this data in CSV files (see https://github.com/sbaltes/git-log-parser).</p> <p>We also retrieved a random sample of GitHub project to validate the effects we observed in the CI project sample. We only considered projects that:</p> <ol> <li>have Java or Ruby as their project language</li> <li>used GitHub from the beginning (first commit not more than seven days before project creation date according to GHTorrent)</li> <li>have commit activity for at least two years (730 days)</li> <li>are engineered software projects (at least 10 watchers)</li> <li>were not in the TravisTorrent dataset</li> </ol> <p>In total, 8,046 projects satisfied those constraints. We drew a random sample of 800 projects from this sampling frame and retrieved the commit and merge data in the same way as for the CI sample. We then split the development activity at the median development date, removed projects without commits or merges in either of the two resulting time spans, and then manually checked the remaining projects to remove the ones with CI configuration files. The final comparision sample contained 60 non-CI projects.</p> <p>This dataset contains the following files:</p> <p><strong>tr_projects_sample_filtered_2.csv</strong><br> A CSV file with information about the 113 selected projects.</p> <p><strong>tr_sample_commits_default_branch_before_ci.csv<br> tr_sample_commits_default_branch_during_ci.csv</strong><br> One CSV file with information about all commits to the default branch before and after the first CI build. Only commits modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the following columns:</p> <p>project: GitHub project name ("/" replaced by "_").<br> branch: The branch to which the commit was made.<br> hash_value: The SHA1 hash value of the commit.<br> author_name: The author name.<br> author_email: The author email address.<br> author_date: The authoring timestamp.<br> commit_name: The committer name.<br> commit_email: The committer email address.<br> commit_date: The commit timestamp.<br> log_message_length: The length of the git commit messages (in characters).<br> file_count: Files changed with this commit.<br> lines_added: Lines added to all files changed with this commit.<br> lines_deleted: Lines deleted in all files changed with this commit.<br> file_extensions: Distinct file extensions of files changed with this commit.</p> <p><strong>tr_sample_merges_default_branch_before_ci.csv<br> tr_sample_merges_default_branch_during_ci.csv</strong><br> One CSV file with information about all merges into the default branch before and after the first CI build. Only merges modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the following columns:</p> <p>project: GitHub project name ("/" replaced by "_").<br> branch: The destination branch of the merge.<br> hash_value: The SHA1 hash value of the merge commit.<br> merged_commits: Unique hash value prefixes of the commits merged with this commit.<br> author_name: The author name.<br> author_email: The author email address.<br> author_date: The authoring timestamp.<br> commit_name: The committer name.<br> commit_email: The committer email address.<br> commit_date: The commit timestamp.<br> log_message_length: The length of the git commit messages (in characters).<br> file_count: Files changed with this commit.<br> lines_added: Lines added to all files changed with this commit.<br> lines_deleted: Lines deleted in all files changed with this commit.<br> file_extensions: Distinct file extensions of files changed with this commit.<br> pull_request_id: ID of the GitHub pull request that has been merged with this commit (extracted from log message).<br> source_user: GitHub login name of the user who initiated the pull request (extracted from log message).<br> source_branch : Source branch of the pull request (extracted from log message).</p> <p><strong>comparison_project_sample_800.csv</strong><br> A CSV file with information about the 800 projects in the comparison sample.</p> <p><strong>commits_default_branch_before_mid.csv<br> commits_default_branch_after_mid.csv</strong><br> One CSV file with information about all commits to the default branch before and after the medium date of the commit history. Only commits modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the same columns as the commits tables described above.</p> <p><strong>merges_default_branch_before_mid.csv<br> merges_default_branch_after_mid.csv</strong><br> One CSV file with information about all merges into the default branch before and after the medium date of the commit history. Only merges modifying, adding, or deleting Java or Ruby source code files were considered. Those CSV files have the same columns as the merge tables described above.</p>
INTEND D 2.1 Transport projects & future technologies synopses database
<p>This excel file provides the database that contains all the project synopses that were carried out in the INTEND D 2.1 Transport projects & future technologies handbook deliverable. The reviews are divided into transport modes and contain the technology themes that were identified and brief summaries of what each project that was reviewed had researched. The database contains a total of 354 transport projects that have carried out hard technology research, predominantly funded under FP7 (2010-2014), all H2020 projects that have been funded as well as other international projects.</p>
HiperLAM Project Video
<p>This is the Project Video of the HiperLAM Project, describing the laser-based additive manufacturing approach coordinated by Orbotech. The Project focuses upon a LIFT-based process (laser-induced forward transfer) in 2 demonstrator applications, namely Fingerprint sensors and RFID tags. The aim of the project is to demonstrate cost and speed improvements by displacing existing processes with the LIFT-based digital printing technology. </p>
Diversify works. introductory video of Diverfarming H2020 project
<p>Introductory video about H2020 Diverfarming project. We show here the benefits of diversified cropping systems and why Europe needs crop diversification.</p> <p>With the long-term view of increasing diversification and biodiversity in Europe and fostering sustainable development of bioeconomy, the Diverfarming consortium come together to develop and deploy innovative farming and agribusiness strategies. Diverfarming will increase the long-term resilience, sustainability and economic revenues of agriculture across the EU by assessing the real benefits and minimising the limitations, barriers and drawbacks of diversified cropping systems under low-input agronomic practices that are tailor-made to fit the unique characteristics of six EU pedoclimatic regions (Mediterranean south and north, Atlantic central, Continental, Pannonian and Boreal), and by adapting and optimising the downstream value chains organization. This approach will provide: i) increased overall land productivity; ii) more rational use of farm land and farming inputs (water, energy, machinery, fertilisers, pesticides); ii) improved delivery of ecosystem services by increments in biodiversity and soil quality; iii) proper organization of downstream value chains adapted to the new diversified cropping systems with decreased use of energy; and iv) access to new markets and reduced economy risks by adoption of new products in time and space. The diversified cropping systems will be tested in field case studies for major crops within each pedoclimatic region. In the end, Diverfarming focuses on research and innovation for rural development, with emphasis on developing new framework systems and business models adapted to the rural context of each pedoclimatic area of the EU, to foster sustainable growth through adoption of diversification, sustainable practices and efficient use of resources.</p>
Monimuotoistaminen toimii. Introductory video of Diverfarming H2020 project
<p>Introductory video about H2020 Diverfarming project. We show here the benefits of diversified cropping systems and why Europe needs crop diversification.</p> <p>With the long-term view of increasing diversification and biodiversity in Europe and fostering sustainable development of bioeconomy, the Diverfarming consortium come together to develop and deploy innovative farming and agribusiness strategies. Diverfarming will increase the long-term resilience, sustainability and economic revenues of agriculture across the EU by assessing the real benefits and minimising the limitations, barriers and drawbacks of diversified cropping systems under low-input agronomic practices that are tailor-made to fit the unique characteristics of six EU pedoclimatic regions (Mediterranean south and north, Atlantic central, Continental, Pannonian and Boreal), and by adapting and optimising the downstream value chains organization. This approach will provide: i) increased overall land productivity; ii) more rational use of farm land and farming inputs (water, energy, machinery, fertilisers, pesticides); ii) improved delivery of ecosystem services by increments in biodiversity and soil quality; iii) proper organization of downstream value chains adapted to the new diversified cropping systems with decreased use of energy; and iv) access to new markets and reduced economy risks by adoption of new products in time and space. The diversified cropping systems will be tested in field case studies for major crops within each pedoclimatic region. In the end, Diverfarming focuses on research and innovation for rural development, with emphasis on developing new framework systems and business models adapted to the rural context of each pedoclimatic area of the EU, to foster sustainable growth through adoption of diversification, sustainable practices and efficient use of resources.</p>
Diversificare funziona. Introductory video of Diverfarming H2020 project
<p>Introductory video in Italian about H2020 Diverfarming project. We show here the benefits of diversified cropping systems and why Europe needs crop diversification.</p> <p>With the long-term view of increasing diversification and biodiversity in Europe and fostering sustainable development of bioeconomy, the Diverfarming consortium come together to develop and deploy innovative farming and agribusiness strategies. Diverfarming will increase the long-term resilience, sustainability and economic revenues of agriculture across the EU by assessing the real benefits and minimising the limitations, barriers and drawbacks of diversified cropping systems under low-input agronomic practices that are tailor-made to fit the unique characteristics of six EU pedoclimatic regions (Mediterranean south and north, Atlantic central, Continental, Pannonian and Boreal), and by adapting and optimising the downstream value chains organization. This approach will provide: i) increased overall land productivity; ii) more rational use of farm land and farming inputs (water, energy, machinery, fertilisers, pesticides); ii) improved delivery of ecosystem services by increments in biodiversity and soil quality; iii) proper organization of downstream value chains adapted to the new diversified cropping systems with decreased use of energy; and iv) access to new markets and reduced economy risks by adoption of new products in time and space. The diversified cropping systems will be tested in field case studies for major crops within each pedoclimatic region. In the end, Diverfarming focuses on research and innovation for rural development, with emphasis on developing new framework systems and business models adapted to the rural context of each pedoclimatic area of the EU, to foster sustainable growth through adoption of diversification, sustainable practices and efficient use of resources.</p>
Diversify werke. Introductory video of Diverfarming H2020 project
<p>Introductory video in Dutch about H2020 Diverfarming project. We show here the benefits of diversified cropping systems and why Europe needs crop diversification.</p> <p>With the long-term view of increasing diversification and biodiversity in Europe and fostering sustainable development of bioeconomy, the Diverfarming consortium come together to develop and deploy innovative farming and agribusiness strategies. Diverfarming will increase the long-term resilience, sustainability and economic revenues of agriculture across the EU by assessing the real benefits and minimising the limitations, barriers and drawbacks of diversified cropping systems under low-input agronomic practices that are tailor-made to fit the unique characteristics of six EU pedoclimatic regions (Mediterranean south and north, Atlantic central, Continental, Pannonian and Boreal), and by adapting and optimising the downstream value chains organization. This approach will provide: i) increased overall land productivity; ii) more rational use of farm land and farming inputs (water, energy, machinery, fertilisers, pesticides); ii) improved delivery of ecosystem services by increments in biodiversity and soil quality; iii) proper organization of downstream value chains adapted to the new diversified cropping systems with decreased use of energy; and iv) access to new markets and reduced economy risks by adoption of new products in time and space. The diversified cropping systems will be tested in field case studies for major crops within each pedoclimatic region. In the end, Diverfarming focuses on research and innovation for rural development, with emphasis on developing new framework systems and business models adapted to the rural context of each pedoclimatic area of the EU, to foster sustainable growth through adoption of diversification, sustainable practices and efficient use of resources.</p>
Diversifikation funktioniert. Introductory video of Diverfarming H2020 project
<p>Introductory video in German about H2020 Diverfarming project. We show here the benefits of diversified cropping systems and why Europe needs crop diversification.</p> <p>With the long-term view of increasing diversification and biodiversity in Europe and fostering sustainable development of bioeconomy, the Diverfarming consortium come together to develop and deploy innovative farming and agribusiness strategies. Diverfarming will increase the long-term resilience, sustainability and economic revenues of agriculture across the EU by assessing the real benefits and minimising the limitations, barriers and drawbacks of diversified cropping systems under low-input agronomic practices that are tailor-made to fit the unique characteristics of six EU pedoclimatic regions (Mediterranean south and north, Atlantic central, Continental, Pannonian and Boreal), and by adapting and optimising the downstream value chains organization. This approach will provide: i) increased overall land productivity; ii) more rational use of farm land and farming inputs (water, energy, machinery, fertilisers, pesticides); ii) improved delivery of ecosystem services by increments in biodiversity and soil quality; iii) proper organization of downstream value chains adapted to the new diversified cropping systems with decreased use of energy; and iv) access to new markets and reduced economy risks by adoption of new products in time and space. The diversified cropping systems will be tested in field case studies for major crops within each pedoclimatic region. In the end, Diverfarming focuses on research and innovation for rural development, with emphasis on developing new framework systems and business models adapted to the rural context of each pedoclimatic area of the EU, to foster sustainable growth through adoption of diversification, sustainable practices and efficient use of resources.</p>
Virtual ChIP-seq predictions of binding of 36 transcription factor in Roadmap Epigenomics Project tissues
<p>This dataset contains predictions of Virtual ChIP-seq for binding of 36 transcription factors in Roadmap Epigenomics dataset tissues with matched DNase-seq and RNA-seq data.</p> <p>Tarball contains subfolders for each of the 36 TFs where Virtual ChIP-seq median MCC in validation cell types was > 0.3.</p> <p>Each subfolder contains gzipped BED files. Each file is named as <Tissue>_<Age>_<TF>_<Accession>_Predictions.bed.gz. Columns correspond to Chromosome, Start, End, <Tissue>_<Age>_<TF>_<Accession>, Posterior probability</p> <p>You can use the posterior probabilities provided in Virchip_PosteriorCutoffs_V3.0.0.tsv. These are posterior probability cutoffs which maximized MCC in H1-hESC cell type, or are set to 0.4 if there was no ChIP-seq data of that TF in H1-hESC (0.4 is the mode of all optimal posterior probability cutoffs in H1-hESC).</p>
Air quality data created by the hackAIR Horizon2020 project
<p>The current datasets comprise of air quality data collected or created within the hackAIR project (https://platform.hackair.eu/) all around Europe from February 2018 until November 2018.</p> <p>i) "measurements_arduino.xlsx": PM10 and PM2.5 measurements collected by hackAIR users with stationary hackAIR sensors (https://www.hackair.eu/hackair-home-v2/). These sensing devices are based either on an Arduino or a Wemos board. The first column is the unique identifier for the measurement in the hackAIR database. The date/time is in UTC timezone, while the unit of the pollutant value is μg/m3.</p> <p>ii) "measurements_bleair.xlsx": PM10 and PM2.5 measurements collected by hackAIR users with mobile hackAIR sensors (https://www.hackair.eu/hackair-mobile/). The first column is the unique identifier for the measurement in the database. The date/time is in UTC timezone, while the unit of the pollutant value is μg/m3.</p> <p>iii) "measurements_sky_photos.xlsx": Air pollution estimations from photos depicting sky. The hackAIR platform estimates the particulate matter content in the air from Flickr photos, photos from webcams and sky photos that users upload on the hackAIR mobile application, based on the colour of the sky. This is expressed as Aerosol Optical Depth (AOD). In the current dataset, the timezone is UTC, while AOD is unitless.</p> <p>The pollutant index is based on a scale created for the purposes of the hackAIR project.</p>
GATEMAN project, GNSS raw data in presence of spoofing
<p>GNSS raw data generated during the in-lab validation activities of spoofing detection and localization performed in the frame of the <strong>GATEMAN</strong> project. These files are grouped for each type of validation scenario defined. A word file describing the test setup is included.</p>
ISO TR 21965 Information and documentation -- Records management in enterprise architecture - Archi tool project file
<p>This file is a project file, in XML format, of the freeware too Archi (version 4.2.0) modeling the ArchiMate diagrams present in the ISO/DTR 21965:2019.</p> <p>Archi tool is freely available from https://www.archimatetool.com</p> <p>The purpose of the ISO/TR 21965:2019 is to provide a common reference for Records managers (or information managers in general) and Enterprise architects about requirements for records processes and systems. The goal is to establish the Records manager as a key stakeholder in Enterprise Architecture, by expressing the related Records Management Viewpoint.</p> <p>This viewpoint makes use of the concepts of “concerns” and “system of concerns” as defined in ISO/IEC/IEEE 42010:2011, and of the concepts of “stakeholders”, “viewpoint, “view” and “model” as also defined coherently in that standard and in the main Enterprise Architecture references of TOGAF and ArchiMate. With reference to ArchiMate, the main scope of this viewpoint is the Motivational aspect and the layers Strategy and Business, with minor considerations for the layers of Application and Implementation. The Open Group Architecture Framework (TOGAF) is used to inform how this Records Management Viewpoint relates to the Architecture Development Method (ADM).</p> <p>The edition of the file is work of the author, but the intelectual content of the file is the resulting of the work of the ISO working group responsible by the production of the Technical Report: ISO/TC 46/SC 11/WG 14 - Records requirements in Enterprise Architecture</p> <p> </p>
Data processing scripts and images from e-MERLIN project CY6213 used in Ghirlanda et al. 2019, Science
<p>Data processing scripts and images from e-MERLIN project CY6213 used in Ghirlanda et al. 2019, Science</p> <p> </p> <ul> <li>info.txt contains a general description of how the data was processed and the main results.</li> <li>pipeline.tar contains the data pipeline used to process the e-MERLIN observations</li> <li>imaging.py is the script used to produce the final images</li> <li>CY6213_images.tar contain the final images of the target source (not corrected by calibration factor, described in the imaging script).</li> </ul> <p> </p>
Topic Map for KPI analysis of e-Infrastructure projects
<p>This is a Topic Map from the e-IRG Knowledge Base used by the e-IRGSP5 project to analyse Key Performance Indicators (KPIs) for e-Infrastructure projects funded by Horizon 2020.</p>
Olfactory receptors from tuatara - supplementary data from the genome project
<p>Olfactory receptor genes identified from the genome assembly of tuatara (<em>Sphenodon punctatus</em>).</p> <p>File 1: List of intact OR genes</p> <p>File 2: alignment file used to infer phylogenetic relationship between intact tuatara ORs and other species of terrestrial squamates and two species of bird.</p> <p>File 3: Excel spreadsheet listing OR identification from scaffolds (includes data on pseudogenes and partials)</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.