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Dataset results
11 results for “The Carpentries”
How green is my valley? Measuring open access friendliness of Indian Institutes of Technology (IITs) through data carpentry (dataset)
<p>This data set is related to the book chapter with the following bibliographic details - Mukhopadhyay, P. (2022). How green is my valley? Measuring open access friendliness of Indian Institutes of Technology (IITs) through data<br> carpentry. In A. Biswas & M. Das Biswas (Eds.), Panorama of open access: Progress, practices & prospects (1st ed., pp. 67–89). Ess Ess. https://doi.org/10.5281/zenodo.6511080.</p> <p>It includes the truncated version of the final data set that has been used for analyzing Open Access Friendliness (OAF) of the Indian Institutes of Technology (IITs). The zipped version of the data set is around 95 MB (465 MB after decompress).</p>
Genome Mining for Data Carpentry lesson
<p>Data for Software Carpentry Genome Mining lesson. These data are part of the study<em> "Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: Implications for the microbial pan-genome" </em>(Hervé Tettelin et al 2005)</p>
Dataset for Medical Image Processing in Python Carpentries lesson
<p>This dataset contains a collection of medical imaging files for use in the <a href="https://github.com/esciencecenter-digital-skills/medical-image-processing">"Medical Image Processing with Python" lesson</a>, originally developed by the <a href="https://www.esciencecenter.nl/">Netherlands eScience Center</a>. </p> <p>The dataset includes:</p> <ol> <li>SimpleITK compatible files: MRI T1 and CT scans (<em>training_001_mr_T1.mha, training_001_ct.mha</em>), digital X-ray (<em>digital_xray.dcm</em> in DICOM format), neuroimaging data (<em>A1_grayT1.nrrd, A1_grayT2.nrrd</em>). Data have been downloaded from <a href="https://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/Python_html/00_Setup.html">here</a>. </li> <li>MRI data: a T2-weighted image (<em>OBJECT_phantom_T2W_TSE_Cor_14_1.nii</em> in NIfTI-1 format). Data have been downloaded from <a href="https://zenodo.org/records/6467772">here</a>. </li> <li>Example images for the machine learning lesson: chest X-rays (<em>rotatechest.png, other_op.png</em>), cardiomegaly example (<em>cardiomegaly_cc0.png</em>).</li> <li>Array data: Array data for the Intro to Medical Imaging lesson. Numpy arrays were created by processing and manipulation of publicly available data i.e. from <a href="https://doi.org/10.1109/TNS.1974.6499235">the Schepp Logan phantom</a> and from the <a href="https://fastmri.med.nyu.edu/">NYU FastMRI dataset</a> <div> </div> </li> <li>Additional data: to be added</li> </ol> <p>These files represent various medical imaging modalities and formats commonly used in clinical research and practice. They are intended for educational purposes, allowing students to practice image processing techniques, machine learning applications, and statistical analysis of medical images using Python libraries such as scikit-image, pydicom, and SimpleITK.</p>
Datasets for the Carpentry-style RNA-seq lesson
<p><strong>Lesson files</strong></p> <p>For all compressed files, go to the Shell and uncompress using `tar -xzvf myarchive.tar.gz`.</p> <p><strong>1) Bioinformatic files: </strong><a href="https://zenodo.org/api/files/436d1854-d638-4fdb-8d6b-7c566ed13af8/bioinformatic_tutorial_files.tar.gz?versionId=a80e4b0b-daf1-4008-8c98-1995ccb33748">bioinformatic_tutorial_files.tar.gz </a></p> <p>This archive contains the following datasets:</p> <p>FASTQ files from Arabidopsis leaf RNA-seq:</p> <ul> <li>Arabidopsis_sample3.fq.gz</li> <li>Arabidopsis_sample1.fq.gz</li> <li>Arabidopsis_sample4.fq.gz</li> <li>Arabidopsis_sample2.fq.gz</li> </ul> <p>Arabidopsis thaliana genome assembly and genome annotation:</p> <ul> <li>AtChromosome1.fa.gz</li> <li>ath_annotation.gff3.gz</li> </ul> <p>The sequence of sequencing adapters in adapters.fasta.</p> <p><strong>2) Gene counts usable with DESeq2 and R: </strong><a href="https://zenodo.org/api/files/436d1854-d638-4fdb-8d6b-7c566ed13af8/tutorial.tar.gz?versionId=17f6c4f9-b013-4806-9d05-9782a0b873ab">tutorial.tar.gz </a></p> <p>This archive contains the following datasets:</p> <ul> <li><strong>raw_counts.csv</strong>: a dataframe of the sample raw counts. It is a comma-separate values file therefore data are separated by commas ','.</li> <li><strong>samples_to_conditions.csv</strong>: a dataframe that indicates the correspondence between samples and experimental conditions (e.g. control, treated). </li> <li><strong>differential_genes.csv: </strong>a dataframe that contains the result of the DESeq2 analysis specifying this contrast in `DESEq2::results()` function: `<code class="language-plaintext">contrast = c("infected", "Pseudomonas_syringae_DC3000", "mock")</code></li> </ul> <p>The<strong> raw_counts.csv </strong>file was obtained by running the `v0.1.1` version of a <a href="https://github.com/KoesGroup/Snakemake_hisat-DESeq/blob/master/README.md">RNA-Seq bioinformatic pipeline</a> on the mRNA-Seq sequencing files from Vogel et al. (2016): <a href="https://www.ebi.ac.uk/ena/data/view/PRJEB13938">https://www.ebi.ac.uk/ena/data/view/PRJEB13938</a>.</p> <p>Please read the original study (Vogel et al. 2016): <a href="https://nph.onlinelibrary.wiley.com/doi/full/10.1111/nph.14036">https://nph.onlinelibrary.wiley.com/doi/full/10.1111/nph.14036</a></p> <p> </p> <p>====</p> <p><strong>Exercise files</strong></p> <p><strong>1) NASA spaceflight</strong></p> <p>The NASA GeneLab experiment GLDS-38 performed transcriptomics and proteomics of Arabidopsis seedlings in microgravity by sending seedlings to the International Space Station (ISS).</p> <p>The raw counts, scaled counts and sample to conditions files are available in the <a href="https://zenodo.org/api/files/d8dfab0f-b449-4608-8f60-a6d553e5aea3/NASA_spaceflight_GLDS38.zip">ZIP archive</a></p> <p><strong>2) Deforges 2019 hormone-treatments: </strong><a href="https://zenodo.org/api/files/d4a0b869-8eed-4b24-853c-ca9f7537bb51/deforges_2019.tar.gz">deforges_2019.tar.gz </a></p> <p>This archive contains:</p> <ul> <li>arabidopsis_root_hormones_raw_counts.csv</li> <li>arabidopsis_shoot_hormones_raw_counts.csv</li> <li>arabidopsis_root_hormones_sample2condition.csv</li> <li>arabidopsis_shoot_hormones_sample2condition.csv</li> <li>dataset01_IAA_arabidopsis_root_raw_counts.csv</li> <li>dataset02_ABA_arabidopsis_root_raw_counts.csv</li> <li>dataset03_ACC_arabidopsis_root_raw_counts.csv</li> <li>dataset04_MeJA_arabidopsis_root_raw_counts.csv</li> </ul> <p>The <em>arabidopsis_root_hormones_raw_counts.csv</em> file contains all gene counts from all hormones. Separate datasets were made for each hormone for convenience.</p>
Library Carpentry SQL Lesson - DOAJ Article Sample Database
<p>Sample Library Carpentry SQL lesson database created from the Directory of Open Access Journals (DOAJ) data. The sample SQL database contains tables: articles, journals, languages, licences, and publishers. Previous version of the sample SQL database: Staiger, Christine (2016): LC-articles. figshare. Dataset. https://doi.org/10.6084/m9.figshare.3409471.v3</p>
Data for the Economics curriculum of Data Carpentry
<p># dc-economics-data<br> Data for the Economics curriculum of Data Carpentry.</p> <p>## References<br> - CEPII, 2011. "GeoDist Database." Downloaded from http://www.cepii.fr/distance/dist_cepii.dta on 2019-06-05.<br> - Mayer, T. & Zignago, S. 2011. "Notes on CEPII’s distances measures: the GeoDist Database." CEPII Working Paper 2011-25. http://www.cepii.fr/CEPII/en/publications/wp/abstract.asp?NoDoc=3877<br> - World Bank, 2019. "World Development Indicators 2018." Downloaded from https://datacatalog.worldbank.org/dataset/world-development-indicators on 2019-06-05.</p>
FAIR Data and Software Carpentries Lesson Mind Map
<p>The FAIR Data and Software Carpentries Concept Map is an instructional planning tool to help in the development of a FAIR Data and Software Carpentries lesson in Library Carpentry.</p>
Library Carpentry SQL Lesson - DOAJ Article Sample Database
<p>Sample Library Carpentry SQL lesson database created from the Directory of Open Access Journals (DOAJ) data. The sample SQL database contains tables: articles, journals, languages, licences, and publishers. Previous version of the sample SQL database: Staiger, Christine (2016): LC-articles. figshare. Dataset. https://doi.org/10.6084/m9.figshare.3409471.v3</p> <p>Note: this is an updated version of the original record uploaded to Zenodo in 2019 at https://zenodo.org/record/2822005.</p>
Databases for the Carpentries Pangenomics Workshop
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Boat in Kim Bong Carpentry Village
This is the vessel which we recorded ship-lines on. It was captured on 31/1/2018 using a Sony a6000 camera. 344 photos were processed in Agisoft Photoscan while in the field. A standard photo scale scale and a north arrow were placed in the starboard stern, there is also a 2m yellow scale lying along the port side on deck, and along the floor on the starboard side. Apart from a small hole on the port hull towards the bow, and right beneath the boat, the model appears complete, and appears to be relatively accurate. Tie points: 182,998. Just over 8 million points were processed into just under a million polygons, which were then textured and uploaded to Sketchfab. Source: Objaverse 1.0 / Sketchfab
Genome Mining for Software Carpentry lesson
<p>Data for Software Carpentry Genome mining lesson. These data are part of the study<em> "Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: Implications for the microbial pan-genome" </em>(Hervé Tettelin et al 2005)</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.