Find research datasets worth reusing

Open Source Software in Data Science

This upload includes an anonymized data set of a survey first launched in 2022. The survey has been revised since. The data set. however, contains answers of the first launch.

opencc-zeroMay 2024View details →

dryad36/100

Data from: Uncorrected soil water isotopes through cryogenic vacuum distillation may lead to a false estimation of plant water sources

Successful use of stable isotopes (δ2H and δ18O) in ecohydrological studies relies on the accurate extraction of unfractionated water from different types of soil samples. Cryogenic vacuum distillation (CVD) is a common laboratory-based technique used for soil water extraction; however, the reliability of this technique in reflecting soil water δ2H and δ18O is still of concern. This study examines the reliability of a newly developed automatic cryogenic vacuum distillation (ACVD) system through a set of pure water extraction. The impacts of extraction parameters (i.e., extraction time, temperature, and vacuum) and soil properties on the recovery of soil water δ2H and δ18O were further assessed for the ACVD and traditional extraction systems (TCVD) systems. Finally, the potential influences of the CVD technique on the prediction of plant water uptake were investigated through a sensitivity analysis. We demonstrated that the ACVD system was reliable for recovering the isotopic composition of pure water, with negligible biases of − 0.1 ± 0.3‰ for δ2H and 0.04 ± 0.09‰ for δ18O. Both ACVD and TCVD similarly extracted water from the rewetted soils when the extraction time of the ACVD system reached 240 min, but none of the CVD systems successfully recovered the isotopic signatures of doped water from soil materials. Mean δ2H offsets of extracted soil water were − 2.6 ± 1.3‰ and − 2.4 ± 1.7‰ for ACVD and TCVD, respectively; while mean δ18O offsets were − 0.16 ± 0.14‰ and − 0.39 ± 0.37‰. The isotopic offsets of CVD systems were positively correlated with soil clay content, and negatively correlated with soil water content. The use of corrected soil data (with CVD offsets) could improve the prediction of plant water uptake based on its high correlation with environmental factors. This study identifies the isotopic offsets of CVD systems (i.e., ACVD and TCVD) and provides possible solutions for better-predicting plant water sources. Even so, the wide use of CVD techniques probably induces noticeable uncertainties in predicting plant water uptake depths. The dataset of soil water extraction in this study will have implications for the technological development of CVD techniques.

April 7, 2024 (v1) Image Open Tuning apicobasal polarity and junctional recycling in the hemogenic endothelium orchestrates the morphodynamic complexity of emerging pre-hematopoietic stem cells —Source data 5 relative to Figure 7 - Figure Supplement 4

Source data file relative to Figure 7 – figure supplement 4 Panel A Raw image of agarose gel showing the 2 alternative mRNAs encoding for ArhGEF11 in control animals (left track, control) and after injection of the MO at the one cell stage (right track, +MO at 2 and 5ng). The source data includes the raw files (native format .scn and open source format .tiff) as well as a pdf file showing both the full scale image and the cropped image selected for the figure.

Data presented in Dupont et al. 2024 "Impact of dust source patchiness on the existence of a constant dust flux layer during aeolian erosion events"

Meteorological and dust data used in Dupont et al. 2024. Data are based on measurements recorded above two erosive surfaces from two very different desert regions, one at a high-latitude location in Iceland and the other at a low-latitude location in Jordan. The Iceland campaign was co-organized by two projects: FRAGMENT (FRontiers in dust minerAloGical coMposition and its Effects upoN climaTe) and HiLDA (Iceland as a model for high-latitude dust sources – a combined experimental and  modeling approach for characterization of dust emission and transport processes); and the Jordan campaign (J-WADI: Jordan Wind erosion And Dust Investigation) was co-organized by the FRAGMENT and Helmholtz Young Investigator Group ``A big unknown in the climate impact of atmospheric aerosol: Mineral soil dust'' projects.

Forest Fire Dataset for Peninsular Malaysia (2001-2023) Extracted from Multiple-Source Remote Sensing Data using Google Earth Engine

<ul> <li>Dataset: Forest Fire data</li> <li>Time Period: 2001 to 2023</li> <li>Location: Peninsular Malaysia</li> <li>Historical Fire Source: MCD64A1 and FIRMS Hotspots</li> <li>Fire Factors Extracted: Global Remote Sensing Data from GEE</li> </ul> The framework extraction process can be reffered from the following publication: <ul> <li>Framework to Create Inventory Dataset for Disaster Behavior Analysis Using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis</li> <li>Journal: Forests 2024, 15(6), 923;</li> <li><a href="https://doi.org/10.3390/f15060923">https://doi.org/10.3390/f15060923</a></li> <li>The variables name such as AET (actual evapotranspiration) can be found from the article.</li> </ul> Access the framework code from:  <ul> <li><a href="https://github.com/chewyeejian/GEE_FrameworkForestFireDataset">https://github.com/chewyeejian/GEE_FrameworkForestFireDataset</a></li> </ul> The time sequence in the variable indicate whether it's a monthly data / yearly accumulated data / seasonal data, example: <ul> <li>200101_aet (Year 2001, Month 01, value for aet (actual evapotranspiration)</li> <li>2001_aet_DJF (Average of December, January, February)</li> <li>2001_aet_MAM (Seasonal Average of March, April, May)</li> <li>2001_aet_JJA (Seasonal Average of June, July, August)</li> <li>2001_aet_SON (Seasonal Average of September, October, November)</li> <li>2001_aet_annual (Annual average of 2001)</li> </ul>

Source data - Engineering Modular and Tunable Single Molecule Sensors by Decoupling Sensing from Signal Output

Research data supporting the findings of "Engineering Modular and Tunable Single Molecule Sensors by Decoupling Sensing from Signal Output" by Lennart Grabenhorst, Martina Pfeiffer, Thea Schinkel, Mirjam Kümmerlin, Gereon A. Brüggenthies, Jasmin B. Maglic, Florian Selbach, Alexander T. Murr, Philip Tinnefeld and Viktorija Glembockyte. For questions concerning this data, please reach out to Philip Tinnefeld or Viktorija Glembockyte.

Memristive circuits based on multilayer hexagonal boron nitride for millimetre wave radiofrequency applications - Source data

Source data for the paper "Memristive circuits based on multilayer hexagonal boron nitride for radiofrequency and millimetre wave applications" Find online at: https://doi.org/10.1038/s41928-024-01192-2

Source data for publication "A multimodal atlas of hepatocellular carcinoma reveals convergent evolutionary paths and 'bad apple' effect on clinical trajectory" in Journal of Hepatology

Processed genomic and transcriptomic data for the publication <a href="https://doi.org/10.1016/j.jhep.2024.05.017">https://doi.org/10.1016/j.jhep.2024.05.017</a>. cnv_segmentation.tsv: CNV segmentation file from Sequenza. cnv_arm.tsv: Significant arm level CNV events called by GISTIC, from broad_values_by_arm.txt file. cnv_gene.tsv: Gene level CNV events called by GISTIC, from all_threshold_by_genes.txt file.  RNA_raw_counts.tsv: Raw RNA-seq read counts from featureCounts. snv_indel.tsv: All SNV and Indel called with annotation from Funcotator.

Data related to the utilisation of source-separated biowaste for the production of bio-based chemicals

The main objective of this dataset was to demonstrate the technological solution of biosolvents production (mainly bioethanol) via the utilisation of urban biowaste within the city context of Athens, Greece. More specifically, the aim of the dataset is the demonstration of a pre-existing system, namely PILOT 5 for the production of bioethanol.  The results achieved were very promising for the viability of the process, either with dried or wet feedstock. It is important to consider the energy consumption, as it constitutes more than 50% in the overall ethanol production cost. In comparison, the total energy consumption for the production of ethanol with dried feedstock is 26% higher than with wet feedstock. The most energy-intensive stage is drying, followed by distillation.

Multi-Source Precipitation Data Fusion Across Continental United States

Dataset Description: This dataset supports our research presented in the paper "A deep learning-based framework for multi-source precipitation fusion" by Gavahi et al., (2023) published in Remote Sensing of Environment. The study introduces a novel deep learning architecture for merging and downscaling multiple precipitation products, aiming to enhance quantitative precipitation estimation (QPE) accuracy. The developed model, the Precipitation Data Fusion Network (PDFN), integrates 3D-CNN and ConvLSTM layers to capture the inherent spatiotemporal dependencies of precipitation data. The results indicate significant improvements in error statistics. The dataset includes merged daily precipitation estimations using the PDFN model. The data cover the Continental United States (CONUS) and are provided at a spatial resolution of 0.05 degrees. Temporal coverage spans from January 1, 2015, to April 30, 2024. The coordinate reference system used is WGS1984. Note: Since the PERSIANN-CDR dataset is only available until the end of 2023, in this dataset, we used PDIR-Now instead to ensure the dataset's continuity and completeness. In the original paper, we used PERSIANN-CDR, but here we used PDIR-Now to extend the dataset to cover the period until April 30, 2024. Usage Notes: This dataset is intended for use in applications such as land surface modeling, flood forecasting, drought monitoring and prediction. Users are requested to cite the associated paper when utilizing the dataset for academic or research purposes.  Related Publications: For further details on the methodology and applications of this dataset, refer to the paper "Gavahi, K., E. Foroumandi, and H. Moradkhani (2023), A deep learning-based framework for multi-source precipitation fusion, Remote Sensing of Environment, doi:10.1016/j.rse.2023.113723"

openmit-licenseApr 2024View details →

eELib: Open-Source Model Library for Prosumer Power Systems and Energy Management Strategies (data)

Dataset and results used for the simulations in following publication: Carsten Wegkamp, Henrik Wagner, Eike Niehs, Julien Essers, Marcel Lüdecke, Mattias Hadlak, Bernd Engel: "eELib: Open-Source Model Library for Prosumer Power Systems and Energy Management Strategies", Open Source Modelling and Simulation of Energy Systems (OSMSES) 2024, Vienna, Austria, 2024   This contains the input (scenario) files for the building & grid scenario and the results of the two simulations. It uses the elenia Energy Library (eELib) with release version 1.0.0: https://gitlab.com/elenia1/elenia-energy-library

Collection data and molecular datasets for: Defining species-specific seed sourcing strategies for restoration: An example of how to use genetic data to inform seed collections for multiple co-occurring species

Two files for each dataset are provided: Metadata files contain colelcting information for the samples in each molecular dataset as well as the group assignments (species, genetic neighbourhood and sites) used in analyses, saved as an excel spreadsheet. Molecular datasets containing samples and SNPs used in analyses. The data is formatted as a genlight object saved as an RData file that can be read into the R statisical environment and analysed using the 'dartR' package (Gruber et al. 2018).

opencc-zeroJul 2024View details →

dryad36/100

Data from: Nutrient sources, phytoplankton blooms, and hypoxia along the Chinese coast in the East China Sea: Insight from summer 2014

This dataset contains data collected onboard the R/V Ocean Researcher I during the summer of 2014 (August 20–31) East China Sea described in the paper: "C.-C. Chen, W.-C. Chou, and C.-C. Hung (2024). Nutrient sources, phytoplankton blooms, and hypoxia along the Chinese coast in the East China Sea: Insight from summer 2014, Marine Pollution Bulletin (accepted on July 4 2024)".

source data figures De Vries 2024 (Comm. E&E)

This is the source data for producing the manuscript figures of De Vries et al. (2024) Comm. Earth & Env.

Source data

Open the record for dataset details and reuse information.

Single-cell analyses of polyclonal Plasmodium vivax infections and their consequences on parasite transmission Source Data

Source Data for the paper by Hazzard et al. entitled Single-cell analyses of polyclonal Plasmodium vivax infections and their consequences on parasite transmission.

Source data including NIfTI images

The file src_data_conat.xlsx contains all available source data from the figures and tables. The sheets are named after the figure/table the data were used for. The sheet figs_supp_PL contains the data for Figs. S1, S13, S14, S15, S16 and Tab. S4.   The zipped folder src_data_images.zip contains the (group-level) NIfTI images in MNI space shown in different figures in both the main text and supplement. Their names are prefixed with the number of the figure. Tabs. 2 and S3 were created based on the files 2B_w_CR.nii.gz and 3_CR_sig.nii.gz.   The file data_validation.xlsx contains all data necessary to reproduce both the cross-sectional and longitudinal validation analyses (see subsection "CR score moderates effects of pathology on cognitive performance, also longitudinally" in the results section of the paper).

Data and codes for the work: "Quasiparticle dynamics in a superconducting qubit irradiated by a localized infrared source"

All data and codes used for the work can be found here.    <ul> <li>For figures 2 and SM6, one must unzip the files and change the directory in the codes accordingly.</li> <li>For figures 3, SM7 and SM8, one should use the file "Figure3_data.h5", already containing the analysis of the raw data of the pulsed experiment, which is also contained inside the zip file.</li> <li>Comsol 6.2 was used to create the simulation file for the sample temperature.</li> </ul>

opencc-by-4.0Dec 2017View details →

Coding data of manuscript "How Do Developers Utilize Source Code from Stack Overflow?"

This is the coding data for the manuscript "How Do Developers Utilize Source Code from Stack Overflow?".