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.
13,117
datasets available to search
ShareScore release 0.9.0
Dataset results
13,117 results for “Maps”
Datasets for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings
<p><strong>Introduction</strong></p> <p>The datasets for training and evaluating our model for explainable depression detection on Twitter aided by metaphor concept mappings proposed in the following paper:</p> <p>Sooji, Han, Rui Mao, and Erik Cambria. "Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings." In Proceedings of the 29th International Conference on Computational Linguistics (COLING), 2022. in press</p> <p>Source code for our model is available at <a href="https://github.com/soojihan/HAN/blob/main/README.md">github.com/soojihan/HAN</a>.</p> <p>These datasets were generated using the dataset proposed in Shen et al., 2017. The original dataset is available at <a href="https://github.com/sunlightsgy/MDDL">github.com/sunlightsgy/MDDL</a>.</p> <p> </p> <p><strong>Description</strong></p> <p>There are three datasets. </p> <p><strong>1. mdl_HAN: </strong>This dataset contains tweets and metaphor concept mappings (MCMs) for 5,899 positive (i.e. depressed) and 4,469 negative users. Tweets in this dataset are extracted from the original MDDL dataset (Shen et al., 2017). MCMs were extracted using MetaPro (Mao et al., 2022). Please refer to our paper for more details.The name of each subfolder under the 'positive' and 'negative' subfolders is tweet userid. Each user's folder contains one or two json files:</p> <ul> <li>[userid].json: This json file contains tweet text objects, each of which is represented by [timestamp, tweet text].</li> <li>[userid]_cm.json: This json file contains MCMs, each of which is represented by [timestamp, MCM]</li> </ul> <p>Note that some users do not have MCMs. There's no [userid]_cm.json file in such users' folders.</p> <p><strong>2. imdl_HAN: </strong>This dataset has the same contents and structure as mdl_HAN except that explicit linguistic cues for depression (i.e., “I’m/I was/I am/I’ve been diagnosed depression” and words containing “depress”, “diagnos”, “anxiety”, “bipolar” and “disorder”) were removed from all tweets.</p> <p><strong>3. sampled_training_eval_data: </strong>This dataset contains 5 randomly sampled cross-validation sets. Each of the five set contains train.csv, test,csv and dev.csv. Each csv file contains user ids.</p> <p> </p> <p><strong>Remarks</strong></p> <p>If you use the datasets, please cite our paper:</p> <p>Sooji, Han, Rui Mao, and Erik Cambria. "Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings." In Proceedings of the 29th International Conference on Computational Linguistics (COLING), 2022. in press</p> <p> </p> <p><strong>Contact</strong></p> <p>If you have any questions about the datasets or source code for our model, please contact <a href="https://soojihan.github.io/">Sooji Han</a>.</p> <p> </p> <p><strong>References</strong></p> <p>Shen, Guangyao, Jia Jia, Liqiang Nie, Fuli Feng, Cunjun Zhang, Tianrui Hu, Tat-Seng Chua, and Wenwu Zhu. "Depression detection via harvesting social media: A multimodal dictionary learning solution." In <em>IJCAI</em>, pp. 3838-3844. 2017.</p> <p>Rui Mao, Xiao Li, Mengshi Ge, and Erik Cambria. 2022. MetaPro: A computational metaphor pro- cessing model for text pre-processing. Information Fusion, 86-87:30–43.</p> <p> </p>
Land cover maps: Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features
<p>Land cover maps obtained with Gaussian Processes, Random Forest, Multilayer Perceptron and Lightweight Temporal Self-Attention models for the year 2018 with Sentinel-2 acquisitions.</p> <p>For further details see section VII-A-2 (Results-Performance results in the Southfrance area-Qualitative results) of the article "Land Cover Classification with Gaussian Processes using spatio-spectro-temporal features ". This article is available <a href="https://hal.archives-ouvertes.fr/hal-03781332">here</a>.</p>
Global potential invasion maps of traded birds under climate and land-cover change
<p>Biological invasions rank among the top five threatening factors affecting biodiversity, but ongoing changes in climate and land cover might exacerbate risks. We used species distribution models for 609 traded bird species on the CITES list to examine the combined effects of projected climate change and land-cover change worldwide on the potential range expansion of bird species with commercial value as pets. The maps of potential invasion (may be inferred as the invasion risk) have been provided in the main manuscript and here, the potential invasion dataset for the current and future times is provided including the species distribution maps, all as GeoTiff files. The maps for the future time are provided for different future years and over a range of climate scenarios (SSP245, SSP370, and SSP585).</p>
Data for the paper "Mapping the space of quasisymmetric stellarators using optimized near-axis expansion"
<p>Data for the paper "Mapping the space of quasisymmetric stellarators using optimized near-axis expansion"</p>
Supplementary data for "Mapping global dynamics of benchmark creation and saturation in artificial intelligence"
<p>Supplementary data for the article "Mapping global dynamics of benchmark creation and saturation in artificial intelligence"</p>
Large scale eDNA monitoring of multiple aquatic pathogens as a tool to provide risk maps for wildlife diseases
<p>Multiple parasites and pathogens cause disease in aquatic wildlife and in aquaculture species, generating a need for monitoring and management. Conventional disease monitoring methods involve laborious, costly and invasive capture and examination of host species, and require specialised expertise for every host and pathogen of interest. These restrictions could be alleviated by using pathogen detection techniques based on environmental DNA that provide simultaneous surveys of multiple aquatic pathogens across different host taxa. This would also be valuable for approaches employing parasite diversity as bioindicators of ecosystem disturbance, which suffer from similar restrictions. Here, we tested the potential for simultaneous detection of four wildlife pathogens in water samples from 280, mainly riverine, sites across Switzerland. We targeted the crayfish pathogen <em>Aphanomyces astaci, </em>the amphibian pathogen <em>Batrachochytrium dendrobatidis, </em>and the fish pathogens <em>Saprolegnia parasitica</em> and <em>Tetracapsuloides bryosalmonae</em>. The eDNA detection showed a widespread distribution of <em>A. astaci</em>, <em>S. parasitica</em> and <em>T. bryosalmonae</em>, although <em>A. astaci </em>and <em>T. bryosalmonae</em> were not detected in some alpine river catchments. <em>B. dendrobatidis</em> was detected only rarely, which was expected since the sampling did not target amphibian breeding sites. Co-detection rates were higher in rivers than in lakes, likely reflecting the habitat preferences and distributions of the host species. We discuss the advantages and limitations of eDNA-based pathogen monitoring and list a set of recommendations for managers. Our study illustrates how eDNA-based techniques can monitor several pathogen species concurrently, thus facilitating more comprehensive disease monitoring schemes. Combined with metabarcoding approaches in the future, eDNA based sampling and detection can facilitate the incorporation of parasite and pathogen occurrence and diversity as an indicator for aquatic ecosystem health, and for revealing the hidden biodiversity and structure of parasite communities.</p>
Fig. 1. Distribution map for Bittacidae Handlirsch, 1906 in A review of Bittacidae (Mecoptera) in Guizhou, China with descriptions of three new species
Fig. 1. Distribution map for Bittacidae Handlirsch, 1906 in Guizhou, China.
Epitope Mapping of VZV-specific skin TRM
<p>T cell lines were generated from skin biopsies taken from rash site and contralateral (uninvolved) site skin of persons recovered from herpes zoster at days 45, 90, and 360 from rash onset. The cell lines were screened for reactivity to all VZV open-reading frame (ORF) products using high throughput assays designed to activate and measure responses of only CD8 T cells or only CD4 T cells. Artificial antigen presenting cells expressing a single HLA class I molecule from a study subject and a single VZV ORF was used to activate CD8 T cells. Secreted IFNgamma was measured (data represent OD450) in culture supernatants using ELISA. For CD4 T cells, crude proteins made by in vitro transcription translation (IVTT) was used as antigen. T cell proliferation was measured using 3H-thymidine incorporation assay (data represent CPM).</p> <p>Proteins that generated positive responses were mapped to epitope containing proteins using B-LCL as antigen presenting cells. Further studies using pan-specific HLA-DR, HLA-DP, or HLA-DQ blocking antibodies were done to identify the restricting HLA locus of the CD4 T cell response. More detailed studies confirmed the restricting HLA molecule through using artificial antigen presenting cells expressing single HLA class I (for CD8 T cells) or class II (for CD4 T cells). IFNg ELISA (OD450) was used to measure T cell activation.</p>
Inundation map of Mandra's flood event (Athens, Greece) 2017
<p>In this dataset we manually reproduced and we share as a shape file the flood extent derived by the National Observatory of Athens BEYOND team, through the processing of a WordlView-4 satellite high resolution (31 cm) image on 21/11/2017 and with ground observations, of the flood event which hit Mandra, Athens, Greece in 15th of November 2017 (Bellos et al., 2020).</p> <p>It is available online by BEYOND team at <a href="http://beyond">http://beyond</a> <a href="http://eocenter.eu/index.php/web-services/floodhub">eocenter.eu/index.php/web-services/floodhub</a>). </p> <p>Another one flood dataset is provided in:</p> <p>https://zenodo.org/record/7140750</p> <p>References</p> <p>Bellos, V., Papageorgaki, I., Kourtis, I., Vangelis, H., Kalogiros, I., Tsakiris, G. (2020).<br> Reconstruction of a flash flood event using a 2D hydrodynamic model under spatial and temporal variability of storm.<br> Natural Hazards, 101(3), 711-726.</p> <p>The dataset is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-sa/4.0/): CC BY-NC-SA) You are free to: Share — copy and redistribute the material in any medium or format; Adapt — remix, transform, and build upon the material, for not commercial use, under the following terms:</p> <p>1) Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made.</p> <p>2) You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.</p> <p>3) ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.</p>
Text-fig. 1. Map of Primorye region, Russia. a – Razdolnaya coal basin; b – Partizansk coal basin. in Angiosperm Diversification In The Early Cretaceous Of Primorye, Far East Of Russia
Text-fig. 1. Map of Primorye region, Russia. a – Razdolnaya coal basin; b – Partizansk coal basin.
Text-fig. 1. Geological map of South Bohemian Basins. in Plant Mesofossils From The Late Cretaceous Klikov Formation, The Czech Republic
Text-fig. 1. Geological map of South Bohemian Basins.
Text-fig. 2. Schematic map of the Prague Basin with position of the Loděnice – vinice locality. in Early Complex Tiering Pattern: Upper Ordovician, Barrandian Area, The Czech Republic
Text-fig. 2. Schematic map of the Prague Basin with position of the Loděnice – vinice locality.
Africa map showing population and grid infrastructure.
<p>Used for Wikipedia. Produced using <a href="https://github.com/pypsa-meets-earth/pypsa-earth">PyPSA-Earth</a> and the plotting scripts given in <a href="https://github.com/pypsa-meets-earth/documentation">https://github.com/pypsa-meets-earth/documentation</a>/ Credits to the PyPSA-Earth authors (previously PyPSA-Africa authors).</p> <p> </p>
The ScaleMaster: The decompostion of Pan-Scalar, Interactive Map (OSM,Google Maps,IGN scan)
<p>The ScaleMaster diagram of Brewer and Buttenfield, "where the scaleLine replaces the timeLine", is a formal tool (Excel sheets) designed to formalize the rules for manual map design and "emphasize changes to the map display" . Inspired by Brewer and Buttenfield, we use ScaleMaster to standardize and formalize changes while zooming and exploring each of pan-scalar map (OSM,Google Maps,Scan IGN). In our methodology, however, we go a step further. The timeline of exploration is also examined in addition to the scaleline of zooming. We focus on map design practices that account for pan-scalar map exploration. For example, we account for generalization changes between scales based on empirically or theoretically justifiable reasons.</p> <p>we use ScaleMaster to analyze particular and common geographic entities in the maps (including rivers, urban areas, bus stations, and administrative borders) representing but a fraction of all map ontologies (e.g., water, roads, transportation networks, relief, points-of-interest, vegetation, administrative districts). We constructed a ScaleMaster for each of the three pan-scalar maps (OSM, Google Map, Scan IGN). </p> <p>Our hope is that this first analysis, and the resulting categories below, will lead to critique, comment, and iterative improvement in the future. In other words, our initial findings are just that – outcomes that further exploration on pan-scalar maps can add to, revise, and improve upon. </p>
Hollywood Cemetery Map
Map at the entrance of Hollywood Cemetery in Richmond, VA. Scanned with [Trnio plus beta](https://www.trnio.com/) in lidar mode. Edited in the app, applied texture defragmentation filter in Meshlab. Source: Objaverse 1.0 / Sketchfab
pack of old maps
pack de mapas viejos en formato fbx, ideal para demos o animaciones Source: Objaverse 1.0 / Sketchfab
Grand Canyon National Park 3D Aerial Map
Map of the Grand Canyon National Park in Arizona. Featuring the Kaibab Plateau, Colorado River, and Little Colorado River Gorge. The Grand Canyon is the second-most visited National Park in the United States, at visitation of around 6 million people each year. The South Rim of the park is around 7,000 feet in elevation, while the North Rim is 8,000 to nearly 9,000 feet. From the Grand Canyon you can often see over 100 miles in many directions, with Flagstaff's San Francisco Peaks to the South, and Navajo Mountain in Utah to the Northeast. The Canyon is home to many Native American tribes, and thousands of archaeology sites are found from the rim to the river. Among notable archaeological sites is the Unkar Delta, with of 52 excavated sites from Ancestral Puebloan people (Hisatsinom) who lived there 800–1200 years ago, planting on the delta and growing corn, squash, beans. Enjoy this free map of one of my favorite places in the world, and just in my "backyard". -Nate Loper Source: Objaverse 1.0 / Sketchfab
Carved Block Normal Map RTI
A test of RTI on carved wood. Scans were done by AISOS, a part of LATIS Labs in the College of Liberal Arts at the University of Minnesota, Twin Cities. Source: Objaverse 1.0 / Sketchfab
Topographip map Yadkinville
Topographic map of Yadkinville N.Y from https://ngmdb.usgs.gov/topoview. Modeled and textured in Blender 2.8. Useful for art galleries, games or renders. Free (but credits are welcome :)) Source: Objaverse 1.0 / Sketchfab
Bosnia and Herzegovina municipality lowpoly map
This is a separated 143 extruded objects, each of them is form of a municipality of Bosnia and Herzegovina. And, one object represents the whole county form. Source: Objaverse 1.0 / Sketchfab
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.