Find research datasets worth reusing

opencc-by-4.0May 2023View details →

Data accompanying "In silico analysis of the profilaggrin sequence indicates alterations in the stability, degradation route, and intracellular protein fate in filaggrin null mutation carriers" article.

This research was supported by the National Science Centre, Poland, grant PRELUDIUM number 2021/41/N/NZ1/03473 to NS, National Science Centre, Poland, grant SONATA BIS number 2019/34/E/NZ6/00354 to DG-O, as well as POIR.04.04.00-00-21FA/16–00 grant, carried out within the First TEAM programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund (awarded to DG-O). WP was supported by the National Science Centre, Poland, grant SONATA-BIS number 2021/42/E/NZ1/00190. SB is supported by a Wellcome Trust Senior Research Fellowship (220875/Z/20/Z).

opencc-by-4.0Aug 2024View details →

Supporting information and Figures of the article "Harnessing the Biomimetic Effect of Macromolecular Crowding in the Cell-Derived Model of Clubfoot Fibrosis"

Open the record for dataset details and reuse information.

opencc-by-4.0Apr 2024View details →

Experimental data for the article "High Resolution Rovibrational Spectroscopy of the ν6 and ν3+v7 Bands of H2CCCH+"

data measured with COLTRAP apparatus 6th December - 11th December 2023  column 1 (x-axis) is frequency in cm-1, accuracy ~0.001 cm-1, precision ~0.0002 cm-1  column 2 (y-axis) are ion counts on mass 39u, integer  The data given here are original data with 14 concatenated data files.  Some spurious data points have been commented out by using "#"

Experimental data used in the article entitled "Engineering an ultra-fine grained microstructure, twins and stacking faults in PBF-LB/M Al-Si alloy via KoBo extrusion method"

Dataset include EBSD results: KOB;O.ang and LPBF_condition.ang Tensile test results: KOBO-processed sample: KOBO.xls LPBF sample: SLM.xls

Data of the subfigures of article "Intracellular dry mass density increases under growth-induced pressure"

Data from the article "Intracellular dry mass density increases under growth-induced pressure". Each excel file tab corresponds to the data plotted in the different sub-figures.

opencc-by-4.0Feb 2024View details →

Data from article "The potential recording of North Ionian Gyre's reversals as a decadal signal in sea level during the instrumental period"

This repository hosts the data from Meli M. 2024: The potential recording of North Ionian Gyre’s reversals as a decadal signal in sea level during the instrumental period. Scientific Reports, 14, 4907. https://doi.org/10.1038/s41598-024-55579-4 The dataset contains: - Python code for analysis of altimetry products ("satellite.py") and tide gauge time series ("tg.py"). - The "satellite" folder contains txt files, each representing the annual time series of the first (_1) and second (_2) oscillatory modes decomposed from the satellite altimetry signal and averaged over specific sub-basins. The nc file, on the other hand, contains the time series for every grid point of the altimetry product, along with the various layers (modes) decomposed from the original signal. The "tide_gauges" folder contains npy files, each representing the annual time series of all oscillatory modes decomposed at each tide gauge selected for this analysis. The csv file contains the sub-basin and regional averaged signals of the second oscillatory mode, along with their standard deviations. UPDATE: In version 2 of the repository, the file IMFs_array.nc (located in the folder satellite.zip) has been updated to IMFs_array_v2.nc. In this version, the land areas in Mode 2 have been masked.

Data from the article titled Mapping of networks and professional capital in the La Algarabía occupational center.

Project PID2020-117020GB-I00 , funded by : Ministerio de Ciencia e Innovación de España/ AEI/10.13039/501100011033 and by the predoctoral contracts grant for the training of PhD implemented by the [grant number PRE2021-098075]

Appendix Figures B.1–B.7 from the article 'Core Prominence as a Signature of Restarted Jet Activity in the LOFAR Radio-Galaxy Population' (Accepted for publication in the journal Astronomy & Astrophysics on August 23, 2024)

Figure captions:   Figs. B.1–B.5. Images of 69 candidate restarted galaxies selected based on high radio $\mathrm{CP_{1400}}$ combined with low SB of extended emission, a steep spectrum of the core, and USS extended emission coupled with a bright core and summarised in Tables A.1 and A.2. Radio contours from VLA FIRST maps (white, $5^{\prime\prime}$), LOFAR high-resolution maps (black, $6^{\prime\prime}$), and NVSS maps (purple, $45^{\prime\prime}$) are overlaid on the LOFAR low-resolution resolution maps (orange, $20^{\prime\prime}$). The contouring of all the maps is made at $\,\sigma_\mathrm{local}\times(-3,3,5,10,20,30,40,50,100,150,200)$ levels, with $\sigma_\mathrm{local}$ representing the local RMS noise of the corresponding maps. The host galaxy position is marked with a yellow cross.   Figs. B.6–B.7. Images of sources excluded from the sample of restarted candidates following the criteria discussed in Sect. 3.1, Sect. 3.2 and Sect. 3.3 and summarised in Tables A.3 and A.4. Radio contours from VLA FIRST maps (white, $5^{\prime\prime}$), LOFAR high-resolution maps (black, $6^{\prime\prime}$), and NVSS maps (purple, $45^{\prime\prime}$) are overlaid on the LOFAR low-resolution resolution maps (orange, $20^{\prime\prime}$). The contouring of all the maps is made at $\,\sigma_\mathrm{local}\times(-3,3,5,10,20,30,40,50,100,150,200)$ levels, with $\sigma_\mathrm{local}$ representing the local RMS noise of the corresponding maps. The host galaxy position is marked with a yellow cross.

Data underlying the manuscript: "Analysis of Research Data Sharing in Scientific Articles on Climate Change in the Covid-19 Year. The Spanish case 2020".

This is the research data for the manuscript "Analysis of Research Data Sharing in Scientific Articles on Climate Change in the Covid-19 Year. The Spanish case 2020". The following is the original abstract: Introduction: Sharing research data on climate change would facilitate the development of solutions to curb its impact, for this, data needs to be shared in an optimal way. General objective: To identify how many Spanish scientific articles on climate change published during 2020 share their research data in some way. Specific objectives: a) Identify the attributes of shared research data b) Describe the characteristics of the case studies found on how research data are shared. Methodology: Qualitative and descriptive study analyzing nine attributes: availability (1), accessibility (2), format (3), license (4), linkage (5), funding (6), editorial policy (7), content (8), statistics (9). Results: We analyzed 2212 articles were analyzed, 1867 (84%) articles had no associated research data. The remaining 16% have associated research data: 152 (7%) articles deposited their data in repositories, 42 (2%) submitted their data as supplementary material, 136 (6%) will share their data upon request to the author and 15 (1%) do not have publication permissions. Conclusions: Researchers are willing to share their research data, but under different conditions. Researchers who reused research data did not share the new data they generated. There is a lack of training among researchers on how to manage their research data. There is information on the web on this topic, but it is not just a matter of publishing manuals, but also of creating training spaces within universities, institutes and research centers to build a community of researchers committed to Open Science.

Dataset related to article: "Health risk assessment for dietary exposure to 3-monochloropropane-1,2-diol, 2-monochloropropane-1,2-diol, and glycidol for Italian consumers"

Excel file containing food consumption data and concentration data in foodstuff used for exposure assessment

Simulation trajectories for the article "Simulated Solute Tempering 2"

Simulation trajectories for the article "Simulated Solute Tempering 2". For each set of simulation is provided: <ul> <li>One .pdb file of the system without solvant and hydrogen atoms coordinates.</li> <li>Trajectorie files in .xtc format without solvant and hydrogen atoms coordinates for each of the 4 replicas.</li> <li>DataFrame files in .csv format containing: <ul> <li>Aim Temp (K)</li> <li>E solute scaled (kJ/mole)</li> <li>E solute not scaled (kJ/mole)</li> <li>E solvent (kJ/mole)</li> <li>E solvent-solute (kJ/mole)</li> <li>Step</li> <li>Potential Energy (kJ/mole)</li> <li>Total Energy (kJ/mole)</li> <li>Temperature (K)</li> <li>Speed (ns/day)</li> <li>Time(µs)</li> <li>lambda</li> </ul> </li> </ul> List of files: <pre><code>. ├── CLN025 │ ├── SST2 │ │ ├── CLN025_SST2_F_1.csv │ │ ├── CLN025_SST2_F_1.pdb │ │ ├── CLN025_SST2_F_1.xtc │ │ ├── CLN025_SST2_F_2.csv │ │ ├── CLN025_SST2_F_2.xtc │ │ ├── CLN025_SST2_F_3.csv │ │ ├── CLN025_SST2_F_3.xtc │ │ ├── CLN025_SST2_F_4.csv │ │ ├── CLN025_SST2_F_4.xtc │ │ ├── CLN025_SST2_F_6_rungs_1.csv │ │ ├── CLN025_SST2_F_6_rungs_1.xtc │ │ ├── CLN025_SST2_F_6_rungs_2.csv │ │ ├── CLN025_SST2_F_6_rungs_2.xtc │ │ ├── CLN025_SST2_F_6_rungs_3.csv │ │ ├── CLN025_SST2_F_6_rungs_3.xtc │ │ ├── CLN025_SST2_F_6_rungs_4.csv │ │ ├── CLN025_SST2_F_6_rungs_4.xtc │ │ ├── CLN025_SST2_F_6_rungs.pdb │ │ ├── CLN025_SST2_F_Tref_350_1.csv │ │ ├── CLN025_SST2_F_Tref_350_1.pdb │ │ ├── CLN025_SST2_F_Tref_350_1.xtc │ │ ├── CLN025_SST2_F_Tref_350_2.csv │ │ ├── CLN025_SST2_F_Tref_350_2.xtc │ │ ├── CLN025_SST2_F_Tref_350_3.csv │ │ ├── CLN025_SST2_F_Tref_350_3.xtc │ │ ├── CLN025_SST2_F_Tref_350_4.csv │ │ ├── CLN025_SST2_F_Tref_350_4.xtc │ │ ├── CLN025_SST2_U_1.csv │ │ ├── CLN025_SST2_U_1.pdb │ │ ├── CLN025_SST2_U_1.xtc │ │ ├── CLN025_SST2_U_2.csv │ │ ├── CLN025_SST2_U_2.xtc │ │ ├── CLN025_SST2_U_3.csv │ │ ├── CLN025_SST2_U_3.xtc │ │ ├── CLN025_SST2_U_4.csv │ │ └── CLN025_SST2_U_4.xtc │ └── ST │ ├── CLN025_ST_F_1.csv │ ├── CLN025_ST_F_1.xtc │ ├── CLN025_ST_F_2.csv │ ├── CLN025_ST_F_2.xtc │ ├── CLN025_ST_F_3.csv │ ├── CLN025_ST_F_3.xtc │ ├── CLN025_ST_F_4.csv │ ├── CLN025_ST_F_4.xtc │ ├── CLN025_ST_F.pdb │ ├── CLN025_ST_U_1.csv │ ├── CLN025_ST_U_1.xtc │ ├── CLN025_ST_U_2.csv │ ├── CLN025_ST_U_2.xtc │ ├── CLN025_ST_U_3.csv │ ├── CLN025_ST_U_3.xtc │ ├── CLN025_ST_U_4.csv │ ├── CLN025_ST_U_4.xtc │ └── CLN025_ST_U.pdb ├── PNGase_p97 │ ├── PNGase_p97_F_1.csv │ ├── PNGase_p97_F_1.xtc │ ├── PNGase_p97_F_2.csv │ ├── PNGase_p97_F_2.xtc │ ├── PNGase_p97_F_3.csv │ ├── PNGase_p97_F_3.xtc │ ├── PNGase_p97_F_4.csv │ ├── PNGase_p97_F_4.xtc │ ├── PNGase_p97_F.pdb │ ├── PNGase_p97_U_1.csv │ ├── PNGase_p97_U_1.xtc │ ├── PNGase_p97_U_2.csv │ ├── PNGase_p97_U_2.xtc │ ├── PNGase_p97_U_3.csv │ ├── PNGase_p97_U_3.xtc │ ├── PNGase_p97_U_4.csv │ ├── PNGase_p97_U_4.xtc │ └── PNGase_p97_U.pdb └── TrpCage ├── SST2 │ ├── TrpCage_SST2_F_1.csv │ ├── TrpCage_SST2_F_1.pdb │ ├── TrpCage_SST2_F_1.xtc │ ├── TrpCage_SST2_F_2.csv │ ├── TrpCage_SST2_F_2.xtc │ ├── TrpCage_SST2_F_3.csv │ ├── TrpCage_SST2_F_3.xtc │ ├── TrpCage_SST2_F_4.csv │ ├── TrpCage_SST2_F_4.xtc │ ├── TrpCage_SST2_F_Tref_350_1.csv │ ├── TrpCage_SST2_F_Tref_350_1.pdb │ ├── TrpCage_SST2_F_Tref_350_1.xtc │ ├── TrpCage_SST2_F_Tref_350_2.csv │ ├── TrpCage_SST2_F_Tref_350_2.xtc │ ├── TrpCage_SST2_F_Tref_350_3.csv │ ├── TrpCage_SST2_F_Tref_350_3.xtc │ ├── TrpCage_SST2_F_Tref_350_4.csv │ ├── TrpCage_SST2_F_Tref_350_4.xtc │ ├── TrpCage_SST2_U_1.csv │ ├── TrpCage_SST2_U_1.pdb │ ├── TrpCage_SST2_U_1.xtc │ ├── TrpCage_SST2_U_2.csv │ ├── TrpCage_SST2_U_2.xtc │ ├── TrpCage_SST2_U_3.csv │ ├── TrpCage_SST2_U_3.xtc │ ├── TrpCage_SST2_U_4.csv │ └── TrpCage_SST2_U_4.xtc └── ST ├── TrpCage_ST_F_1.csv ├── TrpCage_ST_F_1.xtc ├── TrpCage_ST_F_2.csv ├── TrpCage_ST_F_2.xtc ├── TrpCage_ST_F_3.csv ├── TrpCage_ST_F_3.xtc ├── TrpCage_ST_F_4.csv ├── TrpCage_ST_F_4.xtc ├── TrpCage_ST_F.pdb ├── TrpCage_ST_U_1.csv ├── TrpCage_ST_U_1.xtc ├── TrpCage_ST_U_2.csv ├── TrpCage_ST_U_2.xtc ├── TrpCage_ST_U_3.csv ├── TrpCage_ST_U_3.xtc ├── TrpCage_ST_U_4.csv ├── TrpCage_ST_U_4.xtc └── TrpCage_ST_U.pdb</code></pre>

Dataset of the research article "Choline Oxidase and Choline Ionic Liquids in Biocatalytic Heme Peroxidase Cascades"

The dataset contains primary data related to the article entitled "Choline Oxidase and Choline Ionic Liquids in Biocatalytic Heme Peroxidase Cascades", published in ChemCatChem. The dataset contains spreadsheet data detailing kinetic measurements and calculations.

openmit-licenseSep 2024View details →

Dataset for the numerical simulation in the article "Catalytically biased self-assembly by hybridization of reversibility and irreversibility in a reaction network"

This dataset includes the essential source code and the corresponding numerical data for the self-assembly of a M6L4 square-based pyramid (SP) complex.  The associated study is described in  "Catalytically biased self-assembly by hybridization of reversibility and irreversibility in a reaction network", by T. Abe, S. Takahashi, H. Sato, and S. Hiraoka.

opencc-by-4.0Jul 2024View details →

Data described in the article "Three-step enzymatic remodeling of chitin into bioactive chitooligomers"

The Supporting Information file contains the following data: PCR primers and reaction conditions for the generation of TfChit mutants; HPLC chromatogram of chitin hydrolysate; detailed sequence alignment; computational data; additional figures for modeling of TfChit; MALDI-TOF MS analysis of a mixture of insoluble chitooligomers prepared by Y445N AoHex, and m/z values monitored by HPLC-MS of the deacetylation reactions.

Supplementary data for the article Fichtner et al., 2024, A&A, 690, A72

Supplementary data for the paper: Fichtner Y. A., Mackey J., Grassitelli L., Romano-Díaz E., Porciani C., 2024, A&A, 690, A72, Connecting stellar and galactic scales: Energetic feedback from stellar wind bubbles to supernova remnants, DOI: https://doi.org/10.1051/0004-6361/202449638  Paper abstract: Context. Energy and momentum feedback from stars is a key element of models for galaxy formation and interstellar medium dynamics, but resolving the relevant length scales to directly include this feedback remain out of reach of current-generation simulations. Aims. We aim to constrain the energy feedback by winds, photoionisation and supernovae (SNe) from massive stars. Methods. We measure the thermal and kinetic energy imparted to the interstellar medium on various length scales, calculated from high-resolution 1D radiation-hydrodynamics simulations. Our grid of simulations covers a broad range of densities, metallicities, and state-of-the-art evolutionary models of single and binary stars. Results. A single star or binary system can carve a cavity of tens-of-pc size into the surrounding medium. During the pre-SN phase, post-main-sequence stellar winds and photoionisation dominate. While SN explosions dominate the total energy budget, the pre-SN feedback is of great importance by reducing the circumstellar gas density and delaying the onset of radiative losses in the SN remnant. Contrary to expectations, the metallicity dependence of the stellar wind has little effect on the cumulative energy imparted by feedback to the ISM; the only requirement is the existence of a sufficient level of pre-SN radiative and mechanical feedback. The ambient medium density determines how much and when feedback energy reaches to distance >= 10 − 20 pc and affects the division between kinetic and thermal feedback. Conclusions. Our results can be used as a sub-grid model for feedback in large-scale simulations of galaxies. The results reinforce that the uncertain mapping of stellar evolution sequences to SN explosion energy is very important to determining the overall feedback energy from a stellar population. General description of the data release: The data release contains tables with the cumulative flows of kinetic energy, thermal energy, mass and metal mass as a function of time for every simulation presented in the corresponding paper. The cumulative flows are calculated at three different distances from the stellar source (5, 10 and 20 pc). Exemplary parameter files of the simulations and readme files are also included. The tables could be used in a sub-grid model of stellar feedback to determine the amount of feedback ejecta (mass, metals, thermal and kinetic energy) that can be injected, taking into account the circumstellar medium. It is possible to do so as a function of time, ambient metallicity and ambient density. The data release includes simulations of different stellar models of single and binary stars, making it possible to choose between different stellar populations. It is also possible to use the data for a comparison to other work.

Data supporting the article: "Comparative analysis of fecal microbiota between adolescents with early-onset psychosis and adults with schizophrenia"

This dataset supports the article titled "Comparative analysis of fecal microbiota between adolescents with early-onset psychosis and adults with schizophrenia.", available at <a href="https://doi.org/10.3390/microorganisms12102071">https://doi.org/10.3390/microorganisms12102071</a>. The dataset includes fecal microbiota sequencing data from adolescent patients with early-onset psychosis, adult patients with schizophrenia, and non-psychotic controls. The data were generated using 16S rRNA gene sequencing and analyzed with QIIME2 and PICRUSt2 to assess microbial diversity and functional pathways. Variables such as age, diagnosis, and medication use are included. The dataset contains: <ul> <li>Processed results from fecal microbiota analysis (OTUs and taxonomic classifications)</li> <li>Metadata associated with each sample (age, diagnosis, medication)</li> <li>Results from diversity analysis (alpha and beta diversity metrics)</li> <li>Functional analysis of microbial pathways (PICRUSt2)</li> </ul> These data are essential for reproducing the findings discussed in the article. Note that the raw sequencing data are available upon request.

opencc-by-4.0Jul 2024View details →

Dataset for the article "Assessing the Partial Hessian Approximation in QM/MM-based Vibrational Analysis"

This dataset contains the raw data and Jupyter notebooks used in the article "Assessing the Partial Hessian Approximation in QM/MM-based Vibrational Analysis."

Data collection of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"

This dataset contains the definition and name of the data used in the study. It also contains rows of data for all flood parameters applied to the creation of flood vulnerability maps, namely rainfall data, landsat-8 files, DEM, DSMW and drainage survey data.

Data testing of article research tittle "Online GIS and Remote Sensing-Based Mapping of Flood Vulnerability in Samarinda Seberang Subdistrict"

This dataset explains validation testing in a study of the Samarinda Seberang flood vulnerability map. There are two test methods, namely the Kappa accuracy test and the 3D simulation visualization test. The Kappa accuracy test tab displays a table of Kappa calculation results, and the second tab contains a 3D simulation scenario image.

Data and codes for the article "Quantum collective motion of macroscopic mechanical oscillators"

Open the record for dataset details and reuse information.