Find research datasets worth reusing

Regulation of mature mRNA levels by RNA processing efficiency

Data from the research paper "Regulation of mature mRNA levels by RNA processing efficiency" by Henfrey, C., Murphy, S., and Tellier, M.: -Highest expressed transcript annotation for protein-coding genes, gencode v38. -15 txt (BED) files for chromatin vs nucleoplasm enrichment gene sets: HeLa full gene sets, canonical protein only sets, chromatin RNA seq subsamples, mNET seq subsamples, Raji gene sets. -Proteomics data table -mRNA half-life table -Splicing efficiency for POINT-seq, ChrRNA-seq, NucRNA-seq table -Ser2-P mNET-seq readthrough index data table -Splicing efficiency for siLuc/siEX3 table (ChrRNA-seq, NucRNA-seq) -RMATs output tables for alternative splicing results (siEX3 vs siLuc) -Tables for TSS:TES quantifications (mNET-seq(CTD) vs log2FoldChange, chr/nuc/mnet siEX3 vs siLuc)

opencc-by-4.0Apr 2023View details →

Friction extrusion processing of aluminum powders: microstructure homogeneity and mechanical properties

This dataset contains the data from the publication "Friction extrusion processing of aluminum powders: microstructure homogeneity and mechanical properties"

Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)

The dataset contains: i. the meteorological forcing, hydrological boundary condition and chlorophyll-a files used as an input ii. the model output and skill produced for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 2)

Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 1)

Input and output data for Experiment B2 (Deliverable 3.3 - Data assimilation in process-based models for algae bloom forecasting - Section 3)

Simulation data for Doubly Robust Estimation of Business Process Intervention

Event logs of simulated execution of two variants of the same process. A case matrix that contain the outcome, the intervention, and the confounders.

The image processing of Milani: challenges after DART impact

This dataset contain data for the paper The image processing of Milani challenges after DART impact, presented at ESA-GNC Sopot, Poland, June 2023  The .zip folder Dataset is made of 4 subfolders      -CoefficientsPCE With the PCEFull9.mat file with all the coefficients of the aPC basis and PCE listed in the paper          -TestResults With the ResultsAll.mat file that contains the predicted and true values of phase angle over the test set illustrated in the paper and the MakePlot.m script in Matlab. You can use this data to compare your method directly with the results we have obtained in this work. The script generate a simple histogram plot and compute the mean, std, Q67, and Q95 values.      -D1_with_sz_1p00s0ImagesDataset directory within this folder contains all data obtaind during the rendering phase of the 5k samples of Didymos using the polar scale of the Didymos Reference model by ESA (old values before impact).     -D1_with_sz_0p78s0 ImagesDataset directory within this folder contains all data obtaind during the rendering phase of the 5k samples of Didymos using the polar scale of the Didymos Reference model by JHUAPL (updated values after impact). This directory also contains the pre-processed input-output pairs for the WCOB, NN, and PCE (X_features, Y) and CELM, and CNN (X_images, Y) methods.  You can either decide to use the same pre-processed data we have used for training, validation, and testing or you can work with the raw data (the one from the ImagesDataset folder) to generate your own dataset.  Feel free to drop a line vie email in case you need clarification at mattia.pugliatti@polimi.it or pugliatti.mattia@gmail.com

CMIP5 and CMIP6 post-processed AMOC and MLD data supporting Jesse et al. 2023

These are the datasets supporting the paper "Why is CMIP6 projecting larger ocean dynamic sea level in the North Sea than CMIP5?" from Jesse et al. submitted to ERL. See the paper for more information about the data and GitHub for the code that generated the data: https://github.com/dlebars/CMIP_SeaLevel The Atlantic meridional overturning circulation (AMOC) is computed at two latitudes 26N and 35N from different CMIP variables: cmip5_amoc is computed from the variable "msftmyz". cmip6_amoc is computed from the variable "msftmz" or "msftyz" as indicated in the name of the file. cmip5_amoc_vo and cmip6_amoc_vo are computed from the meridional ocean velocity ("vo"). For mixed layer depth the variable "mlotst" is used.

Dataset for Process design within planetary boundaries: Application to CO2 based methanol production

Dataset for the journal article: Process design within planetary boundaries: Application to CO2 based methanol production

Pre-processed and modeled GNSS time-series after the 2011 Tohoku Earthquake

The raw, pre-processed, and modeled GNSS time-series of the 213 GEONET sites in the Tohoku region, Japan, from Mar. 12, 2011 to Nov. 20, 2021, relative to the Okhotsk plate (Argus et al., 2011, Geochemistry, Geophysics, Geosystems). The original GNSS time-series are F5 solutions, which are distributed by Geospatial Information Authority of Japan (GSI, https://www.gsi.go.jp/). The details and availability of F5 solutions are written in Takamatsu et al. (2023, Earth, Planets, and Space) https://doi.org/10.1186/s40623-023-01787-7. The GNSS time-series processing was performed by Tomita (submitted), and the following signals were excluded from the raw time-series: seasonal variation, coseismic step, antenna maintenance offset, and common mode errors. Then, the pre-processed time-series were modeled by a trajectory modeling method considering postseismic deformation of the 2011 Tohoku earthquake, the Boso SSEs, and postseismic deformations due to aftershocks and L-ASE (long-term aseismic slip event) since late 2019. "sitelist.txt" - Site information file column 1: Full site ID column 2: 4digits site ID column 3: Longitude [deg] column 4: Latitude [deg] column 5: Height [m]  "pre-process/xxxx.txt" - Time-series at xxxx (4digits site ID) site column 1: days from Mar. 12, 2011 (1 corresponds to Mar. 12, 2011) column 2: raw East-West displacement [m] column 3: raw North-South displacement [m] column 4: raw Up-down displacement [m] column 5: pre-processed East-West displacement [m] column 6: pre-processed North-South displacement [m] column 7: pre-processed Up-down displacement [m] "model/xxxx/prediction_yy.txt" - Time-series for yy component (yy=EW, NS, UD) at xxxx (4digits site ID) site column 1: days from Mar. 12, 2011 (1 corresponds to Mar. 12, 2011) column 2: modeled displacement excluding the Boso SSEs [m] column 3: modeled displacement excluding the Boso SSEs and postseismic deformation due to aftershocks caused one year after the 2011 Tohoku Eq. [m] column 4: modeled displacement excluding the Boso SSEs, postseismic deformation due to aftershocks caused one year after the 2011 Tohoku Eq. and the 2019 L-ASE [m] The displacement on Mar. 12, 2011 was initially set to be zero before the pre-processing, but the removal of the above factors provided some deviation from zero. The raw time-series excluded outliers from the original F5 solutions, and the raw time-series were transformed into the Okhotsk plate reference. Following the above trajectory modeling, the fully-relaxed postseismic displacement fields due to 2015 Feb. 17 Sanriku-oki earthquake ("Table_displacement1.xlsx"), the 2015 May 13 Miyagi-oki earthquake ("Table_displacement2.xlsx"), and summation of the 2021 Feb. 13 Fukushima-oki, the 2021 Mar. 20 Miyagi-oki, and the 2021 May 1 earthquakes ("Table_displacement2.xlsx") were calculated. Moreover, the cumulative displacement field due to the 2019 L-ASE since Nov. 25, 2019 was also calculated. In those files, the estimation errors are also shown as 1σ standard deviation obtained from diagonal components of the model covariance matrices.    The details of these data are introduced in the corresponding paper (Tomita, submitted).

Processed data supporting figures in Yang et al. 2023: Oceanic eddies induce a rapid formation of an internal wave continuum

This data repository supports a manuscript by Luwei Yang, Roy Barkan, Kaushik Srinivasan, James C. McWilliams, Callum J. Shakespeare, and Angus H. Gibson, submitted to Communications Earth & Environment. This repository contains the processed data that support the figures in the manuscript.

Cold rolling mill: Dataset for Recommender system for process optimization

The dataset (pickle formatted with version pickle=4.0) contains a collection of process values obtained from a process line. Each value represents the mean measurement within a window at specific intervals along the distance domain. These intervals were equidistant and sampled under steady-state conditions, ensuring consistent data collection. The process values included in the dataset cover a range of parameters and variables relevant to the process line. These values provide information about the behavior and characteristics of the process at different points along the distance domain.   Associate source code is available at: <a href="https://github.com/CuAuPro/opti-rec-sys">Recommender system for process optimization (github.com)</a>.

Process Planning with Uncertain Process Time

The generated data represents instances for a project, with variations in the number of items, stations, workers, tasks, and other parameters. Each instance file contains information such as the number of items, stations, workers, tasks, and the Takt time used. Additionally, it includes details about the mean and standard deviation of process times for tasks, equipment costs, and precedence relations between tasks. These instance files can be used as input for various analyses or simulations related to the project, providing a diverse range of scenarios to explore and evaluate. <<This data is generated for the paper under review with the tile: "Mixed-Model Assembly Lines with Process Time Uncertainty>>

Difference and number of works published over the years grouped by the objective of the generative process

Difference and number of works published over the years grouped by the objective of the generative process. Part of the study "What do we mean by GenAI?"

Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes

Spatial data sets of the paper Heinrich Stadial 1 continental sand dunes and Middle to Late Holocene paleosol sequences in SE Iberia: implications for human occupation and site formation processes. This data set is composed by 3 shapefiles: <ol> <li>Dune_field: Feature class polygon shapefile geometry representing the individual dunes identified in the Villena dune field.</li> <li>Sampled dunes: Shapefile of point geometry representing the location of the stratigraphic sequences of CC1, CC2 and CC3 sampled for texture, soil chemistry, OSL and radiocarbon dating. </li> <li>Sediment sourcing samples: Shapefile of point geometry representing the location of the reference samples of El Moron, El Arenal de la Virgen and Sierra del Castellar. </li> </ol> The spatial reference system is EPSG 25830.

Processed snRNA-seq data from "Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation"

Processed snRNA-seq data from "Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation". All nuclei passed QC and were corrected for background noise using cellBender. Files are in R objects saved in RDS (R Data Serialization) format. This repo contains one Seurat v4 object and one gene-by-cell raw RNA count matrix in sparse matrix format (dgCMatrix).

Processed metabolomic data from the EXPOsOMICS Personal Exposure Monitoring study

Metabolomic data from the 'Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study' paper <a href="https://doi.org/10.1021/acs.est.3c03233">DOI: 10.1021/acs.est.3c03233</a> .  The data was originally collected and generated by the multicenter EXPOsOMICS Personal Exposure Monitoring study. Details on data collection and processing are described in the aforementioned paper. The statistical analysis from that paper is available at <a href="https://github.com/moosterwegel/variability-metabolites-paper">https://github.com/moosterwegel/variability-metabolites-paper</a> and may contain useful information/code to work with this data. `processed_covariate_data.csv`: ``` Rows: 298 Columns: 7 $ subjectid: hashed identifier subject $ sample_code: indicates if it's the first (A) or second (B) blood sample $ centre: indicates in which centre the data was collected $ age_cat: indicates age category at the time of a PEM session $ sq_sex:  indicates the sex of the participant (male, female) as filled in during the screening questionaire $ traf: indicates the exposure to traffic (PM2.5 and UFP) as measured during the PEM sessions.  $ bmi_cat: indicates BMI category at the time of a PEM session ``` `processed_lcms_data data.csv` contains the processed LCMS data: ``` Rows: 298 Columns: 4297 $ subjectid: hashed identifier subject $ sample_code: indicates if it's the first (A) or second (B) blood sample $ centre: indicates in which centre the data was collected $ compounds: measured features (compounds) are prefixed by the letter X. The name contains information on the measured monoisotopicmass_retentiontime. Non-detects (below limit of detection (LOD) are coded as 1 for the compounds. .... ``` In the datasets each row indicates a measurement on a day (`sample_code`) and person (`subjectid`). The datasets can be joined on these variables. The other data files (`annotations.xslx`, `ancestors_annotations.xlsx`, `annotations_plus_kegg_pathways.csv`) contain the annotations, ancestors of the annotations (to assign a class to a compound based on ChEBI ontology, see our paper for details), annotations plus KEGG pathways respectively.

opencc-by-4.0Aug 2023View details →

Dataset for publication "Influence of precursor morphology and cathode processing on performance and cycle life of sodium-zinc chloride (Na-ZnCl2) battery cells"

High-temperature sodium-metal battery; sodium-metal halide battery (ZEBRA); molten-salt battery; zinc battery for stationary energy storage; alkali metal anode. Datasets used in the above manuscript.

opencc-by-4.0Dec 2022View details →

Characterization of dairy processing waste in the north of France

Dataset containing the characterization of dairy processing waste from 10 different dairy factories in the north of France.