Find research datasets worth reusing

Predicted Spatially Complete Zoning Map of North Carolina

Spatially-complete zoning map of North Carolina, USA. The results folder contains results of a machine learning (random forest) model predicting 3 core district zones (residential, non-residential, and mixed use) and 13 sub-district zones (open space, industrial, commercial, office, planned use, high-density residential, medium-high-density residential, medium-density residential, medium-low-density residential, low-density residential, agricultural residential, mixed use, and downtown). Results are provided as 30-m rasters (.tif) with each value corresponding to a zoning district. Table containing zone district ID (number) and zone district name (character string) is included in zone_classification.csv. Final (spatially complete statewide maps) can be found in the final_predicted folder. This folder includes Statewide core district results in NC_predicted_core.tif and statewide sub-district results in NC_predicted_sub.tif.  Zoning was generalized and reclassified into 3 core district zones and 13 sub-district zones (described above). Reclassified zoning data, collected from 39 counties in North Carolina is provided in the observed folder with core districts in core_district_observed_zones.tif and sub-districts in sub_district_observed_zones.tif. Also in this folder is zoning_implementation_NC.csv which includes links to the source data (zoning map and zoning ordinance) for all collected data. Two models were created to predict zones under different data availability scenarios (i.e., scenarios that assume different levels of data availability). Predictions labeled “within_county” utilized the within-county model which predicts zoning districts in areas where zoning data is partially available for that county. To approximate scenarios of incomplete data accessibility, 20% of the data was randomly removed from training and reserved for independent performance assessments. Predictions labeled “between-county” utilized the between-county model which predicts zoning districts in areas where zoning data is inaccessible. To approximate this scenario, multiple between-county model iterations were computed by randomly removing entire counties from the training dataset and computing performance metrics on the removed (test) counties. Predictions are provided for both core districts and sub-districts (described above). Results from these models can be found in the predicted folder. This folder contains four subfolders: core_district_within_county, sub_district_within_county, core_district_between_county, and sub_district_between_county. Within each of these folders are predicted maps 30-m raster (.tif), performance reports including precision, recall, and f1 score overall and per district (.csv), and accuracy maps (3-km grid shapefile [.shp, .shx, .prj, .dbf]) with values corresponding to the proportion of misclassified pixels within a grid cell. Multiple randomized testing county samples were conducted for the between-county models. Each random sample is labeled r*_ where * is replaced with a number between 1 and 15.

Single-Cell Mapping Reveals Several Immune Subsets Associated with Liver Metastasis of Pancreatic Ductal Adenocarcinoma

Identifying a metastasis-correlated immune cell composition within the tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) will help to develop promising and innovative therapeutic strategies. Twenty-six samples from 11 patients (including 11 primary tumor tissues, 10 blood, and 5 lymph nodes) with different stages were used to develop a multiscale immune profile. High-dimensional single-cell analysis with mass cytometry was performed to search for metastasis-correlated immune changes in the microenvironment. The details about the files uploaded are as follows: 1. panelA_Blood.zip includes 10 .fcs files from blood samples in Panel A; 2. panelA_LN.zip includes 5 .fcs files from lymph node samples in Panel A; 3. panelA_Tumor.zip includes 11 .fcs files from tumor tissue samples in Panel A; 4. panelB_Tumor.zip includes 11 .fcs files from tumor tissue samples in Panel B; 5. panel_metadata.xlsx describes marker used in Panel A and B; 6. sample_metadata.xlsx describes detailed sample information.

opencc-by-4.0Jul 2022View details →

Material stock map of CONUS

Humanity's role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the 'anthropocene', as humans are 'overwhelming the great forces of nature'. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed 'manufactured capital', 'technomass', 'human-made mass', 'in-use stocks' or 'socioeconomic material stocks', they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with 'real' (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called 'built structures') represent the overwhelming majority of all socioeconomic material stocks.This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.Spatial extent This dataset covers the whole CONUS. Due to upload constraints, detailed data were split into 7 regions and were uploaded into sub-repositories - see related identifiers. (This repository holds aggregated values for the whole CONUS)<ul><li>Great Plains</li><li>Mid West</li><li>North East</li><li>Rocky Mountains</li><li>South</li><li>South West</li><li>West Coast</li></ul>Temporal extent The map is representative for ca. 2018.Data format The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided.Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types).Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e.<ul><li>t at 10m x 10m</li><li>kt at 100m x 100m</li><li>Mt at 1km x 1km</li><li>Gt at 10km x 10km</li></ul>For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv.Material layers Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers):A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), <a href="https://doi.org/10.5281/zenodo.5045337">doi:10.5281/zenodo.5045337.</a>Further information For further information, please see the publication. A web-visualization of this dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/us-stocks/">here</a>. Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.Publication D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gómez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, and H. Haberl (2023): Unveiling patterns in human dominated landscapes through mapping the mass of US built structures. Nature Communications 14, 8014. <a href="https://doi.org/10.1038/s41467-023-43755-5">https://doi.org/10.1038/s41467-023-43755-5</a>Funding This research was primarly funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404.Acknowledgments We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on <a href="https://eodc.eu/">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC, and Wolfgang Wagner for granting access to preprocessed Sentinel-1 data.

opencc-by-4.0Dec 2022View details →

Mapping the epigenomic landscape of human monocytes following innate immune activation reveals context-specific mechanisms driving endotoxin tolerance

Processed datasets for publication at BMC Genomics. We exposed human primary monocytes from healthy donors (n=6) to interferon-γ or differing combinations of endotoxin (lipopolysaccharide), including acute response (2hr LPS; LPS2) and two models of endotoxin tolerance: repeated stimulations (6+6hr; LPS6:6) and prolonged exposure to endotoxin (24hr LPS; LPS24). Another subset of monocytes was left untreated (naïve; UT). We performed total RNA-seq and ATAC-seq for monocytes across these treatment conditions. The following processed datasets include the raw and normalised count data for each gene or ATAC peak, and the bedGraph and bigWig format for genome-wide signal data. [ bigWig files for RNA-seq (monocytes_*_mean.bw); bigWig files for ATAC-seq (Monocyte_ATAC_*_mean_normalised.by.1/size.factor.bw); bedgraph files for eRNA visualization (*RPKM_FR.bedgraph.gz); raw and normalised count files for ATAC-seq and RNA-seq data (Monocyte.featureCounts_RNAseq.txt.gz; Monocyte_filtered_log2.normalized_RNAseq_counts.txt.gz; Meta.file.txt.gz; Monocyte_raw.counts_ATACseq.txt.gz.).] Data curator: Ping Zhang

opencc-by-4.0Jul 2022View details →

Material stock map of CONUS - North East

Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks. This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Spatial extent This subdataset covers the North East CONUS, i.e. <ul> <li>CT</li> <li>DC</li> <li>DE</li> <li>MA</li> <li>MD</li> <li>ME</li> <li>NH</li> <li>NJ</li> <li>NY</li> <li>PA</li> <li>RI</li> <li>VA</li> </ul> For the remaining CONUS, see the related identifiers. Temporal extent The map is representative for ca. 2018. Data format The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided. Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types). Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e. <ul> <li>t at 10m x 10m</li> <li>kt at 100m x 100m</li> <li>Mt at 1km x 1km</li> <li>Gt at 10km x 10km</li> </ul> For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming.  Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv. Material layers Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers): A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), <a href="https://doi.org/10.5281/zenodo.5045337.">doi:10.5281/zenodo.5045337.</a> Further information For further information, please see the publication. A web-visualization of this dataset is available here. Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. Publication D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. In prep Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404. Acknowledgments We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on <a href="https://eodc.eu/">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

NPM3D dataset with instance label. Dataset used in paper "A Review of Panoptic Segmentation for Mobile Mapping Point Clouds"

NPM3D is a public benchmark for point cloud semantic segmentation, with 10 classes including: ground, building, pole (road sign and traffic light), bollard, trash can, barrier, pedestrian, car, natural (vegetation) and unclassified. Results are evaluated only w.r.t. 9 classes, disregarding the "unclassified" label. The data has been captured with a mapping-grade mobile laser scanning system in different cities in France. There are 4 regions designated for training, all captured in Paris and Lille; and 3 regions for testing, captured in Dijon and Ajaccio. The standard 10-class version described above has actually been derived from a more fine-grained version of the dataset by keeping only the most frequent labels. The original annotations feature 50 different semantic classes (most of which are very rare), and also individual object instance labels for the training regions. For panoptic segmentation, a new version has been generated that still uses the 10 semantic category labels listed above, but also includes instance labels. The classes ground, building and barrier are considered "stuff" and are not separated into instances. As no instance labels are available for the 3 test regions, our version for panoptic (or pure instance) segmentation only contains 4 different regions from Paris and Lille. Instead of a fixed training/test split all experiments therefore use 4-fold cross-validation.

Incorporating real-time traffic data into air quality mapping uncovers more severe exposure disparities at the community level

Data and codes for the XGBoost model training in our manuscript titled "Incorporating real-time traffic data into air quality mapping uncovers more severe exposure disparities at the community level"

opencc-zeroAug 2023View details →

dryad36/100

How to select appropriate hue ranges for sequential color schemes on choropleth maps? A quantitative evaluation using map reading experiments

We propose map reading experiments to quantitatively evaluate the selection of hue ranges for sequential color schemes on choropleth maps. In these experiments, 60 sequential color schemes with six base hues and ten hue ranges were employed as experimental color schemes, and a total of 414 college students were invited to complete identification, comparison, and ranking tasks. Both controlled and real-map experiments were performed, each involving a web-based survey and an eye-tracking experiment. In the controlled experiments, the shapes of the map objects were relatively regular, and attribute data were randomized. In contrast, the shapes were complex in real-map experiments, and real data were employed. Our findings show that widely used color schemes with a hue range of 0º yield poor performance in all tasks; 15º hue ranges yield good performance in the comparison and ranking tasks but poor performance in the identification task. For large hue ranges of 120-360º, participants showed good performance in the identification task but poor performance in the comparison and ranking tasks. For 30-60º hue ranges, participants achieved excellent performance in the comparison and ranking tasks and acceptable performance in the identification task. We also found that the ratings of 0-60º ranges were high.

opencc-by-4.0Jun 2023View details →

CSFD: The Corrected SFD Milky Way Dust Map

This package contains the full-sky CSFD Galactic dust reddening E(B−V) map and associated data products described in <a href="https://arxiv.org/abs/2306.03926">Chiang 2023 (arXiv:2306.03926)</a>. This dust map improves upon the widely used <a href="https://ui.adsabs.harvard.edu/abs/1998ApJ...500..525S/abstract">Schlegel, Finkbeiner, & Davis (1998, SFD)</a> map by removing a tomographically reconstructed cosmic infrared background (CIB) component that contaminates the original SFD. This new dust map enables more accurate Galactic extinction corrections for extragalactic photometry. More information can be found on the <a href="https://idv.sinica.edu.tw/ykchiang/CSFD.html">project website</a>.

UKCP18 RCM precipitation and temperature bias corrected using non-parametric quantile mapping method

The UKCP18 RCM PPE (Met Office Hadley Centre, 2018) projections of precipitation and daily average temperature were bias adjusted using a non-parametric quantile mapping method based on empirical quantiles (Boe et al, 2007, Gudmundsson et al, 2012). The datasets cover the period from December 1980 to November 2080 and are intended for use in climate change impact assessments, where the bias correction helps reduce biases in multiple statistics while maintaining projected climatic changes. ------------------------------------------------- Met Office Hadley Centre (2018): UKCP18 Regional Projections on a 12km grid over the UK for 1980-2080. CEDA, 8 March 2022. <a href="https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604">https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604</a> Boe, J.; Terray, L.; Habets, F. & Martin, E. Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies. International Journal of Climatology, 2007, 27, 1643-1655, doi: 10.1002/joc.1602. Gudmundsson, L.; Bremnes, J. B.; Haugen, J. E. & Engen-Skaugen, T. Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations - a comparison of methods. Hydrology and Earth System Sciences, 2012, 16, 3383-3390, doi:10.5194/hess-16-3383-2012. Paper Citation: We kindly ask users of this dataset to cite the paper that describes the dataset. The paper is published and can be accessed via the following link: <a href="https://doi.org/10.5194/essd-17-2113-2025" target="_new" rel="noopener">https://doi.org/10.5194/essd-17-2113-2025</a>. Please reference the paper as: Reyniers, N., Zha, Q., Addor, N., Osborn, T. J., Forstenhäusler, N., and He, Y.: Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain, Earth Syst. Sci. Data, 17, 2113–2133, https://doi.org/10.5194/essd-17-2113-2025, 2025.

Mapping past land cover on Poitiers in 1993 at Very High Resolution using GEOBIA approach and open data

This dataset contains a land cover map of Poitiers in 1993 over an area of 225km². The land cover map was achieved using aerial images of the French National Geographic Institute (IGN) and Landsat-5 TM images combined with remote sensing methods. Geographic Object-Based Image Analysis (GEOBIA) and Random Forest classifications produced a reliable land cover map at a 1m of spatial resolution. Orthophotos produced as well as training and validating polygons to achieve the classifications were added into this dataset. As land cover changes is crucial to land management, this map will help to understand changes from 1993 to now for urban, agricultural issues but also their impact on ecological processes. Data will be easily used in GIS applications for any users. This work is part of the thesis of Elie Morin which was funded by la région Nouvelle-Aquitaine and Grand Poitiers Communauté urbaine, among others.

Endmember spectra and classified mosaics derived from CRISM mapping data at the south pole of Mars

Overview Multispectral mapping data from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) provide a unique opportunity to characterize south polar ice deposits at higher spectral sampling, spatial resolution, or spatiotemporal coverage than previous work. This new perspective can help to constrain the nature and distribution of different mixtures of CO2 ice, H2O ice, and dust that influence the formation, evolution, and preservation of Mars climate records. We processed 1103 CRISM observations spanning southern summer of six Mars Years through a combination of k-means clustering and random forest classification. Using a set of 12 spectral endmembers directly tied to previous work with high-resolution CRISM targeted data, we made a series of temporally restricted mosaics showing surface spectral variation over time. The endmember set and classified mosaics produced in this work can provide critical context for future studies of the dynamic processes that shape south polar ice deposits. This is follow-on work to a previous study that produced a series of classified maps and a spectral library from CRISM targeted data. That work can be accessed with the following links: <ul> <li>Publication: <a href="https://doi.org/10.1029/2022JE007372">https://doi.org/10.1029/2022JE007372</a></li> <li>Repository: <a href="https://doi.org/10.5281/zenodo.6960943">https://doi.org/10.5281/zenodo.6960943</a></li> </ul> Contents The repository contains two .zip files, which can be expanded to access the files described below: <ul> <li>classified_mosaics.zip <ul> <li>lookup_files <ul> <li>SP_CRISM_RF_ColorMap.clr : An ESRI-formatted color map file that can be used to apply the endmember color scheme to random forest-classified maps in ArcGIS (see Symbology settings). Note that for continuity with Cartwright (2022), there are 21 colors in this file, though only endmembers associated with 12 of those colors are present in the classified mosaics.</li> <li>SP_CRISM_RF_ColorMap.txt : A text file that can be loaded into Python as a numpy array and used to generate a matplotlib color ramp. Row indices correspond to endmember numbers (see below) and columns are red, green, and blue values scaled from 0 to 1. Note that for continuity with Cartwright (2022), there are 21 colors in this file, though only endmembers associated with 12 of those colors are present in the classified mosaics.</li> <li>SP_CRISM_RF_Mapping_Endmember_Lookup.csv : A lookup table that can be used to cross-reference between endmember numbers (as stored in GeoTiffs or the indices of the colormaps above) and corresponding endmember names (C1m, Dc3m, etc.). Note that for continuity with Cartwright (2022), the endmember numbers are not sequential and instead correspond to the number of the original reference spectrum (e.g., C1m in this work has the same endmember number as C1 in Cartwright (2022)).</li> </ul> </li> <li>random_forest_classification <ul> <li>Contains GeoTiff mosaics of random forest classification results. Mosaics compile all observations falling in 10º bins of solar longitude (Ls) for a given year, provided that data was acquired in that range. Classified observations are stacked in ascending order of Ls. Mosaics of MSW data have a resolution of ~90 m/pixel while mosaics of MSP or combined MSP and MSW data have a resolution of ~180 m/pixel. Filenames indicate the observation type ("MSP", "MSW", or combined "MSP-MSW"), Mars Year ("MY28", "MY29", etc.) and Ls range ("Ls300-310" indicates all observations acquired between Ls 300º and Ls 310º). These images are not rendered to display colors consistent with the publication figures, but instead store the endmember classification for each pixel as a number from 0 to 21; use the contents of lookup_files to find the corresponding endmember name or render the image with the color scheme from the publication. To view rendered summary plots of each observation, see the contents of summary_plots below.</li> </ul> </li> <li>summary_plots <ul> <li>Contains summary plots of the endmember-classified mosaics provided in random_forest_classification above. Each plot presents the classified mosaic over a grey outline approximating the extent of high-albedo CO2 ice in the south polar residual cap (SPRC). Note that the summary view focuses on the area in and around the SPRC, but the full mosaics may extend beyond these bounds. </li> </ul> </li> </ul> </li> <li>spectral_library.zip <ul> <li>Contains spectral libraries with the median spectrum of each endmember as presented in Figure 3 in the publication. A basic text file listing the wavelength (WVL) and normalized reflectance values for each endmember is included (SP_CRISM_MappingEndmemberMedians.txt) as well as an ENVI-formatted spectral library (SP_CRISM_MappingEndmemberMedians_ENVI.sli). Note that a subset of the 55 wavelengths sampled in the source CRISM data were removed to avoid error-prone bands, leaving these spectral libraries with 51 bands. The spectra are normalized by the value at 1.330 µm.</li> </ul> </li> </ul>

opencc-by-4.0May 2021View details →

Mapping data files to semantic data models using the CaosDB crawler

Data from data acquisition can lead to a high variety of data files on file systems. The figure illustrates that these files can be mapped to semantic data models in the research data management system CaosDB using a customizable crawler.

opencc-by-4.0Jun 2020View details →

Excel template process mapping and FMEA

A FMEA analysis tool with prebuild radiation oncology process steps and sub steps that can be modified based on clinical workflow. Also contains an instruction for use and a sample completed document.

EUKulele databases for Phaeocystis mapping (Krinos et al. 2023)

Supplementary dataset for Krinos et al. 2023, <a href="https://www.biorxiv.org/content/10.1101/2023.07.30.551153v1">Missing microbial eukaryotes and misleading meta-omic conclusions</a>, bioRxiv. Each zipped folder is a different database containing some subset of the Phaeocystis references used to map against the Tara Oceans metagenomes. These folders contain a combined database and taxonomy table derived from other sequencing dataset resources which can be used directly with the <a href="https://github.com/akrinos/2022-euk-diversity">EUKulele software</a>. <ul> <li>`marmmetsp_all_phaeo.tar.gz` - full database including <a href="https://mmp2.sfb.uit.no/databases/">MarRef</a>, MMETSP (Keeling et al. 2014),  and all Phaeocystis references as listed in Krinos et al. (2023)</li> <li>`marmmetsp_all_colonies.tar.gz` - database including <a href="https://mmp2.sfb.uit.no/databases/">MarRef</a>, MMETSP (Keeling et al. 2014),  and Phaeocystis references known to form colonies (Phaeocystis antarctica, Phaeocystis pouchetii, Phaeocystis globosa)</li> <li>`marmmetsp_all_free.tar.gz` - database including <a href="https://mmp2.sfb.uit.no/databases/">MarRef</a>, MMETSP (Keeling et al. 2014),  and Phaeocystis references known not to form colonies (Phaeocystis rex, Phaeocystis jahnii, Phaeocystis cordata)</li> </ul>

opencc-by-4.0Mar 2023View details →

Repeated mapped tree inventory data in a tree plantation for pedunculate oak (Quercus robur) genetic conservation

We performed replicated, repeated mapped tree inventory measures (x,y, height, diameter, vitality, etc.) to allow analysis of the spatial and temporal structure in 1 ha planted pedunculate oak (Quercus robur) stand established in 2003 to resemble the oak-hornnbeam forests for genetic conservation purposes in Po Valley (Foresta Carpaneta). Two inventories were carried out in 2009 and 2019. The use of replicated, repeated, and mapped tree measures allows the examination of true changes in spatial pattern processes through time in this forest type.

dryad36/100

Low-coverage whole genome sequencing for highly accurate population assignment: Mapping migratory connectivity in the American Redstart (Setophaga ruticilla)

Understanding the geographic linkages among populations across the annual cycle is an essential component for understanding the ecology and evolution of migratory species and for facilitating their effective conservation. While genetic markers have been widely applied to describe migratory connections, the rapid development of new sequencing methods, such as low-coverage whole genome sequencing (lcWGS), provides new opportunities for improved estimates of migratory connectivity. Here, we use lcWGS to identify fine-scale population structure in a widespread songbird, the American Redstart (Setophaga ruticilla), and accurately assign individuals to genetically distinct breeding populations. Assignment of individuals from the nonbreeding range reveals population-specific patterns of varying migratory connectivity. By combining migratory connectivity results with demographic analysis of population abundance and trends, we consider full annual cycle conservation strategies for preserving numbers of individuals and genetic diversity. Notably, we highlight the importance of the Northern Temperate-Greater Antilles migratory population as containing the largest proportion of individuals in the species. Finally, we highlight valuable considerations for other population assignment studies aimed at using lcWGS. Our results have broad implications for improving our understanding of the ecology and evolution of migratory species through conservation genomics approaches.

opencc-zeroAug 2023View details →

High-Resolution Vegetation Height Maps for Switzerland in 2017-2020

This dataset comprises 10m-resolution vegetation height maps for Switzerland spanning from 2017 to 2020. It encompasses both mean and maximum vegetation height data, generated through the integration of Sentinel-2 and airborne laser scanning information.

opencc-by-4.0Jul 2011View details →

Building bridges: mapping diverse classifications for a seamless user navigation experience

This paper describes a BBC project to unify Archive and Production workspaces, during which numerous issues with managing different types of metadata and Knowledge Organi- sation Systems (KOSs) were encountered. Integrating diverse content silos requires bringing together not simply the assets, but also the metadata used to manage those assets. The paper summarises the theoretical background to the project, the BBC’s ‘information ecosystem’, and the user research and requirements-gathering exercises undertaken. Much work on developing metadata crosswalks has been at the heading or label level, and not based on semantic analysis of the content of the labelling or description. However, such semantic analysis needs to be undertaken when mapping diverse taxonomies, thesauri, and keyword lists and, in practice, often needs to balance preservation of local or specialised terminology with accessibility for general users. Just as metadata about content permits the organization of that content, so metadata about metadata (parametadata, or meta-metadata) permits the organization of metadata, enabling end users to make informed browse and navigation choices. Increasingly, in order to integrate content, different KOSs, such as taxonomies and ontologies, need to be related. The paper concludes by summarising the ways in which problems that arose during the integration project were resolved, and how policies for managing parametadata, subjective metadata, and semantic-level mapping were developed.

Mapped data: Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency

This record contains mapped sequencing data for the paper "Transcription factor stoichiometry, motif affinity and syntax regulate single cell chromatin dynamics during fibroblast reprogramming to pluripotency" by Nair, Ameen et al. It contains single-cell RNA-seq (scRNA) and single-cell ATAC-seq (scATAC) data from a time course of human dermal fibroblasts induced with Yamanaka factors OSKM using a Sendai virus based delivery system. The scRNA and scATAC data is performed at days 0, 2, 4, 6, 8, 10, 12, 14 and the final iPSCs. The experiment was re-performed and single-nucleus multiome (ATAC+RNA) was collected on days 1 and 2.  The data is as follows: scATAC: We used Chromap (commit <a href="https://github.com/haowenz/chromap/tree/6e97125b9">https://github.com/haowenz/chromap/tree/6e97125b9</a>, <a href="https://doi.org/10.1038/s41467-021-26865-w">https://doi.org/10.1038/s41467-021-26865-w</a>) to perform barcode correction, alignment and filtering for each of our samples. The corresponding fragment files (tab separated file containing mapped fragments with columns: chr, start, end, barcode, number of reads) and their tabix indices are available for each sample. scRNA: We used cellranger v6.0.2 for read mapping and quantification to obtain the counts matrix. We used the GRCh38 2020-A reference. For each sample, the raw and filtered counts matrices are provided. E.g. `D0/raw_feature_bc_matrix.h5` contains an HDF5 object containing gene counts for each barcode and associated metadata for the Day 0 sample. Similarly, the files in `D0/raw_feature_bc_matrix/` contain the same gene x barcode matrix, with the counts matrix in Matrix Market format (`matrix.mtx.gz`), and gene (`features.tsv.gz`) and barcode names (`barcodes.tsv.gz`).  multiome: The ATAC and RNA components are separately processed using the same tools as mentioned above for scATAC and scRNA. Outputs are in the `snATAC` and `snRNA` subdirectories respectively. In addition, the `ATAC.RNA.bc.map.tsv` file contains a map to link snATAC barcodes to snRNA barcodes.