Find research datasets worth reusing

MATLAB Code for "Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients"

This MATLAB code is part of the study titled "Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients", which has been accepted for publication in the Journal of Imaging (MDPI). The code supports image processing, feature extraction, and deep learning model training (including LSTM and RexNet) to classify pediatric patients as anemic or non-anemic based on palm, conjunctival, and fingernail images. Full study details are available in this paper: Berghout T. Joint Image Processing with Learning-Driven Data Representation and Model Behavior for Non-Intrusive Anemia Diagnosis in Pediatric Patients. Journal of Imaging. 2024; 10(10):245. <a href="https://doi.org/10.3390/jimaging10100245">https://doi.org/10.3390/jimaging10100245 </a> The datsets use in this work are: Asare, J. W., Appiahene, P. & Donkoh, E. (2022). Anemia Detection using Palpable Palm Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/ccr8cm22vz.1 Asare, J. W., Appiahene, P. & Donkoh, E. (2023). CP-AnemiC (A Conjunctival Pallor) Dataset from Ghana. Mendeley Data. https://doi.org/10.17632/m53vz6b7fx.1 Asare, J. W., Appiahene, P. & Donkoh, E. (2020). Detection of Anemia using Colour of the Fingernails Image Datasets from Ghana. Mendeley Data. https://doi.org/10.17632/2xx4j3kjg2.1

Data used in the paper Sensitivity of wintertime Arctic black carbon to removal processes and regional Alaskan sources by Ioannidis et al. 2024

Open the record for dataset details and reuse information.

opencc-zeroSep 2024View details →

NB-FOXR2 processed data

The sample names in this dataset match supplementary tables 1 (human) and 9 (mouse). The tarball contains 2 directories: human and mouse. Each of these contains directories specifying data types. See the README.md for a description of all the files.

opencc-by-4.0Feb 2024View details →

DUGseis processing example with needed data files

The DUGseis software is designed for seismic data processing and developed for monitoring seismicity during hydraulic stimulations in the Bedretto Undergound Laboratory. Here, we provide a python run-file and the associated configuration file (.yaml). In the configuration file, paths to station xml files (.xml) and waveform files (.h5) are defined. As an example and to test the software, a small subset of 7 minute waveform data of two acquisition systems (system 03 ending on _03 and system 04 ending on _04) and the needed station xml files are available.

Data for "Magnetic Characterization of Sediment Source-to-Sink Processes in the Bengal Fan since 45 ka"

The file is the dataset for the manuscript entitled 'Magnetic Characterization of Sediment Source-to-Sink Processes in the Bengal Fan since 45 ka' by Huang et al., including age, original and smoothed low frequency magnetic susceptibility, anhysteretic remanence magnetization (ARM) data, and bulk mean grain size data of five gravity cores in the manuscript. Hysteresis loop data is also included.

openmit-licenseOct 2024View details →

Inferring GCE Morphology with Gaussian Processes: Figure Data

Figure data for Inferring GCE Morphology with Gaussian Processes. See GitHub repo for details on creating figures.

Data and processing code for Depth-averaged Subtidal and Tidal Circulation off of a Rocky Shore

Data and code in order to recreate all figures in "Depth-averaged Subtidal and Tidal Circulation off of a Rocky Shore".  JGR_data: all data to remake figures  JGR_functions: all functions to remake figures  Copy_of_make_JGR_figs.m: script to remake figures

Pre-processed WRF output, interpolated from gridded data to 74 station locations, in the Midwest U.S.

The dataset includes pre-processed WRF simulation data for the historical period of 1980 to 2022 and future projections from 2058 to 2100 under two climate change scenarios: RCP 4.5 and RCP 8.5. The data has been interpolated from gridded data to 74 station locations in the Midwest United States. Each file contains data for a single station and a single month with a 3-hourly time step. This dataset can be used to generate intensity-duration-frequency (IDF) curves using the procedure published at https://doi.org/10.5281/zenodo.13685451.

Computational modeling and analytical validation of singular geometric effects in fault data using a combinatorial approach - Input and processed data

The archive contains the input and processed data for the companion manuscript. The input data contains XYZ coordinates of points documenting the investigated interfaces. The output datasets contain directional data from applying the combinatorial algorithm to point data sets. We have also included .VTU and .PVSM files for visualization of the geological settings in ParaView.

Field measurement data of hydrodynamic and morphological processes in the mangrove forest of Lac Bay, Bonaire, Caribbean Netherlands

31-10-2024, Rik Gijsman _________________________________________________________ Dataset of field measurement of hydrodynamic and morphological processes in the mangrove forest of Lac Bay, Bonaire, Caribbean Netherlands. _________________________________________________________ For more information please see scientific publication: Gijsman, R., Engel, S., van der Wal, D., van Zee, R., Johnson, J., van der Geest, M., Wijnberg, K.M. and Horstman, E.M. (2024). The Importance of Tidal Creeks for Mangrove Survival on Small Oceanic Islands. Unpublished Manuscript. _________________________________________________________ Dataset contains:  <ol> <li>Timeseries data from field instruments:  <ol> <li>Atmospheric pressure</li> <li>Water depth</li> <li>Water level</li> <li>Flow velocity</li> <li>Waves</li> <li>Turbidity</li> <li>Rainfall</li> <li>Temperature</li> <li>Wind</li> <li>Tidal creek flows</li> <li>Tidal creek transport</li> <li>Lagoon suspension</li> </ol> </li> <li>Mapped data of field surveys: <ol> <li>Bathymetry and creek profiles</li> <li>Sediment characteristics</li> </ol> </li> <li>Python script for importing and plotting data</li> <li>Overview map with shapefiles of instrument location coordinates</li> </ol> ________________________________________ Additional notes for your information: <ul> <li>Timeseries data stored in .txt files</li> <li>Visualization of data in .txt files provided with accompanying .png files</li> <li>Timeseries data of Hobo sensors, Rain gauge and KNMI station interpolated between 19-01-2022 00:00:00 and 18-05-2022 23:55:00 (120 days) on 5 minute intervals </li> <li>Timeseries data of other sensors interpolated between 19-01-2022 00:00:00 and 08-03-2022 23:55:00 (49 days) on 5 minute intervals</li> <li>Column headers show instrument station abbreviation</li> <li>Datetime in local timezone of Bonaire (GMT-4)</li> <li>When a property (e.g., water depth) was measured by different instruments, the instrument used is mentioned in the file name, e.g. "water_depth_hobo.txt"</li> <li>Measurements from KNMI (The Royal Netherlands Meteorological Institute) station 990 were included in the dataset and indicated with '_knmi' in the file name. Data obtained from 'https://www.knmi.nl/nederland-nu/klimatologie/uurgegevens_Caribisch_gebied' </li> </ul>

Analysis of Padlet in the educational process on the rock cycle through Data Science

Analysis of Padlet in the educational process on the rock cycle through Data Science

Processed data of "Observations of Ring Current Proton Fast Local Loss Associated with Deepening Local Minimum in Phase Space Density in Earth's Inner Magnetosphere"

The dataset includs the processed observation data of "Observations of Ring Current Proton Fast Local Loss Associated with Deepening Local Minimum in Phase Space Density in Earth’s Inner Magnetosphere". *.sav files are the phase space densities of protons and the corresponding adiabatic invariants (μ, K, and L*) which are calculated under the T89D magnetic field model (using the observation data measured by the RBSPICE on the Van Allen Probes from 24th to 26th Jun 2017), which are used to plot Figure 1. The data in Processed data for Figures 2-5.zip are the processed data for Figures 2-5.

Data, Pre- and Post- processing scripts for shallow-water photogrammetry applications

<h1>Description</h1> This repository contains the data and scripts to reproduce the results of Casella et al. (Remote Sensing, 2024). The scripts are in python, two of them are wrapped in Jupyter Notebooks with explanatory notes. Please read the paper for further information on the platform used to collect the data shared in this repository. <h2>Folder structure</h2> The main folder contains two subfolders: <ol> <li>Data: this folder includes all the original data, and the results of the preprocessing and post-processing notebooks. Note that the original photos, included in the "Camera/all_photos" folder must be unzipped before running the preprocessing Jupyter  Notebooks.</li> <li>Precision_Analysis: this folder includes the digital bathymetric models that were co-registered as described in the paper. The co-registration was done offline with Quantum GIS. In this folder are also stored the results of the "Compare_DBMs.py" script, that makes the precision analysis (differences between co-registered DBMs).</li> </ol> <h2>Installation</h2> Refer to the README.md file for a quick installation guide using Anaconda.  <h2>Credits</h2> The code included in this work has been improved with the assistance of ChatGPT, which provided guidance on optimization, debugging, and documentation to enhance clarity and functionality. All the code has been reviewed and supervised by humans to ensure consistency and correctness.

opencc-by-4.0Sep 2024View details →

Experimental data and processing scripts of chirp ENDOR spectra

Experimental data and MATLAB processing scripts for the manuscript: "Increased sensitivity in Electron Nuclear Double Resonance spectroscopy with chirped radiofrequency pulses"

opencc-zeroNov 2024View details →

Processed DepMap data (.h5mu)

Disclaimer The DepMap data were generated and shared by the Broad Institute of Harvard and MIT and the Sanger Institute. We reprocessed the data and packaged it in a single h5mu file for easier access and to reproduce analyses with Semi-supervised Omics Factor Analysis (SOFA).  Please see https://www.biorxiv.org/content/10.1101/2024.10.10.617527v3 for more details on how the data was processed and analysed. Data Usage Policy The Broad Institute publishes its data under the <a href="https://depmap.org/portal/ccle/terms_and_conditions">Terms and Conditions linked here.</a> The Sanger Institute publishes its data under the <a href="https://depmap.sanger.ac.uk/documentation/data-usage-policy/">Terms and Conditions linked here.</a> The DepMap data  are provided under <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 license</a>. Contact <a href="mailto:depmap@broadinstitute.org">depmap@broadinstitute.org</a> or <a href="mailto:depmap@sanger.ac.uk">depmap@sanger.ac.uk</a> for more information Please cite the following when using these data <ul> <li>Drug response: <a href="https://www.cell.com/cancer-cell/fulltext/S1535-6108(22)00274-4?dgcid=raven_jbs_aip_email">Gonçalves, E. et al. Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 40, 835–849.e8 (2022).</a> (https://figshare.com/articles/dataset/Pan-cancer_proteomic_map_of_949_human_cell_lines/19345397)</li> <li>Proteomics: <a href="https://www.cell.com/cancer-cell/fulltext/S1535-6108(22)00274-4?dgcid=raven_jbs_aip_email">Gonçalves, E. et al. Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 40, 835–849.e8 (2022).</a> (https://figshare.com/articles/dataset/Pan-cancer_proteomic_map_of_949_human_cell_lines/19345397)</li> </ul> <ul> <li>RNA-Seq: <a href="https://aacrjournals.org/cancerres/article/78/3/769/633178/Transcription-Factor-Activities-Enhance-Markers-of">Garcia-Alonso, L. et al. Transcription factor activities enhance markers of drug sensitivity in cancer. Cancer Res. 78, 769–780 (2018).</a> (https://cellmodelpassports.sanger.ac.uk/downloads)</li> <li>Methylation: <a href="https://www.cell.com/fulltext/S0092-8674(16)30746-2">Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016).</a> (https://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources/Home.html)</li> <li>Mutation: <a href="https://www.cell.com/fulltext/S0092-8674(16)30746-2">Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016).</a> (https://cellmodelpassports.sanger.ac.uk/downloads)</li> <li>CRISPR-Cas9: <a href="https://www.nature.com/articles/s41467-021-21898-7">Pacini, C. et al. Integrated cross-study datasets of genetic dependencies in cancer. Nat. Commun. 12, 1661 (2021)</a>. (https://score.depmap.sanger.ac.uk/downloads)</li> <li>Metadata for all cell lines was obtained from: <ul> <li><a href="https://www.cell.com/cancer-cell/fulltext/S1535-6108(22)00274-4?dgcid=raven_jbs_aip_email">Gonçalves, E. et al. Pan-cancer proteomic map of 949 human cell lines. Cancer Cell 40, 835–849.e8 (2022).</a> </li> <li><a href="https://www.nature.com/articles/s41467-021-21898-7">Pacini, C. et al. Integrated cross-study datasets of genetic dependencies in cancer. Nat. Commun. 12, 1661 (2021)</a>.</li> </ul> </li> </ul>

Data and code of "Unified percolation scenario for the α and β processes in simple glass formers

Molecular dynamics simulation data and post-processing code for "Unified percolation scenario for the α and β processes in simple glass formers"

opencc-zeroDec 2015View details →

Data from: Using spatial capture–recapture to elucidate population processes and space-use in herpetological studies

The cryptic behavior and ecology of herpetofauna make estimating the impacts of environmental change on demography difficult; yet, the ability to measure demographic relationships is essential for elucidating mechanisms leading to the population declines reported for herpetofauna worldwide. Recently developed spatial capture–recapture (SCR) methods are well suited to standard herpetofauna monitoring approaches. Individually identifying animals and their locations allows accurate estimates of population densities and survival. Spatial capture–recapture methods also allow estimation of parameters describing space-use and movement, which generally are expensive or difficult to obtain using other methods. In this paper, we discuss the basic components of SCR models, the available software for conducting analyses, and the experimental designs based on common herpetological survey methods. We then apply SCR models to Red-backed Salamander (Plethodon cinereus), to determine differences in density, survival, dispersal, and space-use between adult male and female salamanders. By highlighting the capabilities of SCR, and its advantages compared to traditional methods, we hope to give herpetologists the resource they need to apply SCR in their own systems.

opencc-zeroDec 2017View details →

Data from: Central place foragers and moving stimuli: a hidden-state model to discriminate the processes affecting movement

1. Human activities can influence the movement of organisms, either repelling or attracting individuals depending on whether they interfere with natural behavioural patterns or enhance access to food. To discern the processes affecting such interactions, an appropriate analytical approach must reflect the motivations driving behavioural decisions at multiple scales. 2. In this study, we developed a modelling framework for the analysis of foraging trips by central place foragers. By recognising the distinction between movement phases at a larger scale and movement steps at a finer scale, our model can identify periods when animals are actively following moving attractors in their landscape. 3. We applied the framework to GPS tracking data of northern fulmars Fulmarus glacialis, paired with contemporaneous fishing boat locations, to quantify the putative scavenging activity of these seabirds on discarded fish and offal. We estimated the rate and scale of interaction between individual birds and fishing boats and the interplay with other aspects of a foraging trip. 4. The model classified periods when birds were heading out to sea, returning towards the colony or following the closest boat. The probability of switching towards a boat declined with distance and varied depending on the phase of the trip. The maximum distance at which a bird switched towards the closest boat was estimated around 35 km, suggesting the use of olfactory information to locate food. Individuals spent a quarter of a foraging trip, on average, following fishing boats, with marked heterogeneity among trips and individuals. 5. Our approach can be used to characterise interactions between central place foragers and different anthropogenic or natural stimuli. The model identifies the processes influencing central place foraging at multiple scales, which can improve our understanding of the mechanisms underlying movement behaviour and characterise individual variation in interactions with a range of human activities that may attract or repel these species. Therefore, it can be adapted to explore the movement of other species that are subject to multiple dynamic drivers.

opencc-zeroDec 2014View details →

Data from: Echoes of a distant time: effects of historical processes on contemporary genetic patterns in Galaxias platei in Patagonia

Interpreting the genetic structure of a metapopulation as the outcome of gene flow over a variety of timescales is essential for the proper understanding of how changes in landscape affect biological connectivity. Here we contrast historical and contemporary connectivity in two metapopulations of the freshwater fish Galaxias platei in northern and southernmost Patagonia where paleolakes existed during the Holocene and Pleistocene, respectively. Contemporary gene flow was mostly high and asymmetrical in the northern system while extremely reduced in the southernmost system. Historical migration patterns were high and symmetric in the northern system and high and largely asymmetric in the southern system. Both systems showed a moderate structure with a clear pattern of isolation by distance (IBD). Effective population sizes were smaller in populations with low contemporary gene flow. An approximate Bayesian computation (ABC) approach suggests a late Holocene colonization of the lakes in the northern system and recent divergence of the populations from refugial populations from east and west of the Andes. For the southern system, the ABC approach reveals that some of the extant G. platei populations most likely derive from an ancestral population inhabiting a large Pleistocene paleolake while the rest derive from a higher-altitude lake. Our results suggest that neither historical nor contemporary processes individually fully explain the observed structure and geneflow patterns and both are necessary for a proper understanding of the factors that affect diversity and its distribution. Our study highlights the importance of a temporal perspective on connectivity to analyse the diversity of spatially complex metapopulations.