Find research datasets worth reusing

Fig. 4 in A new technique and software to optimize compression and data retrieval in the Material Examined section of taxonomic publications

Fig. 4. Repetitions in each column identified with the ib code for all columns (strict usage of the gridsetting technique).

Dataset of Last Interglacial climate from publication "Modeled storm surge changes in a warmer world: the Last Interglacial" by P. Scussolini et al.

Results from the simulation of Last Interglacial (Eemian) climate with climate model CESM1.2. Variables are: sea-level pressure (PSL); meridional wind (V), and zonal wind (U). Time step is 6-hourly. Detailed description of the methods are in the original publication: Scussolini, P., Dullaart, J., Muis, S., Rovere, A., Bakker, P., Coumou, D., Renssen, H., Ward, P. J., and Aerts, J. C. J. H.: Modelled storm surge changes in a warmer world: the Last Interglacial, EGUsphere, 2022, 1-20, 10.5194/egusphere-2022-101, 2022.

Dataset of pre-industrial climate from publication "Modeled storm surge changes in a warmer world: the Last Interglacial" by P. Scussolini et al.

Results from the simulation of pre-industrial climate with climate model CESM1.2. Variables are: sea-level pressure (PSL); meridional wind (V), and zonal wind (U). Time step is 6-hourly. Detailed description of the methods are in the original publication: Scussolini, P., Dullaart, J., Muis, S., Rovere, A., Bakker, P., Coumou, D., Renssen, H., Ward, P. J., and Aerts, J. C. J. H.: Modelled storm surge changes in a warmer world: the Last Interglacial, EGUsphere, 2022, 1-20, 10.5194/egusphere-2022-101, 2022.

A Novel Curated Scholarly Graph Connecting Textual and Data Publications

This dataset contains an open and curated scholarly graph we built as a training and test set for data discovery, data connection, author disambiguation, and link prediction tasks. This graph represents the European Marine Science community included in the OpenAIRE Graph. The nodes of the graph we release represent publications, datasets, software, and authors respectively; edges interconnecting research products always have the publication as source, and the dataset/software as target. In addition, edges are labeled with semantics that outline whether the publication is referencing, citing, documenting, or supplementing the related outcome. To curate and enrich nodes metadata and edges semantics, we relied on the information extracted from the PDF of the publications and the datasets/software webpages respectively. We curated the authors so to remove duplicated nodes representing the same person.  The resource we release counts 4,047 publications, 5,488 datasets, 22 software, 21,561 authors, and 9,692 edges connect publications to datasets/software. This graph is in the curated_MES folder. We provide this resource as: <ol> <li>a property graph: we provide the dump that can be imported in neo4j</li> <li>5 jsonl files containing publications, datasets, software, authors, and relationships respectively. Each line of a jsonl file contains a JSON object representing a node and contains the metadata of that node (or a relationship).</li> </ol> We provide two additional scholarly graphs: <ul> <li>The curated MES graph with the removed edges. During the curation we removed some edges since they were labeled with an inconsistent or imprecise semantics. This graph includes the same nodes and edges as the previous one, and, in addition, it contains the edges removed during the curation pipeline; these edges are marked as Removed. This graph is in the curated_MES_with_removed_semantics folder.  </li> <li>The original MES community of OpenAIRE. It represents the MES community extracted from the OpenAIRE Research Graph. This graph has not been curated, and the metadata and semantics are those of the OpenAIRE Research Graph. This graph is in the original_MES_community folder.</li> </ul>

WRF output for the Geophysical Research Letters publication "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific"

This dataset is the Weather Research and Forecasting (WRF) model output for the Geophysical Research Letters publication entitled "Potential Impacts of Radio Occultation Data Assimilation on Forecast Skill of Tropical Cyclone Formation in the Western North Pacific". The dataset includes the azimuthal-averaged parameters with 0.2°resolution, three-day forecast, and two experiments for all cases analyzed in the publication. Due to the data size, only the variables used in the figures are uploaded (i.e., relative humidity, relative vorticity, temperature, and water vapor mixing ratio). Detailed information, composite calculation, and model settings can be found in the publication.

Predicting mobility and research performance of the faculty members in the economics departments at Turkish public universities

The data used for the regression analysis is in Table 5 on page 17 of the paper.

pSCoPE: Prioritized Single-Cell Proteomics (data for generating publication figures)

Major aims of single-cell proteomics include increasing the consistency, sensitivity, and depth of protein quantification, especially for proteins and modifications of biological interest. To simultaneously advance all these aims, we developed prioritized Single Cell ProtEomics (pSCoPE). pSCoPE consistently analyzes thousands of prioritized peptides across all single cells (thus increasing data completeness) while analyzing identifiable peptides at full duty-cycle, thus increasing proteome depth. These strategies increased the sensitivity, data completeness, and proteome coverage over 2-fold. The gains enabled quantifying protein variation in untreated and lipopolysaccharide-treated primary macrophages. Within each condition, proteins covaried within functional sets, including phagosome maturation and proton transport. This protein covariation within a treatment condition was similar across the treatment conditions and coupled to phenotypic variability in endocytic activity. pSCoPE also enabled quantifying proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition. pSCoPE is freely available and widely applicable, especially for analyzing proteins of interest without sacrificing proteome coverage. Support for pSCoPE is available at: <a href="http://scp.slavovlab.net/pSCoPE">scp.slavovlab.net/pSCoPE</a>   The files contained in this .zip directory are necessary for replicating the analysis and figures associated with the pSCoPE manuscript.

Data set for publication Sikora P., Techman M., Federowicz K., El-Khayatt A.M., Saudi H.A., Abd Elrahman M., Hoffmann M., Stephan D., Chung S.-Y. Insight into the microstructural and durability characteristics of 3D printed concrete: Cast versus printed specimens. Case Studies in Construction Materials (2022), 17, e01320.

Open dataset for publication Sikora P., Techman M., Federowicz K., El-Khayatt A.M., Saudi H.A., Abd Elrahman M., Hoffmann M., Stephan D., Chung S.-Y. Insight into the microstructural and durability characteristics of 3D printed concrete: Cast versus printed specimens. Case Studies in Construction Materials (2022), 17, e01320. <a href="https://doi.org/10.1016/j.cscm.2022.e01320">https://doi.org/10.1016/j.cscm.2022.e01320</a> File 1 - Mechanical characteristics - *.opju (Origin) File 2 - Particle size distributions of used materials - *.opju (Origin) File 3 - Sorptivity measurement data - *.opju (Origin) File 4 - G-code for printing of 1 layered specimen - *txt File 5 - G-code for printing of 3 layered specimens - *txt

opencc-by-4.0Aug 2022View details →

Figs 3-8 in On the identity and distribution of Hebrus nipponicus HORVÁTH, 1929, with a note on the publication date of Hebrus CURTIS (Hemiptera: Hebridae)

Figs 3-8: Morphological details of Hebrus nipponicus, lectotype (except 6). (3) Head, frontal view. (4) Head, lateral view. (5) Mesoscutellum and metanotal elevation of brachypterous morph. (6) Same of macropterous morph (specimen from Philippines). (7) Proctiger, dorsal view. (8) Left paramere, lateral view.

opencc-by-4.0Aug 2022View details →

Figs 1-2 in On the identity and distribution of Hebrus nipponicus HORVÁTH, 1929, with a note on the publication date of Hebrus CURTIS (Hemiptera: Hebridae)

Figs 1-2: Lectotype of Hebrus nipponicus, brachypterous male. (1) Habitus, dorsal view. (2) Paper card with genitalia and labels (not on scale).

opencc-by-4.0Jan 2022View details →

CLAMATO DR2: IGM Lyman-Alpha Forest Tomography Survey Public Data Release of Spectra and Maps

CLAMATO Data Release 2Updated 2023 Jan 11th by Khee-Gan Lee (<a href="mailto:kglee@ipmu.jp">kglee@ipmu.jp</a>)Supporting paper has been published in <a href="https://ui.adsabs.harvard.edu/abs/2022ApJS..263...27H/abstract">ApJS</a> (arXiv:2109.09660)These are data products associated with the second data release (DR2) of the COSMOS Lyman-Alpha Mapping And Tomography Observations (CLAMATO) survey with the Keck-I telescope, which mapped 3D Lyman-alpha forest absorption at 2.05<z<2.55 within the COSMOS field. This is an updated version of the original DR2 repository (https://doi.org/10.5281/zenodo.5842842), which had accidentally left out several files. The other files that was in that repository are duplicated here.The following is the summary of the main products:<ul><li>Source catalog (cl2020_valueadded_release_20200602.txt)</li><li>Reduced spectra, in spec_v0 .tar.gz tarball</li><li>Continuum-fitted 2.05<z<2.55 Lyman-alpha forest pixel data (pixel_data_v0.bin)</li><li>Wiener-reconstructed 3D absorption map (map_2020_v0.bin)</li><li>Reconstructed 3D matter density field using the TARDIS-II algorithm (TARDIS_CLAMATO_DR2_v0.4_smoothed.npy)</li><li>Eigenvalues of the pseudo-deformation tensor calculated from the density field (TARDIS_eigenvalues_CLAMATO_DR2_v0.4_smoothed.npy)</li></ul> Redshift Catalog and SpectraWe provide our redshift catalog and reduced spectra obtained with Keck-I/LRIS-Blue.The source catalog is provided in the ASCII file cl2020_valueadded_release_20200602.txt, with the following columns:<ul><li>SPECFIL: Filename of spectrum (within spec_v0.tar.gz)</li><li>TOMO_ID: CLAMATO ID number</li><li>GMAG: g-magnitude (AB) per Capak et al 2007 photometric catalog</li><li>CONF: Redshift confidence grade: see https://arxiv.org/abs/1710.02894</li><li>ZSPEC: Spectroscopic redshift as determined from CLAMATO spectrum</li><li>QSO: QSO flag (1 if QSO, 0 if non-QSO)</li><li>RA: R.A. in degrees (J2000)</li><li>DEC: Dec in degrees (J2000)</li><li>S/N_1: Estimated Lya-forest S/N at 2.05<z<2.15, -9.0 denotes no estimate</li><li>S/N_2: Estimated Lya-forest S/N at 2.15<z<2.35, -9.0 denotes no estimate</li><li>S/N_2: Estimated Lya-forest S/N at 2.35<z<2.55, -9.0 denotes no estimate</li><li>S/N_RED: Estimated S/N over restframe 1250 Å < \(\lambda\) < 1350 Å, -9.0 denotes no estimate</li><li>TOMOFLAG: Flag on whether sightline was used in tomographic map (0 for no, 1 for yes)</li><li>EXPTIME: Exposure time on the spectrum, in seconds (aggregate)</li></ul>The tarball spec_v0.tar.gz include all the reduced spectra from LRIS-Blue, with the respective filename indicated by the first column of the catalog. We decided not to make available the LRIS-Red spectra.The individual LRIS spectra are provided in FITS format, with the following HDU Extensions:<ul><li>HDU0: Object spectral flux density, in units of \(10^{-17}\,\mathrm{erg\,s^{-1}\,cm^{-2}\,angstrom^{-1}}\)</li><li>HDU1: Noise standard deviation</li><li>HDU2: Pixel Wavelengths in angstroms</li></ul>Pixel DataThe binary file PIXEL_DATA_v0.BIN stores the concatenated Lyman-alpha forest pixels at 2.05<z<2.55 that have been extracted from the 1D spectra and continuum-fitted. The first value in the binary is a 32-bit integer specifying the number of pixels (84608), followed by 5 double-precision floating point (64-bit) vectors storing the x, y, z, sigma_f, and delta_f of the pixels.An example python script to read pixel_data is as follows:import numpy as np with open('pixel_data_v0.bin','r') as f:    npix = np.fromfile(f, dtype=np.int32, count=1)    f.seek(4)    pixel_data = np.fromfile(f,dtype=np.float64).reshape((npix,5))LIST_TOMO_INPUT_2020.TXT is a summary file of corresponding to PIXEL_DATA.BIN, listing the [x,y,z] position of the sightlines that contributed to the file as well as, in the final two columns, the index range that can be used to grab the relevant pixels from the concatenated pixel list.The origin of the map coordinates is at [RA=149.89150 deg, Dec=2.0915050 deg], where x is increasing in the R.A. dimension and y is increasing in the Dec.Tomographic MapThe Wiener-reconstructed map of the 2.05<z<2.55 IGM within the CLAMATO field is the result of applying the "dachshund" algorithm (http://github.com/caseywstark/dachshund) to PIXEL_DATA.BIN, with the configuration file INPUT.CFG . (Caveat: the version of PIXEL_DATA.BIN here is not actually the right version to directly input into the dachshund code: the first integer in this file should not be present for input to dachshund). The reconstructed map is MAP_2020_V0.BIN, which is a 68x56x876 = 3335808 pixel double-precision binary file. The dimension that changes fastest is the z-dimension along the line-of-sight (876 pixels per dimension), followed by the y-dimension  in increasing Declination (56 pixels per dimension) and x-dimension along the direction of increasing R.A. (68 pixels per dimension).Each map pixel represents a 0.5Mpc/h comoving voxel of the Ly-alpha forest absorption. See the Appendix of https://arxiv.org/abs/1710.02894 for the conversion factors to assume to switch between pixel/voxel and [RA, Dec, redshift].Note that no additional smoothing has been applied in this binary, whereas most of the visualizations in the paper have had Gaussian smoothing applied.Reconstructed Density Field and Cosmic WebWe also release the underlying matter density field in the CLAMATO volume, estimated using the TARDIS-II algorithm (https://arxiv.org/abs/2007.15994). This is in numpy format, and can be read directly into a 3D array using Python as follows:>>> import numpy as np >>> den=np.load('TARDIS_CLAMATO_DR2_v0.4_smoothed.npy') >>> np.shape(den) (34, 28, 438) >>> eigen = np.load('TARDIS_eigenvalues_CLAMATO_DR2_v0.4_smoothed.npy') >>> np.shape(eigen) (34, 28, 438, 3)The 3 dimensions correspond to the R.A., Declination, and line-of-sight directions, respectively. Note that unlike the Wiener-filtered absorption map, the TARDIS-II reconstruction outputs are in 1Mpc/h comoving voxels. The coordinate zero-point sand line-of-sight comoving distance-redshift relationship are otherwise the same as the Wiener map. The eigenvalues of the pseudo-deformation tensor are in TARDIS_eigenvalues_CLAMATO_DR2_v0.4_smoothed.npy, with the same array shape as the matter density, but with an additional array dimension storing the 3 sorted eigenvalues at each point in the volume. For reference, we have included an iPython notebook (Tomographic_Maps.ipynb) that plots the Wiener-filtered absorption alongside the TARDIS-II densities and eigenvalues (e.g. Figure 8 of the main paper).

Dataset of Public Objects in Uncontrolled Environment for Navigation Aiding

Computer vision is a new approach to navigation aiding that assist visually impaired people to travel independently. A deep learning-based solution implemented on a portable device that uses a monocular camera to capture public objects could be a low-cost and handy navigation aid. By recognizing public objects in the street and estimating the distance between the users, visually impaired people are able to avoid obstacles in the outdoor environment and walk safely. This dataset is created for navigation aiding and contains 3 classes of objects which commonly exist on the pavement in the city. It is verified that the dataset is of high quality for object detection and distance estimation, and is ultimately utilized for navigation aid solution.

Datasets of Antiaromatic COFs associated with the publication "Antiaromatic Covalent Organic Frameworks Based on Dibenzopentalenes"

X-ray scattering and sorption data associated with the publication "Antiaromatic Covalent Organic Frameworks Based on Dibenzopentalenes". X-ray scattering data is provided for COF and POP materials, including data from stability tests, as three-column ascii files with columns q (nm-1), I (m-1) and uncertainty on I, as well as being provided in 2θ.

opencc-by-4.0May 2022View details →

Dataset for publications "Willingness to pay for urban agriculture in Oslo" and "The Value of Urban Farming in Oslo, Norway: Community Gardens, Aquaponics and Vertical Farming"

Dataset for publications “(Willingness to pay for urban agriculture in Oslo)[https://zenodo.org/record/6510590#.Y851cXbMLfs]" and “The Value of Urban Farming in Oslo, Norway: Community Gardens, Aquaponics and Vertical Farming”. The data was collected as a contingent valuation study of urban agriculture in Oslo, Norway.

Data Availability Statements in the 2020 and 2021 scientific publications of Tampere University

For this dataset, scientific peer-reviewed articles by Tampere University researchers from the years 2020 and 2021 were extracted from the TUNICRIS. A random sample of 40 percent was taken from the listed 4,922 publications according to faculties and years. There were 2,085 analyzed articles, i.e. more than 42 percent of the total number.  To find Data Availability Statements, articles were opened one by one and searched for mentions of research data and its availability. For each article, it was written down whether DAS existed and where in the article it was located. From the contents of DAS, information about data availability, location, openness and possible restrictions on use was written down.  Dataset also includes information about the journals and publications taken from TUNICRIS.  The prevalence of DAS and data openness were examined in relation to different variables. Tampere University faculty information has been removed from the dataset.  Related slides: https://doi.org/10.5281/zenodo.7655892 Related article (in Finnish): Toikko, T., & Kylmälä, K. (2023). Tutkimusdatan saatavuustiedot tieteellisissä artikkeleissa: Raportti Data Availability Statementien käytöstä Tampereen yliopistossa. Informaatiotutkimus, 42(1-2), 31–50. https://doi.org/10.23978/inf.126098

IUCLID for public consultations

The video describes how to participate in the public consultation on a pesticide dossier and how to browse the studies in a IUCLID dossier. It provides a high-level description of the following: <ul> <li>the transparency regulation</li> <li>the steps in the authorisation process</li> <li>the requirement for standard data formats</li> <li>information about the IUCLID format & OECD harmonised templates, which are used globally</li> <li>how a dossier is prepared for the public consultation</li> <li>Open EFSA Portal and how the find substances and dossiers of interest</li> <li>finding and opening dossiers in PUBLIC IUCLID</li> <li>how to view the product dataset and the substance dataset</li> <li>how to view scientific studies in an endpoint study records and endpoint summaries</li> <li>how to create a PDF of a study of interest</li> <li>how to report your comments in the public consultation</li> <li>how to review all the comments in a closed public consultation</li> </ul>

Publication packages for academic research

Infographic summarizing what to include in a publication package, based on the <a href="https://doi.org/10.5281/zenodo.7583831">Guideline for the archiving of academic research for Faculties of Behavioural and Social Sciences in the Netherlands (March 2022)</a>

opencc-by-4.0Feb 2023View details →

98 selected publications analysed in-depth in the context of a systematic literature review on Computational Thinking in compulsory education

This is the list of the 98 publications from between 2016 and 2021 analysed in-depth through a review matrix in the context of the "<a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC128347">Reviewing Computational Thinking in Compulsory Education: State of Play and</a> <a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC128347"> Practices from the Field</a>" research study. The <a href="https://computhink2study.eu/">study</a> was designed, funded, and followed by the European Commission’s Joint Research Centre (JRC) to investigate how Computational Thinking (CT) is currently positioned within compulsory school education in Europe’s various Member States, as well as outside the EU. The study was carried out from April to December 2021 by the Institute for Educational Technology of the Italian National Research Council (CNR-ITD), together with European Schoolnet (EUN) and Vilnius University (VU). Bocconi, S., Chioccariello, A., Kampylis, P., Dagienė, V., Wastiau, P., Engelhardt, K., Earp, J., Horvath, M.A., Jasutė, E., Malagoli, C., Masiulionytė-Dagienė, V., & Stupurienė, G. (2022). Reviewing Computational Thinking in Compulsory Education. Publications Office of the European Union. <a href="https://doi.org/10.2760/126955">https://doi.org/10.2760/126955</a>

opencc-by-4.0Feb 2023View details →

Data set and pseudo-code for publication 10.5194/amt-2022-250

This is the data and example software code for the publication: New Absolute Cavity Pyrgeometer equation by application of Kirchhoff’s law and adding a convection term by Forgan et al., 2023, https://doi.org/10.5194/amt-2022-250