Find research datasets worth reusing

opencc-by-4.0Mar 2021View details →

NewsEye / READ AS training dataset from French Newspapers (19th, early 20th C.)

The dataset comprises French newspaper pages from 19th and early 20th century with annotated text. The page images were provided by the <a href="https://www.bnf.fr/en">French National Library</a> and comprise 183 pages (training set). The data are formed according to the PAGE format (cf. Cf. <a href="https://github.com/PRImA-Research-Lab/PAGE-XML/">https://github.com/PRImA-Research-Lab/PAGE-XML/</a>) and were produced with the <a href="http://read.transkribus.eu/">Transkribus </a>platform with support of the <a href="http://newseye.eu/">NewsEye</a> and the <a href="http://read.transkribus.eu/">READ </a>project. The guidelines with which the AS GT was created are uploaded here as well.

Percentage of Population Who Read Books In European Regions

The indicator is created from the Eurobarometer 79.2 survey’s <a href="https://search.gesis.org/research_data/ZA5688">GESIS datafile</a> using regional subsamples. The regional subsamples were recoded to the NUTS 2016 regional boundary definitions with the <a href="https://regions.dataobservatory.eu/">regions</a> R package. In the larger countries, where only NUTS1 level information was present (for example, in Germany and the United Kingdom), we imputed the NUTS1 territorial average values to the constituent NUTS2 regions. A ‘dirty averaging’ was used to create regional averages, with scale national post-stratification weights to an expected value of 1. Al respondents who read at least one book in the previous 12 months were coded to have read a book. This indicator was used in the Balázs Bodó, Dániel Antal, Zoltán Puha: <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242509">Can scholarly pirate libraries bridge the knowledge access gap?</a> An empirical study on the structural conditions of book piracy in global and European academia, in Plos ONE (Published: December 3, 2020.)

Formatted TCGA clinical and RNA-Seq data for colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ)

COAD/READ/COADREAD_rnaseq_fpkm.txt files contain TCGA RNA-Seq data in FPKM normalisation for colorectal adenocarcinoma (COAD), rectum adenocarcinoma (READ) or combined (COADREAD). COAD/READ/COADREAD_rnaseq_tpm.txt files contain TCGA RNA-Seq data in TPM normalisation for colorectal adenocarcinoma (COAD), rectum adenocarcinoma (READ) or combined (COADREAD). COAD/READ/COADREAD_clinical_raw.xlsx files contain TCGA clinical data for patients with colorectal adenocarcinoma (COAD), rectum adenocarcinoma (READ) or combined (COADREAD). COAD/READ/COADREAD_rnaseq_clinical_raw.xlsx files contain corresponding information of TCGA clinical data and RNA-Seq data for patients with colorectal adenocarcinoma (COAD), rectum adenocarcinoma (READ) or combined (COADREAD).

Simulated NGS read datasets for prediction of novel fungal pathogens and multiple pathogen classes

This repository contains simulated Illumina read datasets for novel fungal pathogen prediction and real-time detection of multiple pathogen classes. They were used to train the models hosted at <a href="https://zenodo.org/record/5711877">https://zenodo.org/record/5711877</a>. The reads were simulated with Mason (<a href="https://www.seqan.de/apps/mason/">https://www.seqan.de/apps/mason/</a>) from genomes downloaded from NCBI, based on metadata stored in a manually curated database (<a href="https://zenodo.org/record/5846345">https://zenodo.org/record/5846345</a>). We provide the following: 1) An rds file describing assignment of fungal species from the database (<a href="https://zenodo.org/record/5846345">https://zenodo.org/record/5846345</a>) to training, validation and test sets (TrainValTest_fungi.rds). A second rds file (TrainValTest_temporal.rds) includes species added within 12 weeks after the original datasets were compiled. Those species were used for a temporal benchmark. 2) Fungal validation and test sets. Each contains 1.25 million, 250bp-long reads simulated from non-overlapping sets of human ("pathogenic") or non-human ("nonpathogenic") pathogens. The test set contains paired reads ("_1" and "_2" for the first and second mate). The number of reads per species is proportional to the respective genome length. An additional, temporal test set (*temporal*fasta.gz) includes 15 species added after 12 weeks from the consturction of the original datasets. 3) Fungal training sets. They contain 250bp-long reads simulated from species not present in the validation or test sets. There are four variants: 3a) "low-coverage, linear" - 20 million reads, number of reads per species proportional to genome length 3b) "low-coverage, logarithmic" - 20 million reads, number of reads per species proportional to the logarithm of genome length ("log") 3c) "high-coverage, linear" - 240 million reads, number of reads per species proportional to genome length  ("24") 3d) "high-coverage, logarithmic" - 240 million reads, number of reads per species proportional to the logarithm of genome length  ("24log") 4) Training, validation and test sets for the multiclass models. They should be used together with the "pathogenic" read sets hosted at <a href="https://zenodo.org/record/4456857">https://zenodo.org/record/4456857</a>. Here, we share sets for two of the four total classes: 4a) The 'non-pathogen' class is a mixture of "nonpathogenic" biacterial and viral read sets, concatenated and downsampled to the original read number (20M for training, 1.25M for validation and test). The training and validation sets contain mixed-length (25-20bp) simulated subreads (original sets hosted here: <a href="https://zenodo.org/record/4456857">https://zenodo.org/record/4456857</a>). The test set contains 250bp long reads based on the test sets from here: <a href="https://zenodo.org/record/3678563">https://zenodo.org/record/3678563</a> and here: <a href="https://zenodo.org/record/4312525">https://zenodo.org/record/4312525</a>; it was also sorted by species. 4b) Mixed-length versions of the "pathogenic" fungal training and validation sets, prepared by random shortening of the "low-coverage" read sets in the "linear" (_rn_) and "logarithmic" (_rn_*log_) flavours. See also the preprint: <a href="https://www.biorxiv.org/content/10.1101/2021.11.30.470625">https://www.biorxiv.org/content/10.1101/2021.11.30.470625</a>

opencc-by-4.0May 2022View details →

Machine learning classifiers for species classification of fungi using error-prone long-reads on extended metabarcodes

Machine learning models used in the decision tree of linked machine learning models (<a href="https://github.com/teenjes/fungal_ML">https://github.com/teenjes/fungal_ML</a>)

opencc-by-4.0May 2022View details →

HBV-only reads from cultured human hepatocytes infected with HBV used for testing HBVouroboros functionalities.

The dataset consists of bulk-RNA reads extracted from cultured human hepatocytes infected with HBV. The data is used to test the performance of the HBVouroboros software (https://github.com/bedapub/HBVouroboros). Note that fastq files are comprised of reads that map to HBV genome, as such sample files for negative controls of HBV infection are empty files. We include these for the sake of completeness and to reflect the experimental design.

opencc-by-4.0Apr 2018View details →

READ ABP Table datasets

Datasets used in the publication : Comparing Machine Learning Approaches for Table Recognition in Historical Register Books, Hervé Déjean, Jean-Luc Meunier, Stéphane Clinchant, Eva Maria Lang and Florian Kleber, 13TH IAPR INTERNATIONAL WORKSHOP ON DOCUMENT ANALYSIS SYSTEMS DAS 2018; Vienna, Austria CHANGES: 07/05/2018: second version: dataset150 images were missing dataset111     img/  images     xml/ READ pagexml with BIESO annotation dataset150     img/  images     GT_xml: READ pagexml with BIESO annotation     WK_xml: workflow version: pagexml corresponding to the workflow outputs (textlines are automatically recognised, columns as well)     ROWREF: GT for the row regions Tagset (attribute of the TextLine element) Type: deprecated DU_row:     B: first element of cell     I: inside a cell     E: last element of a cell     S: single element of  a cell     O: outside the table

opencc-by-4.0Sep 2018View details →

Dataset for ICFHR2018 Competition on Automated Text Recognition on a READ Dataset

The main idea of this dataset is to analyse the impact of training data. How many training data specific to the document, you are transcribing, is necessary?    general data: This is a collection of heterogeneous documents to train an initial system. For each text line there is an image file of that line, a file with the ground truth text and an information file containing an automatically generated surrounding polygon. specific data: The specific data contains documents related to the test data. For the specific systems only the images of the train list may be used. The file are of the same type as the general data. test data: The test data contains only the images and the information files. More Information, some published results and an evaluation procedure at https://scriptnet.iit.demokritos.gr/competitions/10/

opencc-by-4.0Feb 2019View details →

Edward FitzGerald Life and Letters – Analysis of writing and reading

These files form part of an archive of research material relating to the Life and Letters of Edward FitzGerald.  The data have been compiled by independent researchers W H (Bill) Martin and Sandra Mason;  their contact details are below.  The files comprise a number of searchable listings of information contained in FitzGerald’s letters.  The information formed input to a book on Edward FitzGerald which is referenced below. This section of the archive contains a database, efgliterature, which analyses the letters in terms of their comments on FitzGerald’s writings and the books and other material that he read.  It also shows the dating of the letters, the people to whom FitzGerald wrote, and his location at the time of writing.  The letters are those contained in the collection published by A M Terhune and A B Terhune in 1980 – see reference below. The database contains all the letters published by the Terhunes, including some which have no literary references.  An  explanatory README text file contains a table showing the fields included in the database and giving definitions of them and of the codings used where relevant.

opencc-by-4.0Sep 2024View details →

The North Pacific Eukaryotic Gene Catalog: clustered nucleotide metatranscripts and read counts

This data continues with the development of the NPEGC Trinity de novo metatranscriptome assemblies from the protein data repository of <a href="../doi/10.5281/zenodo.10472589">The North Pacific Eukaryotic Gene Catalog</a>. The nucleotide sequences corresponding to the NPEGC cluster representatives are collected together in these repository files: NPac.G1PA.bf100.id99.nt.fasta.gz NPac.G2PA.bf100.id99.nt.fasta.gz NPac.G3PA.bf100.id99.nt.fasta.gz NPac.G3PA_diel.bf100.id99.nt.fasta.gz NPac.D1PA.bf100.id99.nt.fasta.gz A full description of this data is published in Scientific Data, available here: <a href="https://www.nature.com/articles/s41597-024-04005-5" target="_blank" rel="noopener">The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations</a>. Please cite this publication if your research uses this data: Groussman, R. D., Coesel, S. N., Durham, B. P., Schatz, M. J., & Armbrust, E. V. (2024). The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations. Scientific Data, 11(1), 1161. These nucleotide sequences have been sourced from the Zenodo repository for raw assemblies: <a href="../records/7332796">The North Pacific Eukaryotic Gene Catalog: Raw assemblies from Gradients 1, 2 and 3</a> Key processing steps are sampled below with links to the detailed code on the main github code repository: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog">https://github.com/armbrustlab/NPac_euk_gene_catalog</a> Code used to build the kallisto indices and map the short reads against indices with kallisto are online in the code repository here: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/nt_data/NPEGC.nt_kallisto_counts.sh">NPEGC.nt_kallisto_counts.sh</a> There are two main steps: 1. Generate the kallisto index on the sets of clustered nucleotide metatranscripts 2. Map the short reads from environmental samples back to the assembly index As generated above, kallisto generates separate results files for each of the sample files. Even after compression, the total size of the tarballed kallisto output results directories are prohibitively large (>50GB). We use the code in this template R script to join together the 'est_count' estimated count values for the tens of millions of protein sequences in each project metatranscriptome, along with length. The code in this template script was used for each project: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/nt_data/aggregate_kallisto_counts.R">aggregate_kallisto_counts.R</a> The output count files for each project are Gzip-compressed and uploaded to the NPEGC nucleotide data repository here:  G1PA.raw.est_counts.csv.gz G2PA.raw.est_counts.csv.gz G3PA.raw.est_counts.csv.gz G3PA_diel.raw.est_counts.csv.gz D1PA.raw.est_counts.csv.gz

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

opencc-by-4.0Aug 2024View details →

Recreational Reading as a Leisure Activity: Perspectives from Georgian and Finnish Hearing and deaf students

Using a mixed methods approach, this study investigates Finnish and Georgian deaf students' attitudes towards leisure reading and the relationship between their reading intentions and actual reading. The primary interest of the study is to identify the external factors that affect reading behaviour directly or indirectly.  An Integrative Model of Planned Behaviour is used as a theoretical framework, where intention is considered the main determinant for the behaviour.  The study is cross-cultural, bringing the experiences of two distinct countries together.  Reading attitude is investigated quantitatively, using a 5-point Likert Scale survey. 29 statements are broken down into three variables: enjoyment ( statements 4-13), difficulty ( statements 14-22) and social reinforcement (statements 23-29).  Follow-up focus group interviews investigated the perceived external factors that affect reading behaviour negatively. interview questions were also grouped under three categories: 1) General feelings about reading (experiential attitude or affect), 2) Influences from individuals, close networks or society (perceived norms)  and situational or environmental restrictions affect behaviour performance (skills, knowledge, resources, other factors).   The interview protocol, questions survey and row data are attached as files. Interview transcripts are saved in Finnish and Georgian languages and can be shared only upon a reasonable request.

opencc-by-4.0Jun 2022View details →

MALDI MS Data and Metadata from "A biological reading of a palimpsest"

Spectra in mzML format along with the metadata associated with it: <ul> <li>The mzML file names follow the following format UoCXX_Y.mzML, where UoCXX is the sample name and Y is the replicate number (1, 2 or 3)</li> <li>uoc_metadata.csv file contains species, book and quire number associated with each spectra file. It is used in the data analysis in <a href="https://doi.org/10.5281/zenodo.7406297">doi.org/10.5281/zenodo.7406297</a></li> <li>Dataset S1.xlsx contains extended metadata with the results of the visual analysis of the parchment.</li> </ul>

opencc-by-4.0May 2023View details →

EyeLink 1000 raw eye tracking data - Reading numbers is harder than reading words: An eye-tracking study

Reading Arabic numerals is a fundamentally different activity compared to word reading. This study aimed to investigate the eye movements of normal-reading adults when reading aloud short and long Arabic numerals (with or without a thousand separator) compared to matched-in-length words and pseudowords. This dataset contains the raw data of the article "Reading numbers is harder than reading words: An eye-tracking study" published in the journal Acta Psychologica (https://doi.org/10.1016/j.actpsy.2023.103942)

opencc-by-4.0Jul 2023View details →

Reads-per-UMI tables across single-cell RNA sequencing protocols

Data analyzed in <a href="https://www.biorxiv.org/content/10.1101/2023.08.02.551637v1">Lause, Ziegenhain et al. (2023)</a>. Code to obtain these tables from public data sources is available on <a href="https://github.com/berenslab/read-normalization">github</a>.   Each row in the table is a UMI-tag detected in a certain cell (column RG) attached to a molecule from a specific gene (column GE) with a certain barcode (column UB). Column N gives the number of times the UMI was detected for that gene and cell. Data sources and protocols are given with the respective file names below. Johnsson2022_Smartseq3_PE.hd1.txt.gz: Mouse fibroblasts profiled with Smart-seq3 paired-end; accession E-MTAB-10148, sample plate2, <a href="https://doi.org/10.1038/s41588-022-01014-1">Paper</a> Hagemann-Jensen2020_Smartseq3_SE.hd1.txt.gz: Mouse fibroblasts profiled with Smart-seq3 single-end; accession E-MTAB-8735, sample Smartseq3.Fibroblasts.smFISH <a href="https://doi.org/10.1038/s41587-020-0497-0">Paper</a> Hagemann-Jensen2022_Smartseq3xpress.hd1.txt.gz: HEK293 cells profiled with Smart-seq3Xpress; accession E-MTAB-11467. <a href="https://www.biorxiv.org/content/10.1101/2021.07.10.451889v1">Paper</a> Ziegenhain2017.hd1.txt.gz: Mouse embryonic stem cells profiled by CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq; GEO accession GSE75790 <a href="https://doi.org/10.1016/j.molcel.2017.01.023">Paper</a>

openOpenApr 2016View details →

Ground Water Depth Readings at BCEF/LTER Floodplain Sites, 1991-Present: Weekly

Ground water depth readings were collected at BCEF/LTER floodplain sites beginning in 1991. Wells were visited and measured weekly during the growing season.

openOpenFeb 2012View details →

Ground Water Depth Readings at BCEF/LTER Floodplain Sites, 1991-Present: Hourly

Campbell CR7 dataloggers were used at FP1A, FP2A, and FP4A to measure ground water and river levels (river levels only at FP4A) during the summer growing season. Each site has four wells being monitored and any river levels are in addition to these wells. A Druk Inc. sensor (model PDRC 10/D) was placed at the bottom of the 3 meter by 15 cm PVC well tubes and calibrated to output depth from forest floor to groundwater. Sensors are scanned every 5 minutes and hourly averages are output to a storage module.

openCC (other)Apr 2024View details →

Catalog of NCBI sequence read archive (SRA) data for salamanders at the Hubbard Brook Experimental Forest 2012-2021

This project was designed to describe fine-scale population genetic differentiation of the stream salamander Gryinophilus porphyriticus among five study streams in the Hubbard Brook Experimental Forest. The data are paired with intensive capture-recapture data to assess direct fitness effects of individual genetic diversity, including effects of individual multilocus heterozygosity on stage-specific survival probabilities. This dataset publishes a manifest of the genomic sequence reads submitted to the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). These samples are published at NCBI under the BioProject ID 1090913 (https://www.ncbi.nlm.nih.gov/bioproject/1090913). The tables here include sample metadata and the NCBI URLs to each sample. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.

openCustomJan 2020View details →

Year 2001, 15 minute measurements of stage, water temperature, conductivity, dissolved oxygen, and pH on the Ipswich R. mainstem at North Reading, just upstream of Rt. 28 (~48 km2 drainage area).

Year 2001, continuous measurements, every 15 minutes, were made of stage, water temperature, conductivity, dissolved oxygen, and pH in a small headwater stream draining a moderate sized, mixed land use watershed. Measurements are for part of the ice free season (June – December). Discharge is determined from stage using discharge vs stage regressions. Several day gaps occur periodically due to removal for recalibration.