Find research datasets worth reusing

Environmental map data (OS1-128)

Environmental maps associated with https://zenodo.org/deposit/7233945 . Maps are provide in two formats (PLY and PCD).

Cluster Interconnectedness maps of COVID-19 spread in India.

This project contains clustered maps from sample data on COVID-19 acquired from the Indian Council of Medical Research. Citation is required if anyone wishes to use the files for additional research.

opencc-zeroMay 2023View details →

dryad36/100

Scores on independence, quality of life, and satisfaction on visual mapping software intervention in children with Down syndrome

Enhancing independence and quality of life are key modifiable outcomes that are short- and long-term goals for children with Down syndrome and for their parents. Here we report the outcome of a 4-week feasibility study in a cohort of 26 children with Down Syndrome, 7–17 years old, who used an assistive technology approach that incorporated smart device software and step-by-step pictures (the MapHabit System). Parents reported improvements in children's activities of daily living, quality of life, and independence. They recommended this technology to other families. This report and its findings underscore the feasibility of using assistive technology in children with Down syndrome within home and family settings. The current findings that assistive technology can be used successfully and effectively in family and home settings set the stage for more informative systematic studies using assistive technology for this population.

Working code and data for a wall-to-wall mapping of carbon loss within the Chornobyl Exclusion Zone after the 2020 catastrophic wildfire

Links to data in this code are from <a href="https://github.com/Janzeero-PhD/C_loss_Chornobyl_2022">GitHub repository</a>. But can be replaced by the files supplied here. Only code to generate models and to create figures for their validation is provided. Also code to create GMFR function (from Riemann et al. 2010 and adapted in Henderson et al. 2019) is provided. Code to: calculate C stock for training and validation data set per biomass compartment; prepare raster data (mostly in Google Earth Engine); make raster predictions; calculate C loss according to local combustion factors and NBR severity levels is available upon request. MetaData.xlsx contains a description of all files within this package, and variable explanation for provided data sets.

opencc-by-4.0Jul 2022View details →

iWalk on FROST - How to create a walkable map

iWalk on FROST This project created a virtual walkable maps by using geo-data and a gaming engine. The video is a step-by-step guide on how to create a similar game. More information about the project can be found here: https://www.iearth.no/resource/iwalk-how-to-create-a-walkable-map An example game can be found here: https://doi.org/10.5281/zenodo.7997591 A tutorial for preparing in-game videos for games can be found here: https://doi.org/10.5281/zenodo.7997495

Spatial Mapping and Host Linking of Mobile Genetic Elements in Complex Microbiomes - Mapping MGEs in oral plaque biofilms at high specificity

We stained for the GFP gene in samples that contained mixtures of plaque and GFP-transformed E. coli. We mapped mefE, an AMR gene located on a plasmid and encoding an antibiotic efflux pump, in the plaque metagenomic data of volunteer A but not volunteer B. To test the efficacy of gel embedding and clearing, we used orthogonal FISH probes, designed to not target any sequence in the plaque. We identified a T7-like prophage via metagenomic analysis and developed probes targeting its capsB gene, which encodes the minor capsid protein. We identified a highly prevalent prophage of the class Caudoviricetes with a large terminase gene, termL, and were able to design a large set of FISH probes to stain in three different colors simultaneously. We identified three non-plasmid AMR genes within metagenome assembled genomes: patA, patB, and adeF.

Spatial Mapping and Host Linking of Mobile Genetic Elements in Complex Microbiomes - Optimization of single molecule MGE FISH

We used Escherichia coli transformed with pJKR-H-tetR plasmids encoding an inducible GFP gene as a model system to assess and optimize MGE-FISH on a confocal microscope. We designed FISH probes for the non-coding strand of the GFP gene, used non-transformed E. coli as a negative control, and tested six different FISH protocols.

Spatial Mapping and Host Linking of Mobile Genetic Elements in Complex Microbiomes - Combined taxonomic mapping and MGE mapping

We used rRNA FISH to stain five common oral genera, Veillonella, Streptococcus, Corynebacterium, Lautropia, and Neisseria, each with a different fluorophore, and we used MGE-FISH to stain the termL gene of an active prophage with a sixth fluorophore.  We chose a target panel of 18 genera that are highly abundant and prevalent in human plaque and designed a HiPR-FISH probe panel using a 5-fluorophore combinatorial barcoding scheme. Using MGE-FISH, we stained a plasmid carrying mefE, subunit of a major-facilitator-superfamily antibiotic efflux pump.

Maps Under Construction

Occasionally it is possible to find a partially finished or marked up map that illustrates the processes by which map-makers produce a new map.  Here are a few examples.

opencc-by-4.0Apr 2023View details →

TSlam-mapping-data

This repository contains all the data for the mapping phase of TSlam. This data is necessary for each specimen inference TSlam. Each szip folder contains: - the optimized .map file - the .ply model reconstructed from the map - the -mp4 videos of the raw feed and with GUI - the .yml file corresponding to the map with the list and pose of tags detected

Mapping of aridity and its connections with climate classes and climate desertification in future scenarios – Brazilian semi-arid region

This database comes from the article entitled ''Mapping of aridity and its connections with climate classes and climate desertification in future scenarios –Brazilian semi-arid region'' (https://seer.ufu.br/index.php/sociedadenatureza /article/view/67666/36193). We provide data on aridity and desertification index for the current scenario and future projections considering changes in climate.

Image bank to use with the Resource Flow Mapping tool when implementing the Clean and Green framework

This image bank consists of a set of 116 images that can be used together with the Resource Flow mapping tool and other sensitization tools when implementing the Clean and Green framework as part of rural sanitation programs. The image bank consists of a wide range of images including everything from images depicting the use of various types of ecological sanitation toilets to the use of excreta-derived products in agriculture. All the images have a white background, and hence can be used in a variety of media and contexts. Each image has a descriptive file name. The images can be downloaded individually, or collectively by downloading the "Images.zip" file. The image bank was developed as part of the Clean and Green framework project at the Stockholm Environment Institute, in collaboration with WaterAid Burkina Faso. Learn more: https://www.sei.org/projects-and-tools/projects/clean-and-green/

Validating NISAR's cropland mapping approach and the USDA/NASS Cropland Data Layer against ground truth data in a fragmented urban agricultural region

Field data used in manuscript: 1 shapefile containing the ROI outline for which Sentinel-1 data was cropped 1 shapefile containing the 93 fields investigated with their names, types and sizes as attributes  8 annual csv data for active fields, consisting of 3 harvest dates and 5 planting dates. This list is after translating data from Farmlogic Report (not conducive to analysis in the format) and filtering for fields greater 1 acre. The study lateron further screened to use only fields greater than 2 acreas (0.81 hectares).

opencc-by-4.0Mar 2023View details →

A digital GIS update to the classic Ulbrich 1930 map of muskrat (Undatra zibethica) population range expansion in Central Europe

This dataset is a digital version of a seminal dispersal data set of muskrat spread in Central Europe. We obtained and scanned an original copy of Ulbrich (1930), creating a digital image file, which we then geo-referenced in Arc Gis 10.2. The data herein contains metadata explaining the conversion process, necessary images to geo-reference the map, reference points used to "fix" the dispersal map to a map of present-day Europe, intermediate files (points and polygons produced when tracing the population ranges), and the final spatial dataset.   Reference to the original data:  Ulbrich, Johannes.  1930.  Die Bisamratte; lebenweise, gang ihrer ausbreitung in Europa, wirtschaftliche bedeutung und bekampfung.  Heinrich, Dresden.

Data, research code, and metadata related to the Soils of the Upper Part of Itatiaia National Park (INP) for Pedology, Hyperspectral Soil Mapping, and Spectral studies

Data, research code (in R language), and metadata related to the Soils of the Upper Part of Itatiaia National Park (INP) for Hyperspectral Soil Mapping Up to this point, this data, and code were used in the Doctoral thesis of Elias Mendes Costa and Yuri Andrei Gelsleichter, and the following publications. ------------------------------------------------------------------------------------------------------------------------------------------------------------ Dados, código de pesquisa (em linguagem R) e metadados relacionados aos Solos e ao Mapeamento da Parte Superior do Parque Nacional de Itatiaia (PNI) para o Mapeamento Hiperespectral de Solos Até este momento, esses dados, e códigos foram utilizados nas teses de doutorado de Elias Mendes Costa e de Yuri Andrei Gelsleichter e nas seguintes publicações.   Gelsleichter, Y. A., Costa, E. M., dos Anjos, L. H. C., & Marcondes, R. A. T. (2023). Enhancing soil mapping with hyperspectral subsurface images generated from soil lab vis-SWIR spectra tested in southern Brazil. Geoderma Regional, e00641. doi: https://doi.org/10.1016/j.geodrs.2023.e00641 link: https://www.sciencedirect.com/science/article/pii/S2352009423000378 Costa, E. M., Pinheiro, H. S. K., Anjos, L. H. C. dos, Marcondes, R. A. T., & Gelsleichter, Y. A. (2020). Mapping soil properties in a poorly-accessible area. Revista Brasileira de Ciência Do Solo, 44. doi: https://doi.org/10.36783/18069657rbcs20190107 link: https://www.rbcsjournal.org/article/mapping-soil-properties-in-a-poorly-accessible-area/ Costa, E. M., dos Anjos, L. H. C., Pinheiro, H. S. K., Gelsleichter, Y. A., & Marcondes, R. A. T. (2020). Spatial Bayesian belief networks: A participatory approach for mapping environmental vulnerability at the Itatiaia National Park, Brazil. Environmental Earth Sciences, 79, 1–13. doi: https://doi.org/10.1007/s12665-020-09099-9 link: https://link.springer.com/article/10.1007/s12665-020-09099-9#citeas The code is arranged to deliver the outputs in their respective folders.

Inundation Maps - Polyphytos open surface water reservoir - WQeMS raster products

Within this dataset, inundation maps of the Polyphytos open surface water reservoir in Greece for the years 2021 and 2022 are available in GeoTIFF raster format. They were generated by the Land Water Transition Zone Change Detection service of the WQeMS project as intermediate products. Each raster file in the dataset is named according to the date in which the processing was performed. Copernicus Sentinel-2 data was utilized for the generation of the inundation maps.

Multi-Nucleic Acid Interaction Mapping in Single Cell (MUSIC) for simultanouse chromatin, RNA-chromatin and transcriptome mapping at single cell resolution

<a href="https://doi.org/10.1101/2023.06.28.546457">MUSIC manuscript:</a> Joint profiling of multiplex chromatin interactions, gene expression, and RNA-chromatin associations in single cells of the human brain. MUSIC-docker is the customized pipeline to process the raw fastq files to bam files: http://sysbiocomp.ucsd.edu/public/wenxingzhao/MUSIC_docker/intro.html. Each bam file records the final output of our single cell mixed species analysis. Read name recods the cell barcode, complex barcode and I7 index. For each DNA/RNA read, read header contains <code>raw read name</code>| <code>BC3</code>_<code>BC2</code>_<code>BC1</code> - <code>10x barcode</code> # <code>UMI</code>. Details of the read name can be found here: http://sysbiocomp.ucsd.edu/public/wenxingzhao/MUSIC_docker/step.html#demultiplexing. merge_DNA [RNA]_human [mouse].sort.bam: bam file of DNA [RNA] reads from the mix species library (H1+E14) that can uniquely mapped to the human [mouse] genome. PCR duplicates have been removed.

NEATmap: a high-efficiency deep learning approach for whole mouse brain neuronal activity trace mapping

Here are some demo datasets for validating the NEATmap pipeline for high-efficiency whole brain c-Fos+ cell automated segmentation and quantitative analysis, including: <ol> <li>BrainImage_group.zip.001-007: Validation of NEATmap for automated segmentation and quantitative analysis of mouse whole-brain c-Fos activity images (in Forced Swimming Test).</li> <li>Segmentation_result.zip: Figure 1a, Supplementary Videos 1 and 2 show dual-channel brain slices and segmentation results. They can be merged using Imaris to validate the segmentation results of NEATmap.</li> <li>RawImage_example.zip: High-resolution 3D images of mouse brain slices showing c-Fos+ cells in Figure 1e.</li> </ol> Due to the total size of the mouse whole-brain image datasets (both raw and processed) included in all the tests exceeding 10 Terabytes, uploading it to a public data repository is impractical. In this work, we provide a dataset of dual-channel (c-Fos+ channel and autofluorescence channel in forced swimming test experimental group) whole-brain images of mouse for the validation of NEATmap automated segmentation method.

Land cover maps: End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes

Land cover maps obtained with mTAN-GP, mTAN-MLP, mTAN-LTAE and raw-LTAE models for the year 2018 with Sentinel-2 acquisitions. For further details see the pre-print article "End-to-end Learning for Land Cover Classification using Irregular and Unaligned SITS by Combining Attention-Based Interpolation with Sparse Variational Gaussian Processes ". This article is available : <a href="https://hal.science/hal-04112115">here</a>.

Supplementary material of the study "Help! I need somebody. A Mapping Study about Expert Identification in Software Development"

Supplementary Material Context: Software development is a knowledge-intensive activity, and its success in an organization relies deeply on knowledge sharing. Knowledge management challenges are often increased in agile environments, which involve a lot of tacit knowledge, commonly acquired through experiences and hard to be made explicit. Therefore, knowledge sharing among practitioners is crucial. However, identifying suitable experts to share specific knowledge is not trivial. It involves not only discovering the individuals with the desired knowledge but also considering other factors that may improve the expert responsiveness, such as social connections and availability. Objective: Considering the important role experts play in knowledge sharing, we decided to investigate approaches that help identify experts that can share knowledge in software development. Our goal is to provide a panorama of the existing approaches and shine a light on research opportunities. Method: We carried out a systematic literature mapping and analyzed 17 publications. Results: The results show that most approaches have relied on code repositories as a source of evidence for identifying experts and, consequently, focus on supporting developers and aiding in the codification activity. Additionally, expert identification has been mostly automated, and factors beyond possessing the desired knowledge have often been disregarded. Conclusion: Although there are several expert identification approaches, there has been a lack of concern with factors that influence reaching the most suitable expert for a specific situation (e.g., considering the characteristics of the person seeking knowledge). Moreover, there is a need for deeper reflection on how to better explore different artifacts as sources of expert evidence and how to combine them to improve expert identification. This package contains supplementary material of the study performed to investigate approaches that help identify experts that can share knowledge in software development. It contains: <ul> <li>A spreadsheet containing raw data (research protocol, considered and selected publications, and research questions answers).</li> </ul>