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13,117 results for “Maps”
Landslide mapping using satellite imagery and machine learning algorithms
<p>Cyclone Idai made landfall on 15th March near Beira, Mozambique, and caused heavy rainfall across Mozambique, Malawi, Madagascar, and eastern Zimbabwe. Chimanimani District of Zimbabwe received 200 to 400 mm rainfall between 15th and 19th March, which caused widespread flooding and triggered thousands of landslides. This study aims to map the landslides in Chimanimani District and differentiate concurrent flooding from the landslides using high resolution PlanetScope imagery and DEM. Three machine learning algorithms namely, Random Forest, Artificial Neural Network, and Support Vector Machine have been deployed for the supervised landslide classification. </p> <p> </p>
A Systematic Mapping of the Classification of Open Educational Resources for Computer Science Education in Digital Sources (Data)
<p>Data from a Systematic Mapping of the classification of Open Educational Resources for Computer Science Education.</p> <p>Content:</p> <ul> <li>Studies selected</li> <li>Digital sources used to classify Open Educational Resources for Computer Science Education</li> <li>Computer Science domains explored by Open Educational Resources</li> <li>Approaches for the classification of Open Educational Resources for Computer Science Education</li> </ul>
To Automatically Map Source Code Entities to Architectural Modules with Naive Bayes: Replication Package
<p>This is the replication package for the JSS article To Automatically Map Source Code Entities to Architectural Modules with Naive Bayes. It provides the source data files and the r-script to produce analysis and images.</p>
Replication Package for the Paper: Understanding Software Architecture Erosion: A Systematic Mapping Study
<p>This is the replication package for the paper: "Understanding Software Architecture Erosion: A Systematic Mapping Study". It contains five files as described below:</p> <p><strong>1. List of the Selected Studies_SMS.xlsx</strong><br> includes the detailed information of the 73 selected studies.</p> <p><strong>2. Data Extraction.xlsx</strong><br> includes the extracted data based on the data items (i.e., D1-D16).</p> <p><strong>3. Sample of Study Selection.xlsx</strong><br> includes the 100 randomly selected papers from the search results and the results of each round selection.</p> <p><strong>4. Pilot Data Extraction.xlsx</strong><br> includes the pilot data extraction results from five papers.</p> <p><strong>5. Sample of Data Extraction.xlsx</strong><br> includes the data extraction results from another randomly selected five papers.</p>
Crossref Funder Registry - Mapping to top-level funding organizations
<p>The <a href="https://www.crossref.org/services/funder-registry/">Crossref Funder Registry</a> is an open registry of names and identifiers of research funding organizations. The registry has a hierarchical structure. Each organization may have relations with parent and child organizations.<br> <br> We provide a mapping of organizations in the Crossref Funder Registry to the corresponding top-level organizations. The mapping is based on version 1.34 of the Crossref Funder Registry, made available in an RDF file in the <a href="https://gitlab.com/crossref/open_funder_registry/">Crossref Funder Registry GitLab repository</a>.<br> <br> To create the mapping to top-level organizations, we first extracted the hierarchical structure of the Crossref Funder Registry based on all ‘narrower’ and ‘broader’ assignments. Using this hierarchical structure, we then identified for each of the 27,681 active organizations in the registry the corresponding top-level organizations. For most organizations, we identified a single top-level organization. However, organizations may have multiple parent organizations, and for some organizations we therefore identified multiple top-level organizations.<br> <br> Example: ‘H2020 Societal Challenges’ has ‘Horizon 2020 Framework Programme’ as its only parent organization, ‘Horizon 2020 Framework Programme’ has ‘European Commission’ as its only parent organization, and ‘European Commission’ does not have a parent organization. The top-level organization corresponding to ‘H2020 Societal Challenges’ therefore is ‘European Commission’.<br> <br> We ignored the following parent-child relations in order to avoid having cycles in the network of parent-child relations:</p> <ul> <li>Child organization: Ministry of Science and Technology of the People's Republic of China (CHN) [DOI: 10.13039/501100002855]<br> Parent organization: National Science and Technology Major Project (CHN) [DOI: 10.13039/501100018537]</li> <li>Child organization: University of Georgia (USA) [DOI: 10.13039/100007699]<br> Parent organization: College of William and Mary (USA) [DOI: 10.13039/100008277]</li> </ul> <p>We ignored the following parent-child relations because they seem to be mistakes:</p> <ul> <li>Child organization: College of William and Mary (USA) [DOI: 10.13039/100008277]<br> Parent organization: University of Georgia (USA) [DOI: 10.13039/100007699]</li> <li>Child organization: Fakulti Pertanian, Universiti Putra Malaysia (MYS) [DOI: 10.13039/501100006018]<br> Parent organization: Department of Child Health, University of Manchester (GBR) [DOI: 10.13039/100007546]</li> <li>Child organization: Pusat Penyelidikan dan Inovasi, Universiti Malaysia Sabah (MYS) [DOI: 10.13039/501100006243]<br> Parent organization: Workplace Health, Safety and Compensation Commission (CAN) [DOI: 10.13039/501100000183]</li> <li>Child organization: Workplace Health, Safety and Compensation Commission (CAN) [DOI: 10.13039/501100000183]<br> Parent organization: Shahid Beheshti University of Medical Sciences (IRN) [DOI: 10.13039/501100005851]</li> </ul>
Operando magnetic resonance imaging for mapping of temperature and redox species in thermo-electrochemical cells
<p>Raw data for our publication "Operando magnetic resonance imaging for mapping of temperature and redox species in thermo-electrochemical cells", including imaging files (Paravision), ASCII, MATLAB and EC Lab data files organised corresponding to each figure in the paper and supplementary information.</p>
Dataset (VII) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>MD simulation data of compound <strong>1</strong> in MSM <strong>2-<em>S</em><sub>3</sub></strong> conformations of the related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
Dataset (VIII) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>MD simulation data of compound <strong>1</strong> in MSM <strong>2-<em>S</em><sub>3</sub></strong> conformations of the related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
Dataset (V) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>MD simulation data of <strong>SB203580</strong> related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
MLR - Mapping Collaboration Challenges in Hybrid Working Environments of Information Technology Teams
<p> Supplementary artifact for Mapping Collaboration Challenges in Hybrid Working Environments of Information Technology Teams</p>
57 Block--63k or 50k scale Maps of South Asia--additional 2--20251024
<p>89 new map files to add to the set of 63k/50k Maps of South Asia, Block 57. An updated Index Map for Block 57 is included in this set of maps.</p>
63 Block--63k or 50k scale Maps of South Asia--additional 2--20251024
<p>96 new map files to add to the set of 63k/50k Maps of South Asia, Block 63. An updated Index Map for Block 63 is included in this set of maps.</p>
56 Block--63k or 50k scale Maps of South Asia--additional 2--20251024
<p>13 new map files to add to the set of 63k/50k Maps of South Asia, Block 56. An updated Index Map for Block 56 is included in this set of maps.</p>
53 Block--63k or 50k scale Maps of South Asia--additional 1--20251024
<p>99 new map files to add to the set of 63k/50k Maps of South Asia, Block 53. An updated Index Map for Block 53 is included in this set of maps.</p>
39 Block--63k or 50k scale Maps of South Asia--additional 2--20251024
<p>48 new map files to add to the set of 63k/50k Maps of South Asia, Block 39. An updated Index Map for Block 39 is included in this set of maps.</p>
43 Block--63k or 50k scale Maps of South Asia--additional 2--20251024
<p>89 new map files to add to the set of 63k/50k Maps of South Asia, Block 43. An updated Index Map for Block 43 is included in this set of maps.</p>
79 Block--63k or 50k scale Maps of South Asia--additional 1--20240105
<p>77 new map files to add to the set of 63k/50k Maps of South Asia, Block 79. An updated Index Map for Block 79 is included in this set of maps.</p>
47 Block--63k or 50k scale Maps of South Asia--additional 1--20251024
<p>56 map files of add to the set of 63k/50k Maps of South Asia, Block 47. An updated Index Map for Block 47 is included in this set of maps.</p>
73 Block--63k or 50k scale Maps of South Asia--additional 1--20251024
<p>97 new map files to add to the set of 63k/50k Maps of South Asia, Block 73. An updated Index Map for Block 73 is included in this set of maps.</p>
78 Block--63k or 50k scale Maps of South Asia--additional 1--20240105
<p>43 new map files to add to the set of 63k/50k Maps of South Asia, Block 78. An updated Index Map for Block 78 is included in this set of maps.</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.