Find research datasets worth reusing

opencc-by-4.0Jan 2024View details →

Data from: A Deep Learning Approach for Fast Muscle Water T2 Mapping with Subject Specific Fat T2 Calibration from Multi-Spin-Echo Acquisitions

This repository contains the imaging data (in NIfTI) and analysis code to reproduce the work presented in "A Deep Learning Approach for Fast Muscle Water T2 Mapping with Subject Specific Fat T2 Calibration from Multi-Spin-Echo Acquisitions" by Marco Barbieri, Melissa T. Hooijmans, Kevin Moulin, Tyler E. Cork, Daniel B. Ennis, Garry E. Gold, Feliks Kogan and Valentina Mazzoli. Citation: "Barbieri, M., Hooijmans, M.T., Moulin, K. et al. A deep learning approach for fast muscle water T2 mapping with subject specific fat T2 calibration from multi-spin-echo acquisitions. Sci Rep 14, 8253 (2024). https://doi.org/10.1038/s41598-024-58812-2" The source code for setting up the Deep Learning application can be found in the GitHub repository https://github.com/barma7/Deep_Learning_for_Muscle_T2_mapping.git

HAPPY: a deep learning pipeline for mapping cell-to-tissue graphs across placenta histology whole slide images

These two zipped folders contain all data necessary to train, validate and reproduce results from the paper. Unzipping the files will create 6 folders. Data from folders with the same name across both zips should be combined into one folder. The 'annotations' folder contains all ground truth annotations for training all three deep learning models. The 'datasets' folder contains images for training the nuclei localisation and cell classification models. The 'embeddings' folder contains cell embedding vectors and nuclei coordinates from two slides used to create nodes to train the graph tissue classification model. The 'graph_splits' folder contains regions defining the validation and test splits for the graph model. The 'slides' folder contains a sample region of a whole slide image as a .tiff file for running the inference demo. The 'trained_models' folder contains trained weights for each of the three models. Further instructions for dataset use and creation of custom datasets are available in the GitHub readme: https://github.com/Nellaker-group/happy.

opencc-by-4.0Jan 2024View details →

COS-1m: the first community-scale open space maps of 31 major cities in China created with the layered occlusion perception model

Correspondence to: Shihong Du Peking University Beijing 100871, China E-mail: shdu@pku.edu.cn COS-1m (Community Open Spaces) offers detailed, high-resolution maps of community-level open spaces across 31 major cities in China. Developed using the latest very high-resolution satellite imagery and a novel Layered Occlusion Perception Model (LOPM), this dataset ensures precise representation with a spatial resolution of 1 meter. In total, the COS-1m dataset covers an area of 46727.46 square kilometers. The COS-1m dataset addresses the critical challenge of layered occlusions in remote sensing imagery by accurately modeling and reconstructing the intricate layered structures of COS. This approach allows for the detailed delineation of occluded ground elements, overcoming the limitations of traditional single-layer mapping methods. The dataset reveals that, on average, 60.51 km² of COS area per city is occluded, constituting 10.18% of the total COS area, which had previously been overlooked. Key features of the COS-1m dataset include: <ul> <li>Four Types of Public Open Space Elements: Green spaces (1), blue spaces (2), squares (4), and walkways (6).</li> <li>Three Types of Coupled Open Spaces: waterfront woodlands (3), shaded squares (5), and shaded walkways (7).</li> </ul> The dual-layer mapping technique used in COS-1m provides a comprehensive overview of urban open spaces, capturing the vertical overlaps of various elements such as trees, water bodies, squares, and walkways. This dataset is a significant advancement in COS monitoring technology, filling a critical gap in community-scale Sustainable Development Goal (SDG) assessments. It provides urban planners and policymakers with valuable insights to promote more effective and sustainable urban development. COS-1m achieved an overall accuracy of 86.39% and an average F1-score of 77.47% across the 31 cities, demonstrating its robustness and reliability as a tool for urban space analysis and planning. If you use this dataset, please cite: "Lei, Y., Zhang, X., Xiong, S., Tan, G., & Du, S. (2025). Developing Layered Occlusion Perception Model: Mapping community open spaces in 31 China cities. Remote Sensing of Environment, 316, 114498."

Agricultural land use (raster) : National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021)

The dataset contains maps of the main classes of agricultural land use (dominant crop types and other land use types) in Germany, which are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in <a href="https://doi.org/10.1016/j.rse.2021.112831">Blickensdörfer et al. (2022)</a>. All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software <a href="https://force-eo.readthedocs.io/en/latest/">FORCE </a>- Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated. The map extent covers all areas in Germany that are defined in the respective year as cropland, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020).  Version v201: Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015). Version v202: Additional post-processing was performed to detect and mask additional non-plausible areas that were not adequately covered by the first post-processing (e.g., areas with sparse vegetation, montane forests) based on the &bdquo;Ökosystematlas Deutschland“ (© Statistisches Bundesamt, Deutschland, 2024). As a consequence, the current version includes a new class “Small woody features on other land”. Furthermore, the class "permanent grassland" was refined. Each pixel that was classified as "cultivated grassland" in at least five years (between 2017 and 2022) was translated to "permanent grassland" in the annual maps. The maps are available as cloud optimized GeoTiffs, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the provided URL to the datasets (right click on the respective data set --> “copy link address”). By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately. Class-specific accuracies for each year are provided in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability.    References: Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831. BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022). BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022). Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124. Statistisches Bundesamt, Deutschland (2024). Ökosystematlas Deutschland https://oekosystematlas-ugr.destatis.de/ (last accessed: 08.02.2024). ___________________________________________________________________________ National-scale crop type maps for Germany from combined time series of Sentinel-1, Sentinel-2 and Landsat data (2017 to 2021) © 2024 by Schwieder, Marcel; Tetteh, Gideon Okpoti; Blickensdörfer, Lukas; Gocht, Alexander; Erasmi, Stefan;  licensed under CC BY 4.0.  Funding was provided by the German Federal Ministry of Food and Agriculture as part of the joint project “Monitoring der biologischen Vielfalt in Agrarlandschaften” (<a href="https://www.agrarmonitoring-monvia.de/en/">MonViA</a>, Monitoring of biodiversity in agricultural landscapes). The study was financially supported by the European Environment Agency and the European Union’s Horizon Europe Research and Innovation programme under Grant Agreement No 101060423 (LAMASUS).

Learning Markovian Dynamics with Spectral Maps

Used in the following publications: <ul> <li> J. Rydzewski, T. Gökdemir Learning Markovian Dynamics with Spectral Maps J. Chem. Phys. 160, 091102 (2024) <a href="https://doi.org/10.1063/5.0189241">doi:10.1063/5.0189241</a> / <a href="https://arxiv.org/abs/2311.16411">arXiv:2311.16411</a> / <a href="https://zenodo.org/records/10678142">Zenodo:10678142</a> / <a href="https://www.plumed-nest.org/eggs/24/005/">plumID:24.005</a> </li> <li> J. Rydzewski Spectral Map: Embedding Slow Kinetics in Collective Variables J. Phys. Chem. Lett. 14, 5216 (2023) <a href="https://doi.org/10.1021/acs.jpclett.3c01101">DOI:10.1021/acs.jpclett.3c01101</a> / <a href="https://arxiv.org/abs/2404.01809">arXiv:2404.01809</a> </li> </ul>

Lake_TP: annual improved maps to understand the complete evolution of 9 thousand lakes on the Tibetan Plateau in 1991-2023

Lake_TP (shapefile format): this dataset records the annual spatial distributions and water areas of 9789 lakes (over 0.1 km²) that appeared on the Tibetan Plateau from 1991 to 2023.

Data from: Mapping wing morphs of Tetrix subulata using citizen science data: flightless groundhoppers are more prevalent in grasslands near water

To analyse the correlation between groundhopper wing morph and landscape characteristics, we collected the following data. GBIF observations of Tetrix subulata (Linnaeus, 1758) in the Netherlands were annotated with wing morph and sex (based on the images), and weather and landscape information (based on the location). The weather information is based on an interpolation of data from the Royal Netherlands Meteorological Institute (KNMI). Landscape and habitat information was characterised by determining the area of different area types, from the Basisregistratie Topografie (BRT) TOP10NL dataset, in a radius around the observation. The landscape information also includes Dutch physical-geographical regions. To check for possible effects of seasonality, the event date of the observation is included. To check for the effect of the landscape radius on the analysis, those analyses were repeated for different radiuses. Depending on the precision of the coordinate location, some radiuses could not be tested for some observations.  The file "data.csv" contains one row per observation-radius combination, with the following columns (* repeated for each radius): <table> <tbody> <tr> <td>Columns</td> <td>Description</td> </tr> <tr> <td>gbifID*</td> <td>ID of the observation on GBIF</td> </tr> <tr> <td>eventDate*</td> <td>Date of observation (YYYY-MM-DD)</td> </tr> <tr> <td>wing_morph*</td> <td>Wing morph annotation (long; short)</td> </tr> <tr> <td>sex*</td> <td>Sex annotation (female; male; obscured; multiple; reevaluate i.e. unknown)</td> </tr> <tr> <td>region*</td> <td>Physical-geographical region in which the observation was made</td> </tr> <tr> <td>temperature*</td> <td>Predicted temperature (average over 1991-2020; degrees Celsius)</td> </tr> <tr> <td>windspeed*</td> <td>Predicted windspeed (average over 1991-2020; meters per second)</td> </tr> <tr> <td>precipitation*</td> <td>Predicted precipitation (average over 1991-2020; millimeters)</td> </tr> <tr> <td>landscape_radius</td> <td>Landscape radius (50, 100, 200, 500, or 1000 meters)</td> </tr> <tr> <td>deciduous forest, grass, mixed forest, ...</td> <td>Total surface area of given area type in given radius around observation (square meters)</td> </tr> </tbody> </table>

Mapping of Plains Terrains in the Moon's South Polar Region

This page archives data used in the following article: Qiao, L., Xu, L., Head, J. W., Chen, J., Zhang, Y., Li, B., Ling, Z., 2024. Geological evidence for extensive basin ejecta as plains terrains in the Moon’s south polar region. Nature Communications, 15, 5783. <a href="https://doi.org/10.1038/s41467-024-50155-w" target="_blank" rel="noopener">https://doi.org/10.1038/s41467-024-50155-w</a> It contains ArcGIS shapefiles for the mapping of plains terrains in lunar south polar region (PolarPlains_Mapping.7z).

Functional and structural computed connectivity profiles for pre-defined whole-brain and cortical hull maps

This Repository holds Lead-DBS Lead-Mapper v.2.5.2 computed functional and structural estimated connectivity. The structural dMRI connectivity is computed based on the "HCP1000 6K" connecome and the fMRI connectivity "GSP 1000 Yeo 2011" connectome. Both connectivity metrices were computed using two pre-defined grids: <ol> <li> Cortical hull (1025 points). The grid was estimated based on the <a href="https:/github.com/neuromodulation/py_neuromodulation/blob/main/py_neuromodulation/ConnectivityDecoding/_get_grid_hull.m">SPM cortex_20484 grid</a> and subsampled by every 20th point. The grid is saved as a <a href="(https:/github.com/neuromodulation/py_neuromodulation/blob/main/py_neuromodulation/ConnectivityDecoding/mni_coords_cortical_surface.mat)">.mat file</a> and can be acessed through the main repository .</li> <li>Whole-brain (1236 points). Given the Lead-DBS provided <a href="https://github.com/neuromodulation/py_neuromodulation/blob/main/py_neuromodulation/ConnectivityDecoding/Automated%20Anatomical%20Labeling%203%20(Rolls%202020).nii">AAL3 atlas</a> the whole-brain .nii grid is subsampled by a factor of 150. See here the <a href="https://github.com/neuromodulation/py_neuromodulation/blob/main/py_neuromodulation/ConnectivityDecoding/_get_grid_whole_brain.py">grid computation</a>.</li> </ol> Both grids can be accessed trough the <a href="https://github.com/neuromodulation/py_neuromodulation/tree/main">py_neuromodulation</a> API using the <a href="https://github.com/neuromodulation/py_neuromodulation/blob/main/py_neuromodulation/nm_RMAP.py">nm_RMAP.py</a> module.

Supplementary Material for the Paper: "Mining Architectural Information: A Systematic Mapping Study"

The supplementary material is composed of three files: 1. SelectedStudies.pdf contains the 104 studies selected in this systematic mapping study. 2. PublicationVenues.pdf contains the major publication venues of the selected studies. 3. Dataset.xlsx contains the following Excel sheets: (1) Architecting Activities contains the 11 architecting activities, mined architectural information, datasets and sources used in the selected studies, and relevant studies. (2) Automatic Approaches contains tasks, automatic approaches, used sources, mined architectural information, and relevant studies. (3) Semi-automatic Approaches contains tasks, semi-automatic approaches, used sources, mined architectural information, and relevant studies. (4) Manual Approaches contains tasks, manual approaches, used sources, mined architectural information, and relevant studies. (5) General Tools  contains general tool names, description of each tool, URL of each tool (if provided), and relevant studies. (6) Automatic Tools contains tasks, automatic tool names, used sources, mined architectural information, URL of each tool (if provided), and relevant studies. (7) Semi-automatic Tools contains tasks, semi-automatic tool, used sources, mined architectural information, URL of each tool (if provided), and relevant studies.

opencc-by-nc-4.0Mar 2024View details →

Mapping fast DNA polymerase exchange during replication

<h2>Abstract</h2> Despite extensive studies on DNA replication, the exchange mechanisms of DNA polymerase during replication remain unclear. Existing models propose that this exchange is facilitated by protein partners like helicase. Here we present data, employing a combination of mechanical DNA manipulation and single fluorescent protein observation, that reveal DNA polymerase undergoing rapid and autonomous exchange during replication not coordinated by other proteins. The DNA polymerase shows fast unbinding and rebinding dynamics, displaying a preference for either exonuclease or polymerase activity, or pausing events, during each brief binding event. We also observed a 'memory effect' in DNA polymerase rebinding, i.e., the enzyme tends to preserve its prior activity upon reassociation. This effect, potentially linked to the ssDNA/dsDNA junction's conformation, might play a role in regulating binding preference enabling high processivity amidst rapid protein exchange. Taken together, our findings support an autonomous replication model that includes rapid protein exchange, burst of activity, and a 'memory effect' while moving processively forward.  <h2>Data Set Description</h2> The study presents findings on the autonomous exchange mechanisms of DNA polymerase at the replication fork. Employing a combination of mechanical DNA manipulation and single fluorescent protein observation, the research reveals rapid and autonomous exchange of DNA polymerase, independent of protein partners like helicase. It highlights the enzyme's preference for exonuclease or polymerase activity and a 'memory effect' during binding events. In this data repo, we provided all the raw data used to reproduce the findings. <h2>Methodology</h2> The single-molecule experiments were performed at room temperature in a 5-channel microfluidic flow cell using the LUMICKS C-Trap instrument that combines dual optical trapping, confocal microscopy, and microfluidics for single-molecule assays. Data analysis was conducted using Python, Origin, and MATLAB. Detailed methodology and instrument specifications are provided in the associated publication. <h2>File Formats</h2> <ul> <li> All data is obtained from C-trap in .tdms format. </li> </ul> <h2>Usage Notes</h2> The custom-written python scripts used in this study is available at <a href="https://github.com/longfuxu/DNAPolymeraseProject">https://github.com/longfuxu/DNAPolymeraseProject</a>, under the MPL-2.0 license. The repository includes example dataset, example Jupiter notebook, along with a detailed README file for instructions on installation and usage. <h2>Acknowledgments</h2> We thank Seyda Aca and Sandrine D'Haene for assistance with protein purification and DNA construction, Noémie Danné for help with implementing the step-fitting algorithm. We thank Erwin Peterman for critical reading and constructive feedbacks of this manuscript. This work was financially supported by a PhD fellowship from China Scholarship Council (To L.X., funding No. 201704910912), the European Union H2020 Marie-Sklowdowska Curie International Training Network AntiHelix (To G.J.L.W., funding No. 859853), and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program MONOCHROME (to G.J.L.W., funding No.883240). <h2>Competing Interest</h2> The combined optical tweezers and fluorescence technologies used in this article are patented and licensed to LUMICKS B.V., in which M.T.J.H., and G.J.L.W. declare a financial interest. All other authors declare that they have no competing interests. <h2>Author Contributions</h2> L.X. and G.J.L.W. conceptualized the research. L.X. prepared protein samples and collected single-molecule data and analyzed data; M.T.J.H. analyzed data; L.X., M.T.J.H. and G.J.L.W. wrote and edited the manuscript; G.J.L.W. supervised the project; the manuscript is read, revised, and confirmed by all the listed authors.

Dataset for 'Mapping twist-tuned multi-band topology in bilayer WSe$_2$'

Datasets and code for the manuscript 'Mapping twist-tuned multi-band topology in bilayer WSe2.' See 'README.txt' for further details.

Reduced Gun Violence Frame Corpus data set for the Text2Story 2024 article: "Evaluating the Ability of Computationally Extracted Narrative Maps to Encode Media Framing"

Title: Simplified Gun Violence Frame Corpus (GVFC) Subset Description: This data set is a simplified subset of the Gun Violence Frame Corpus (GVFC) from Liu et al. (2019). The original GVFC consists of 1300 news articles in English from multiple U.S. based sources extracted during the year 2018, focusing on media frames commonly used when reporting the issue of Gun Violence. The original data set has 9 types of frames, including both issue-specific and generic frames. Due to high computational costs in our analysis methods, we decreased the data set size from 1300 articles to 131 articles using stratified sampling, maintaining the original distribution of the frame labels. We also manually searched for the original sources of each article based on its headline and added the missing temporal information and news source to the data set, as it was required by our algorithms. To further reduce the complexity of the framing model and account for the smaller data set size, we grouped the original nine frames into three higher-level frames: 1. Frame 1: Political Issues - Combining the first, second, and third frames, which focus on political issues mostly related to gun control. 2. Frame 2: Public Services - Combining the fourth and fifth frames, which focus on mental healthcare issues, as well as school and public safety. 3. Frame 3: Cultural and Societal Issues - Combining the last four frames, which are oriented towards cultural or societal issues, including discussions around race and ethnicity, public opinion, and economic consequences. The resulting simplified data set contains 131 news articles, each labeled with one of the three higher-level frames, along with the necessary temporal information and news source for the narrative extraction process. If you use this data set, please make sure to cite the original GVFC paper and our workshop paper please.  References: <ol> <li>Liu et al. (2019) "Detecting Frames in News Headlines and Its Application to Analyzing News Framing Trends Surrounding US Gun Violence", 23rd Conference on Computational Natural Language Learning (CoNLL 2019).</li> <li>Concha Macías, Sebastián and Keith Norambuena, Brian (2024). "Evaluating the Ability of Computationally Extracted Narrative Maps to Encode Media Framing", Text2Story 2024 Workshop, ECIR 2024.</li> </ol>

opencc-by-nd-4.0Jun 2023View details →

Figure 3. Current distribution map for the genera Anarrhotus Simon, 1902 in First records of the genera Anarrhotus Simon, 1902 and Gelotia Thorell, 1890 from India (Araneae: Salticidae)

Figure 3. Current distribution map for the genera Anarrhotus Simon, 1902 and Gelotia Thorell, 1890.

opennotspecifiedMar 2024View details →

MAP 2. Camposgryllacris australis n. comb. and Fiancogryllacris rivimeridionalis n in Studies of Raspy Crickets: The Status of the Wingless American gryllacridines (Orthoptera: Gryllacrididae)

MAP 2. Camposgryllacris australis n. comb. and Fiancogryllacris rivimeridionalis n. comb., distribution.

On the utility of Cas13d for genetic interaction mapping

Scripts to calculate tau values and genetic interaction (GI) scores in knockdown experiments using Cas13d.

A Systematic Mapping of Games for Teaching Programming

<div>To answer the Research Question RQ1 - Which games are used to teach programming? the following Table (appendix A) was created. The table's fields are described as follows:</div> <div> <ul> <li>Code: Reference code used in this mapping.</li> <li>Name: Name of the game as described in the study, in omitted cases it has been inserted (name not found).</li> <li>References: Bibliographic reference in APA format.</li> </ul> </div>

opennotspecifiedMar 2024View details →

MAP 1. A in Discovery of the cicada genus Becquartina Kato, 1940 (Hemiptera: Cicadidae: Cicadinae) in India with the description of a new species from Meghalaya

MAP 1. A. Map showing the location of Meghalaya on India map. B. Location of Meghalaya (type locality) on Northeast India. C. Map showing the distribution of Becquartina bicolor sp. nov. in Meghalaya.

opencc-by-4.0Apr 2024View details →

TractOracle - Maps and masks for TractoInferno

"Partial volume maps" to run Particle Filtering Tractography on TractoInferno. The WM-GM interface was obtrained by dilating the WM and taking the intersection between the dilated maps and the GM map. The tracking mask is the dilated WM mask.