Find research datasets worth reusing

EOL computer vision pipelines: Classification for Image Tagging: Image Rating: Chiroptera

Produced by the EOL Image Rating Classifier. Classifies images as bad or good quality (used for image gallery sorting). Dataset generated for EOL Chiroptera images. See model on the CV for <a href="https://www.kaggle.com/models/eolorg/image-quality-rating-bad-or-good" target="_blank" rel="noopener">EOL Images Model Zoo on Kaggle</a>.

Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 0.325 micron pixel size RAW

This repository contains raw data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Raw data at a pixel-size of 0.325 microns is stored as a .nxs file, and a savu process list is provided to perform the reconstruction we used to reproduced the reconstructed data. This data is one of four resolutions obtained. A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322# The size of the .hdf file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset: <blockquote> curl dataset_url > 169065_raw.hdf </blockquote> Please replace dataset_url with the url you copied.

Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 1.625 micron pixel size RAW

This repository contains raw data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Raw data at a pixel-size of 1.625 microns is stored as a .nxs file, and a savu process list is provided to perform the reconstruction we used to reproduced the reconstructed data. This data is one of four resolutions obtained. A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322# The size of the .hdf file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset: <blockquote> curl dataset_url > 169067_raw.hdf </blockquote> Please replace dataset_url with the url you copied.

Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 2.6 micron pixel size RAW

This repository contains raw data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Raw data at a pixel-size of 2.6 microns is stored as a .nxs file, and a savu process list is provided to perform the reconstruction we used to reproduced the reconstructed data. This data is one of four resolutions obtained. A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322# The size of the .hdf file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset: <blockquote> curl dataset_url > 169068_raw.hdf </blockquote> Please replace dataset_url with the url you copied.

Zinc Doped Zeolite 13X DIAD X-Ray Diffraction Computed Tomography - 25 and 50 micron spot-size

This repository contains X-Ray Diffraction Computed Tomography (XRD-CT) data of a zinc doped zeolite 13X sample on the Dual Imaging and Diffraction (DIAD / K11) at Diamond Light Source. XRD-CT data is provided at a diffraction spot size of 25 microns for three region of interest slices, with a dataset size of 40x2000x80. Both the raw and reconstructed data is provided, along with the code to perform the reconstructions.  XRD-CT data is also provided at a diffraction spot size of 50 microns for a full 1.05mm volume, with a dataset size of 20x2000x40. Scans start at 43336 and finish at 43401, with a movement of 0.05mm vertically upwards between each scan. The raw and reconstructed data is provided, along with the code used to perform the reconstructions. Note: Scan 43401 is excluded as a phase-based reconstruction could not be performed. Powder X-Ray Diffraction data can be found in an alternative repository at 10.5281/zenodo.13329670 which provides the q-values of the peaks for both the Zn and Na phase to allow the best reconstructions. A detailed data descriptor pre-print can be found at https://arxiv.org/abs/2409.07322

Zinc Doped Zeolite 13X I13-2 X-Ray Computed Tomography - 0.8125 micron pixel size RAW

This repository contains raw data for the zinc-doped zeolite 13X sample imaged on the I13-2 beamline at Diamond Light Source. Raw data at a pixel-size of 0.8125 microns is stored as a .nxs file, and a savu process list is provided to perform the reconstruction we used to reproduced the reconstructed data. This data is one of four resolutions obtained. A detailed data descriptor pre-print is available at https://arxiv.org/abs/2409.07322# The size of the .hdf file is >50GB and cannot be downloaded from the browser. It is recommended to use a terminal to download the data using the 'curl' or 'wget' command. To generate a file url, right-click the 'Download' button for the dataset you want to download and select 'Copy Link. Enter the following command in your terminal to download the dataset: <blockquote> curl dataset_url > 169066_raw.hdf </blockquote> Please replace dataset_url with the url you copied.

opencc-by-4.0Nov 2021View details →

RDF version of the data from Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).

This is an RDFied version of the dataset published by Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021). The original dataset publication DOI: <a href="https://doi.org/10.1016/j.impact.2021.100308">https://doi.org/10.1016/j.impact.2021.100308</a> The Original publication authors: Anastasios G. Papadiamantis, Antreas Afantitis, Andreas Tsoumanis, Eugenia Valsami-Jones, Iseult Lynch, Georgia Melagraki

PhasAGE Training School 1 - Computational prediction and databases of protein phase separation - PRACTICAL

The Training School 1 “Computational Methods to Study Protein Phase Separation” is the first edition of a series of PhasAGE training activities. The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of Phase Separation, its role in biological processes and diseases. In addition, the course will provide an overview of the available computational resources to navigate this knowledge. Participants will have hands-on training in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.

PhasAGE Training School 1 - Computational prediction and databases of protein phase separation Overview- LECTURE

PhasAGE Training School 1 - Computational prediction of intrinsic disorder in proteins-DisProt - PRACTICAL

PhasAGE Training School 1 - Computational prediction of intrinsic disorder in proteins-MobiDB - PRACTICAL

Electrochemical and Spectroscopic Data supported by Computational Models for Exploring the Metal- and Ligand-Based Oxidation of Mackinawite Nanoparticles

Supporting information to our study, where under anaerobic conditions, ferrous iron reacts with sulfide producing FeS precipitate, which can then undergo a temperature, redox potential, and pH dependent maturation process resulting in the formation of oxidized mineral phases such as gregite or pyrite. The dataset provide information about the chemical speciation of iron-sulfide by cyclic voltammetry, Raman and X-ray absorption spectroscopic techniques. Nanoparticulate FeS was found to get oxidized to a Fe3+ containing FeS phase at -0.5 V vs. Ag/AgCl (pH = 7) and in a concomitant oxidation step, polysulfides are proposed to give a material described as Fe2+(1−3x)Fe3+(2x)S2-(1-y)(Sn2-)y. The thermodynamic differences between ligand- and metal-based oxidation processes from density functional theory can be used to describe one- and two-electron electronic and structural transformations. These findings together point to the existence of a previously unknown, metastable FeS phase located between FeS and greigite (Fe2+Fe3+2S2-4) along a metal oxidation path, and Fe2+S2- and pyrite (Fe2+S22-) along a ligand oxidation path, respectively.

opencc-by-4.0Jul 2021View details →

GVI: Sample data for computing VGVI. Vancouver, BC and Manchester.

This is a supplement for the <a href="http://doi.org/10.5281/zenodo.5068835">GVI: Greenness Visibility Index R package</a>.   Description: This dataset contains raster (TIFF) data for computing the VGVI for the City of Vancouver and Manchester. Greater Manchester: <ul> <li>Digital Terrain Model (DTM): <ul> <li>Spatial Resolution: 5m</li> <li>Source: <a href="https://data.gov.uk/dataset/5f6f7d5b-3f4c-4476-bfb8-cda490c9cf0e/lidar-composite-dtm-2017-50cm">LIDAR Composite DTM 2017 - 50cm</a></li> <li>Licence: <a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">Open Government Licence (OGL)</a></li> <li>File name: GreaterManchester_DTM_5m.tif  </li> </ul> </li> <li>Digital Surface  Model (DSM): <ul> <li>Spatial Resolution: 5m</li> <li>Source: <a href="https://data.gov.uk/dataset/0ab507af-cd91-40cb-8524-3efafc267211/lidar-composite-dsm-2017-50cm">LIDAR Composite DSM 2017 - 50cm</a></li> <li>Licence: <a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">Open Government Licence (OGL)</a></li> <li>File name: GreaterManchester_DSM_5m.tif  </li> </ul> </li> <li>Greenspace Mask: <ul> <li>Spatial resolution: 5m</li> <li>Source: <a href="https://doi.org/10.3390/land7010017">Dennis et al. 2017</a></li> <li>Licence: <a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">Open Government Licence (OGL)</a></li> <li>File name: GreaterManchester_GreenSpace_5m.tif</li> </ul> </li> </ul>   Vancouver: <ul> <li>Digital Terrain Model (DTM): <ul> <li>Spatial Resolution: 1m</li> <li>Source: Canada’s Open Government Portal</li> <li>Licence: <a href="https://open.canada.ca/en/open-government-licence-canada">Open Government Licence - Canada</a></li> <li>File name: Vancouver_DTM_1m.tif  </li> </ul> </li> <li>Digital Surface Model (DSM): <ul> <li>Spatial Resolution: 1m</li> <li>Source: Canada’s Open Government Portal</li> <li>Licence: <a href="https://open.canada.ca/en/open-government-licence-canada">Open Government Licence - Canada</a></li> <li>File name: Vancouver_DSM_1m.tif  </li> </ul> </li> <li>Greenspace Mask: <ul> <li>Spatial Resolution: 2m</li> <li>Source: Land Cover Classification 2014 - 2m LiDAR</li> <li>Licence: <a href="http://www.metrovancouver.org/data">Metro Vancouver</a></li> <li>File name: Vancouver_GreenSpace_2m.tif  </li> </ul> </li> <li>Landuse <ul> <li>Spatial Resolution: 2m</li> <li>Source: Land Cover Classification 2014 - 2m LiDAR</li> <li>Licence: <a href="http://www.metrovancouver.org/data">Metro Vancouver</a></li> <li>File name: Vancouver_LULC_2m.tif</li> </ul> </li> </ul>

opencc-by-4.0May 2021View details →

Mutually Beneficial Combination of Molecular Dynamics Computer Simulations and Scattering Experiments - DATA

Specular reflectivities of the SoyPC bilayer stack measured at the vertical reflectometer MARIA  at Heinz Maier-Leibnitz Zentrum (MLZ) in Garching, Germany. Offspecular reflectivity map (log scale) of the multilayer sample as a function of theangle of incidence (θi) and of the reflection angle (θi). Specular reflectivities of the Si/SiO2/DMPC/H2O at 4 different contrasts (H2O, D2O, SMW and 4MW) Small-angle neutron scattering of the unilamellar  SoyPC

opencc-by-4.0Jul 2021View details →

Modelling of excitation propagation on computer models of insoles colonised by fungal mycelium. Videos and potential difference recordings.

We used an artistic image of the mycelium network projected onto a $364 \times 985$ nodes grid.  The original image $M=(m_{ij})_{1 \leq j \leq n_i, 1 \leq j \leq n_j}$, $m_{ij} \in \{ r_{ij}, g_{ij}, b_{ij} \}$, where $n_i=364$ and $n_j=985$, and $1 \leq r, g, b \leq 255$, was converted to a conductive matrix $C=(m_{ij})_{1 \leq i,j \leq n}$ derived from the image as follows: $m_{ij}=1$  if $r_{ij}>170$, $g_{ij}>170$ and $b_{ij}<200$; a dilution operation was applied to $C$.  FitzHugh-Nagumo (FHN) equations is a qualitative approximation of the Hodgkin-Huxley model of electrical activity of living cells: \begin{eqnarray} \frac{\partial v}{\partial t} & = & c_1 u (u-a) (1-u) - c_2 u v + I + D_u \nabla^2 \\ \frac{\partial v}{\partial t} & = & b (u - v), \end{eqnarray} where $u$ is a value of a trans-membrane potential, $v$ a variable accountable for a total slow ionic current, or a recovery variable responsible for a slow negative feedback, $I$ {is} a value of an external stimulation current. The current through intra-cellular spaces is approximated by $D_u \nabla^2$, where $D_u$ is a conductance. The term $D_u \nabla^2 u$ governs a passive spread of the current. The terms $c_2 u (u-a) (1-u)$ and $b (u - v)$ describe the ionic currents. The term $u (u-a) (1-u)$ has two stable fixed points $u=0$ and $u=1$ and one unstable point $u=a$, where $a$ is a threshold of an excitation. We integrated the system using the Euler method with the five-node Laplace operator, a time step $\Delta t=0.015$ and a grid point spacing $\Delta x = 2$, while other parameters were $D_u=1$, $a=0.13$, $b=0.013$, $c_1=0.26$. We controlled excitability of the medium by varying $c_2$ from 0.05 (fully excitable) to 0.015 (non excitable). Boundaries are considered to be impermeable: $\partial u/\partial \mathbf{n}=0$, where $\mathbf{n}$ is a vector normal to the boundary.  To record dynamics of excitation in the network, as if in laboratory experiments, we simulated electrodes by calculating a potential $p^t_x$ at an electrode location $x$ as $p_x = \sum_{y: |x-y|<2} (u_x - v_x)$. Configuration of electrodes $1, \cdots, 16$ is shown in Fig.~\ref{fig:mycelium}c.  Time-lapse snapshots provided in the paper were recorded at every 100\textsuperscript{th} time step, and we display sites with $u >0.04$; videos and figures were produced by saving a frame of the simulation every 100\textsuperscript{th} step of the numerical integration and assembling the saved frames into the video with a play rate of 30 fps.  Insole_01: Excitation started at electrode E2 Insole_10: Excitation started at electrode E1 Insole_11: Excitation started at electrodes E1 and E2

opencc-by-4.0Sep 2021View details →

A computational intelligence approach to predict energy demand using Random Forest in a Cloudera cluster

Society’s energy consumption has shot up in recent years, making the prediction of its demand a current challenge to ensure an efficient and responsible use. Artificial intelligence techniques have proven to be potential tools in handling tedious tasks and making sense of large-scale data to make better business decisions in different areas of knowledge. In this article, the use of random forests algorithms in a Big Data environment is proposed for households energy demand forecasting. The predictions are based on the use of information from different sources, confirming a fundamental role of socioeconomic data in consumer’s behaviours. On the other hand, the use of Big Data architectures is proposed to perform horizontal and vertical scaling of the solution to be used in real environments. Finally, a tool for high-resolution predictions with great efficiency is introduced, which enables energy management in a very accurate way. Raw data is incuded in data.csv. This file contains half hourly home electricity consumption registers for 4404 households with fix tariffs (not subject to dynamic time of use) for a period between November 2011 and February 2014. Original information was acquired from the Low Carbon London project led by UK Power Networks (https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households) RFResults.zip contains the energy predictions for each ACORN group using the generated Random Forest algorithm. For this purpose, the first 613 days of a total of 818 observations of each group were considered for training and the last 205 days for testing. Meteorological data was adquired from the darksky app (https://darksky.net). These data are included in the weather_hourly_darksky.csv uk_bank_holidays. xlsx contains the dated of UK bank holidays for the studied period, used as additional variable related to occupancy

Fighting COVID-19 with computational tools: an AI guided review of 17,000 studies - The CSCoV database.

CSCoV (Computational Studies about COVID-19) is a dataset containing COVID-19 related studies extracted from PubMed, bioRxiv, medRxiv, and arXiv, together with article and author related metrics obtained from Semantic Scholar (plus page views from bioRxiv and medRxiv). Using machine learning, the articles are categorized in six topics (Pharmacology, Genomics, Epidemiology, Healthcare, Clinical Medicine, Clinical Imaging) and prioritized. The database is periodically updated. <ul> <li>Publication: TBA</li> <li>Files included in this release: <ul> <li>cscov_09_2021.png: dataset statistics for the current CSCoV release.</li> <li>cscov_09_2021.tsv: CSCoV database.</li> <li>schema.json: metadata.</li> <li>cscov_09_2021.tar.gz: Doc2Vec and DeepWalk features used for the DL model</li> </ul> </li> <li> Source code: <a href="https://github.com/SFB-KAUST/covid-review">https://github.com/SFB-KAUST/covid-review</a> </li> </ul>

opencc-zeroMay 2023View details →

BIP! NDR (NoDoiRefs): a dataset of citations from papers without DOIs in computer science conferences and workshops

<h2>Overview</h2> In the field of Computer Science, conference and workshop papers serve as important contributions, carrying substantial weight in research assessment processes, compared to other disciplines. However, a considerable number of these papers are not assigned a Digital Object Identifier (DOI), hence their citations are not reported in widely used citation datasets like OpenCitations and Crossref, raising limitations to citation analysis. While the Microsoft Academic Graph (MAG) previously addressed this issue by providing substantial coverage, its discontinuation  has created a void in available data. BIP! NDR aims to alleviate this issue and enhance the research assessment processes within the field of Computer Science. To accomplish this, it leverages a workflow that identifies and retrieves Open Science papers lacking DOIs from the DBLP Corpus, and by performing text analysis, it extracts citation information directly from their full text. The current version of the dataset contains ~4.3M citations made by approximately 211K open access Computer Science conference or workshop papers that, according to DBLP, do not have a DOI. The DBLP snapshot used for this version was the one released on September 2025.  <h2>Dataset files</h2> <h3>1. Core Non-DOI Citation Dataset - bip_ndr_{version}.tar.gz</h3> The dataset is formatted as a JSON Lines (JSONL) file (one JSON Object per line) to facilitate file splitting and streaming.  Each JSON object has three main fields: <ul> <li> “_id”: a unique identifier, </li> <li> “citing_paper”, the “dblp_id” of the citing paper, </li> <li> “cited_papers”: array containing the objects that correspond to each reference found in the text of the “citing_paper”; each object may contain the following fields: <ul> <li> “dblp_id”: the “dblp_id” of the cited paper. Optional - this field is required if a “doi” is not present. </li> <li> “doi”: the doi of the cited paper. Optional - this field is required if a “dblp_id” is not present. </li> <li> “bibliographic_reference”: the raw citation string as it appears in the citing paper. </li> </ul> </li> </ul> Changes from previous version: <ul> <li>Added more papers from DBLP.</li> </ul> <h3>2. Citation Intents Dataset - bip_ndr_ci_{version}.tar.gz</h3> This file enriches the BIP! NDR dataset with citation-level intent classification. It preserves the same base structure of the previous file, while adding a nested array of "citations" with each element of "cited_papers". Each "citation" provides the local textual context, section, and intent of the citation in the following format: <ul> <li>"citation_id": Unique identifier in the format {citing_id}>{cited_id}_CIT{index} linking the citing and cited entities.</li> <li>"section": The section of the citing paper where the citation occurs (e.g., Introduction, Methods, Results).</li> <li>"intent": Inferred purpose of the citation based on textual context (see classification schema below).</li> </ul> The "intent" field follows the SciCite classification schema, which categorizes citations into three high-level functional types: <ol> <li>background information: The citation states, mentions, or points to the background information giving more context about a problem, concept, approach, topic, or importance of the problem in the field.</li> <li>method: Making use of a method, tool, approach or dataset.</li> <li>results comparison: Comparison of the paper's results/findings with the results/findings of other work.</li> </ol> The classification is done with the <a href="https://huggingface.co/sknow-lab/Qwen2.5-14B-CIC-SciCite">Qwen2.5-14B-CIC-SciCite fine-tuned Large Language Model, published by Athena RC</a>.  Changes from previous version:  <ul> <li>Added more papers with intent</li> </ul>

opencc-by-4.0Oct 2022View details →

A computer program to calculate discrete wavelet transform for one-dimensional signals

This is the most recent version of the True Basic program 'NDHAAR.TRU', which was part of the supplementary materials for the following publication: X. Dong, P. Nyren, B. Patton, A. Nyren, J. Richardson and T. Maresca, 2008. Wavelets for agriculture and biology: A tutorial with applications and outlook. BioScience 58: 445-453. The original version (1.0, April 8, 2008) accepts a one-dimensional signal with 1024 data points. It was previously posted at http://www.ag.ndsu.edu/CentralGrasslandsREC/wavelets-for-agriculture-and-biology Version 1.1 (June 1, 2010) accepts signals with a length of 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, or 65536. This version with documentation was initially posted at www.infoclearinghouse.com. Later the website was closed. Now the documentation can still be accessed at https://www.scss.tcd.ie/Khurshid.Ahmad/Research/Wavelets/wva.pdf. Version 1.2 is posted in this current upload. A major change in this version is the correction of a few typos existing in Version 1.1, so that the program can correctly process signals longer than 4096 (that is, with signal length as either of 8192, 16384, 32768, or 65536). Note that Version 1.1 is fine in correctly processing signals with a length at or shorter than 4096. Two sample input data files are included. Also included is the original supplemental material Suppl_dong_2008.pdf. The first input data file 'pdsi.txt' has a length of 1024, and  the related output files are OO1.txt, OO2.txt, OO3.txt, OO4.txt and OO5.txt. These data files  are discussed in the original BioScience paper as well as in Suppl_dong_2008.pdf.  The second sample input file 'warm.txt' has a length of 65536 and the related output files are OUT_1.txt, OUT_2.txt, OUT_3.txt, OUT_4.txt, and OUT_5.txt. The sample input file warm.txt contains NDVI values of winter wheat measured at Uvalde, TX, USA, from about 8 am to 10 am on April 12, 2018. The measurement was made using an ACS-430 Crop Circle sensor mounted to a push-wheel cart. This file and the associated output files are part of the intermediate results for Supplementary Figure S2 to the article entitled "Leaf water potential of field crops estimated using NDVI in ground-based remote sensing - opportunities to increase prediction precision" (PeerJ. 9:e12005 DOI 10.7717/peerj.12005), which can be accessed at https://zenodo.org/record/4574674#.YD7kI2hKiUk