In the age of digital transformation, scientific and social interest for data and data products is constantly on the rise. The quantity as well as the variety of digital research data is increasing significantly. This raises the question about the governance of this data. For example, how to store the data so that it is presented transparently, freely accessible and subsequently available for re-use in the context of good scientific practice. Research data repositories provide solutions to these issues.

Considering the variety of repository software, it is sometimes difficult to identify a fitting solution for a specific use case. For this purpose a detailed analysis of existing software is needed. Presented table of requirements can serve as a starting point and decision-making guide for choosing the most suitable for your purposes repository software. This table is dealing as a supplementary material for the paper "How to choose a research data repository software? Experience report." (persistent identifier to the paper will be added as soon as paper is published).

This file collection is part of the ORD Landscape and Cost Analysis Project (DOI: 10.5281/zenodo.2643460), a study jointly commissioned by the SNSF and swissuniversities in 2018.

Please cite this data collection as: von der Heyde, M. (2019). Data from the International Open Data Repository Survey. Retrieved from https://doi.org/10.5281/zenodo.2643493

Further information is given in the corresponding data paper: von der Heyde, M. (2019). International Open Data Repository Survey: Description of collection, collected data, and analysis methods [Data paper]. Retrieved from https://doi.org/10.5281/zenodo.2643450

Contact

Swiss National Science Foundation (SNSF)

Open Research Data Group

E-mail: ord@snf.ch

swissuniversities

Program "Scientific Information"

Gabi Schneider

E-Mail: isci@swissuniversities.ch

Context

Zenodo.org is a popular data repository hosted by CERN. There are tens of thousands of datasets in the repository, but not all of them are used to the same extent.

Content

This dataset includes names and links to the top 500 most downloaded datasets on Zenodo.

Inspiration

This dataset can be used to find datasets deposited on zenodo that would benefit from additional exposure to the DS/ML community by uploading them to Kaggle.

This data continues with the development of the NPEGC Trinity de novo metatranscriptome assemblies from the protein data repository of The North Pacific Eukaryotic Gene Catalog. 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: The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations. 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: The North Pacific Eukaryotic Gene Catalog: Raw assemblies from Gradients 1, 2 and 3

Key processing steps are sampled below with links to the detailed code on the main github code repository: https://github.com/armbrustlab/NPac_euk_gene_catalog

Code used to build the kallisto indices and map the short reads against indices with kallisto are online in the code repository here: NPEGC.nt_kallisto_counts.sh

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: aggregate_kallisto_counts.R
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

Repository for all research outputs from across all fields of science in any file format as well as both positive and negative results. They assign all publicly available uploads a Digital Object Identifier (DOI) to make the upload easily and uniquely citeable. They further support harvesting of all content via the OAI-PMH protocol. They promote peer-reviewed openly accessible research, and curate uploads. ZENODO allows users to create their own collection and accept or reject all uploads to it. They allow for uploading under a multitude of different licenses and access levels.

This file collection is part of the ORD Landscape and Cost Analysis Project (DOI: 10.5281/zenodo.2643460), a study jointly commissioned by the SNSF and swissuniversities in 2018.

Please cite this data collection as: von der Heyde, M. (2019). Data and tools of the landscape and cost analysis of data repositories currently used by the Swiss research community. Retrieved from https://doi.org/10.5281/zenodo.2643495

Connected data papers are: von der Heyde, M. (2019). Open Data Landscape: Repository Usage of the Swiss Research Community: Description of collection, collected data, and analysis methods [Data paper]. Retrieved from https://doi.org/10.5281/zenodo.2643430 von der Heyde, M. (2019). International Open Data Repository Survey: Description of collection, collected data, and analysis methods [Data paper]. Retrieved from https://doi.org/10.5281/zenodo.2643450

Connected data sets are: von der Heyde, M. (2019). Data from the Swiss Open Data Repository Landscape survey. Retrieved from https://doi.org/10.5281/zenodo.2643487 von der Heyde, M. (2019). Data from the International Open Data Repository Survey. Retrieved from https://doi.org/10.5281/zenodo.2643493

Contact

Swiss National Science Foundation (SNSF)

Open Research Data Group

E-mail: ord@snf.ch

swissuniversities

Program "Scientific Information"

Gabi Schneider

E-Mail: isci@swissuniversities.ch

Built and developed by researchers, to ensure that everyone can join in Open Science. The OpenAIRE project, in the vanguard of the open access and open data movements in Europe was commissioned by the EC to support their nascent Open Data policy by providing a catch-all repository for EC funded research. CERN, an OpenAIRE partner and pioneer in open source, open access and open data, provided this capability and Zenodo was launched in May 2013. In support of its research programme CERN has developed tools for Big Data management and extended Digital Library capabilities for Open Data. Through Zenodo these Big Science tools could be effectively shared with the long-tail of research. Zenodo helps researchers receive credit by making the research results citable and through OpenAIRE integrates them into existing reporting lines to funding agencies like the European Commission. Citation information is also passed to DataCite and onto the scholarly aggregators.

Code Images

Context

This is a subset of the Zenodo-ML Dinosaur Dataset [Github] that has been converted to small png files and organized in folders by the language so you can jump right in to using machine learning methods that assume image input.

Content

Included are .tar.gz files, each named based on a file extension, and when extracted, will produce a folder of the same name.

 tree -L 1
.
├── c
├── cc
├── cpp
├── cs
├── css
├── csv
├── cxx
├── data
├── f90
├── go
├── html
├── java
├── js
├── json
├── m
├── map
├── md
├── txt
└── xml

And we can peep inside a (somewhat smaller) of the set to see that the subfolders are zenodo identifiers. A zenodo identifier corresponds to a single Github repository, so it means that the png files produced are chunks of code of the extension type from a particular repository.

$ tree map -L 1
map
├── 1001104
├── 1001659
├── 1001793
├── 1008839
├── 1009700
├── 1033697
├── 1034342
...
├── 836482
├── 838329
├── 838961
├── 840877
├── 840881
├── 844050
├── 845960
├── 848163
├── 888395
├── 891478
└── 893858

154 directories, 0 files

Within each folder (zenodo id) the files are prefixed by the zenodo id, followed by the index into the original image set array that is provided with the full dinosaur dataset archive.

$ tree m/891531/ -L 1
m/891531/
├── 891531_0.png
├── 891531_10.png
├── 891531_11.png
├── 891531_12.png
├── 891531_13.png
├── 891531_14.png
├── 891531_15.png
├── 891531_16.png
├── 891531_17.png
├── 891531_18.png
├── 891531_19.png
├── 891531_1.png
├── 891531_20.png
├── 891531_21.png
├── 891531_22.png
├── 891531_23.png
├── 891531_24.png
├── 891531_25.png
├── 891531_26.png
├── 891531_27.png
├── 891531_28.png
├── 891531_29.png
├── 891531_2.png
├── 891531_30.png
├── 891531_3.png
├── 891531_4.png
├── 891531_5.png
├── 891531_6.png
├── 891531_7.png
├── 891531_8.png
└── 891531_9.png

0 directories, 31 files

So what's the difference?

The difference is that these files are organized by extension type, and provided as actual png images. The original data is provided as numpy data frames, and is organized by zenodo ID. Both are useful for different things - this particular version is cool because we can actually see what a code image looks like.

How many images total?

We can count the number of total images:

find "." -type f -name *.png | wc -l
3,026,993

Dataset Curation

The script to create the dataset is provided here. Essentially, we start with the top extensions as identified by this work (excluding actual images files) and then write each 80x80 image to an actual png image, organizing by extension then zenodo id (as shown above).

Saving the Image

I tested a few methods to write the single channel 80x80 data frames as png images, and wound up liking cv2's imwrite function because it would save and then load the exact same content.

import cv2
cv2.imwrite(image_path, image)

Loading the Image

Given the above, it's pretty easy to load an image! Here is an example using scipy, and then for newer Python (if you get a deprecation message) using imageio.

image_path = '/tmp/data1/data/csv/1009185/1009185_0.png'
from imageio import imread

image = imread(image_path)
array([[116, 105, 109, ..., 32, 32, 32],
    [ 48, 44, 48, ..., 32, 32, 32],
    [ 48, 46, 49, ..., 32, 32, 32],
    ..., 
    [ 32, 32, 32, ..., 32, 32, 32],
    [ 32, 32, 32, ..., 32, 32, 32],
    [ 32, 32, 32, ..., 32, 32, 32]], dtype=uint8)


image.shape
(80,80)


# Deprecated
from scipy import misc
misc.imread(image_path)

Image([[116, 105, 109, ..., 32, 32, 32],
    [ 48, 44, 48, ..., 32, 32, 32],
    [ 48, 46, 49, ..., 32, 32, 32],
    ..., 
    [ 32, 32, 32, ..., 32, 32, 32],
    [ 32, 32, 32, ..., 32, 32, 32],
    [ 32, 32, 32, ..., 32, 32, 32]], dtype=uint8)

Remember that the values in the data are characters that have been converted to ordinal. Can you guess what 32 is?

ord(' ')
32

# And thus if you wanted to convert it back...
chr(32)

So how t...

Zenodo is an open repository that allows researchers to deposit research papers, data sets, research software, reports, and any other research related digital artefacts.

The long-term storage of data presents a significant challenge, particularly when the data is in a proprietary format or requires specialized software for display and processing. This effectively means that to access data from a repository like Zenodo, one must first download the dataset and then install a specific application before the data can be viewed.

In this proof-of-concept, we have demonstrated a more streamlined approach. We uploaded a native Bruker spectrometer NMR dataset to Zenodo and showed that it's possible to visualize the data with just one click. Moreover, you can also zoom in and examine all the acquisition parameters.

To visualize the data, please click on the following link: https://zenodo.nmrium.org/zenodo/v1/record/13307304" href="https://zenodo.nmrium.org/zenodo/v1/record/13307304" target="_blank" rel="noopener">https://zenodo.nmrium.org/zenodo/v1/record/13307304

This link will provide direct access to the data, eliminating the need for downloading the dataset or installing any specific applications, thus making the process of data visualization more efficient and user-friendly.

This dataset aggregates information about 191 research data repositories that were shut down. The data collection was based on the registry of research data repositories re3data and a comprehensive content analysis of repository websites and related materials. Documented in the dataset are the period in which a repository was active, the risks resulting in its shutdown, and the repositories taking over custody of the data after.

Overview The files in this repository compose the Charge-dependent, Reproducible, Accessible, Forcefield-dependent, and Temperature-dependent Exploratory Database (CRAFTED) of adsorption isotherms. This dataset contains the simulation of CO2 and N2 adsorption isotherms on 690 metal-organic frameworks taken from the CoRE-MOF-2014 database and 667 covalent organic frameworks taken from the CURATED-COFs database. The simulations were performed with two force fields (UFF and DREIDING), six partial charge schemes (no charges, Qeq, EQeq, DDEC, MPNN, and PACMOF), and three temperatures (273, 298, 323 K). Contents

CIF_FILES/ contains 6 folders (NEUTRAL, DDEC, EQeq, Qeq, MPNN, and PACMOF), each one with 1357 CIF files; FORCEFIELDS/ contains 2 folders (UFF and DREIDING) with the definition of the forcefields; INPUT_FILES/ contains 97,704 input files for the GCMC simulations; ISOTHERM_FILES/ contains 97,704 adsorption isotherms resulting from the GCMC simulation; ENTHALPY_FILES/ contains 97,704 enthalpies of adsorption from the isotherms; RAC_DBSCAN/ contains the RAC and geometrical descriptors to perform the t-NSE + DBSCAN analysis; Licenses The 690 MOF-related CIF files in the DDEC folder were downloaded from CoRE-MOF-2014 and are licensed under the terms of the Creative Commons Attribution 4.0 International license (CC-BY-4.0). The 667 COF-related CIF files in the NEUTRAL folder were downloaded from CURATED-COFs and are licensed under the terms of the MIT license (MIT).

Dalar Nazarian, Jeffrey S. Camp, & David S. Sholl. (2016). Computation-Ready Experimental Metal-Organic Framework (CoRE MOF) 2014 DDEC Database [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3986573 Ongari, Daniele, et al. "Building a consistent and reproducible database for adsorption evaluation in covalent–organic frameworks." ACS Central Science 5.10 (2019): 1663-1675. https://doi.org/10.1021/acscentsci.9b00619 Ongari, Daniele, Leopold Talirz, and Berend Smit. "Too many materials and too many applications: An experimental problem waiting for a computational solution." ACS Central Science 6.11 (2020): 1890-1900. https://doi.org/10.1021/acscentsci.0c00988 The CO2.def and N2.def forcefield files were downloaded from RASPA and are licensed under the terms of the MIT license.

Dubbeldam, David, et al. "RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials." Molecular Simulation 42.2 (2016): 81-101. https://doi.org/10.1080/08927022.2015.1010082 The remaining MOF-related CIF files in the PACMOF, MPNN, Qeq, EQeq and NEUTRAL folders were derived from those in the DDEC folder and are licensed under the terms of the Creative Commons Attribution 4.0 International license (CC-BY-4.0) from the CoRE-MOF-2014 subset. The remaining COF-related CIF files in the PACMOF, MPNN, Qeq, EQeq and DDEC folders were derived from those in the NEUTRAL folder and are licensed under the terms of the MIT license (MIT) from the CURATED-COFs subset. All remaining files were created by us, and are licensed under the terms of the CDLA-Sharing-1.0 license. Software requirements In order to create a Python environment capable of running the Jupyter notebooks, please install conda and execute conda env create --file environment.yml Usage instructions Execute the command below to run JupyterLab in the appropriate Python environment. conda run --name crafted jupyter-lab

Table of Contents

Main Description File Descriptions Linked Files Installation and Instructions

1. Main Description

This is the Zenodo repository for the manuscript titled "A TCR β chain-directed antibody-fusion molecule that activates and expands subsets of T cells and promotes antitumor activity.". The code included in the file titled marengo_code_for_paper_jan_2023.R was used to generate the figures from the single-cell RNA sequencing data. The following libraries are required for script execution:

Seurat scReportoire ggplot2 stringr dplyr ggridges ggrepel ComplexHeatmap

File Descriptions

The code can be downloaded and opened in RStudios. The "marengo_code_for_paper_jan_2023.R" contains all the code needed to reproduce the figues in the paper The "Marengo_newID_March242023.rds" file is available at the following address: https://zenodo.org/badge/DOI/10.5281/zenodo.7566113.svg (Zenodo DOI: 10.5281/zenodo.7566113). The "all_res_deg_for_heat_updated_march2023.txt" file contains the unfiltered results from DGE anlaysis, also used to create the heatmap with DGE and volcano plots. The "genes_for_heatmap_fig5F.xlsx" contains the genes included in the heatmap in figure 5F.

Linked Files

This repository contains code for the analysis of single cell RNA-seq dataset. The dataset contains raw FASTQ files, as well as, the aligned files that were deposited in GEO. The "Rdata" or "Rds" file was deposited in Zenodo. Provided below are descriptions of the linked datasets:

Gene Expression Omnibus (GEO) ID: GSE223311(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE223311)

Title: Gene expression profile at single cell level of CD4+ and CD8+ tumor infiltrating lymphocytes (TIL) originating from the EMT6 tumor model from mSTAR1302 treatment. Description: This submission contains the "matrix.mtx", "barcodes.tsv", and "genes.tsv" files for each replicate and condition, corresponding to the aligned files for single cell sequencing data. Submission type: Private. In order to gain access to the repository, you must use a reviewer token (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html).

Sequence read archive (SRA) repository ID: SRX19088718 and SRX19088719

Title: Gene expression profile at single cell level of CD4+ and CD8+ tumor infiltrating lymphocytes (TIL) originating from the EMT6 tumor model from mSTAR1302 treatment. Description: This submission contains the raw sequencing or .fastq.gz files, which are tab delimited text files. Submission type: Private. In order to gain access to the repository, you must use a reviewer token (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html).

Zenodo DOI: 10.5281/zenodo.7566113(https://zenodo.org/record/7566113#.ZCcmvC2cbrJ)

Title: A TCR β chain-directed antibody-fusion molecule that activates and expands subsets of T cells and promotes antitumor activity. Description: This submission contains the "Rdata" or ".Rds" file, which is an R object file. This is a necessary file to use the code. Submission type: Restricted Acess. In order to gain access to the repository, you must contact the author.

Installation and Instructions

The code included in this submission requires several essential packages, as listed above. Please follow these instructions for installation:

Ensure you have R version 4.1.2 or higher for compatibility.

Although it is not essential, you can use R-Studios (Version 2022.12.0+353 (2022.12.0+353)) for accessing and executing the code.

1. Download the *"Rdata" or ".Rds" file from Zenodo (https://zenodo.org/record/7566113#.ZCcmvC2cbrJ) (Zenodo DOI: 10.5281/zenodo.7566113).
2. Open R-Studios (https://www.rstudio.com/tags/rstudio-ide/) or a similar integrated development environment (IDE) for R.
3. Set your working directory to where the following files are located:

marengo_code_for_paper_jan_2023.R Install_Packages.R Marengo_newID_March242023.rds genes_for_heatmap_fig5F.xlsx all_res_deg_for_heat_updated_march2023.txt

You can use the following code to set the working directory in R:

setwd(directory)

1. Open the file titled "Install_Packages.R" and execute it in R IDE. This script will attempt to install all the necessary pacakges, and its dependencies in order to set up an environment where the code in "marengo_code_for_paper_jan_2023.R" can be executed.
2. Once the "Install_Packages.R" script has been successfully executed, re-start R-Studios or your IDE of choice.
3. Open the file "marengo_code_for_paper_jan_2023.R" file in R-studios or your IDE of choice.
4. Execute commands in the file titled "marengo_code_for_paper_jan_2023.R" in R-Studios or your IDE of choice to generate the plots.

GitHub Repository - Description: Contains data, processing and analysis code, initial exploratory figures, final publication figures, and final publication tables. - Link: https://github.com/cliffbueno/Sunflower_AEM - Note: A release of this repository has been archived on Zenodo with a stable DOI: https://zenodo.org/doi/10.5281/zenodo.12193724

Data files for the manuscript "Local potential distribution generates edge currents in a magnetic topological insulator" (https://doi.org/10.1103/9t67-dl2v)

This repository contains the data for the paper:

Authors: Kai Jeggle , David Neubauer , Hanin Binder and Ulrike LohmannTitel: Cirrus formation regimes - Data driven identification and quantification of mineral dust effectDate: 2024

Note that the scripts can be found in the accompanying code repository (https://github.com/tabularaza27/cloud_clustering)Contents:├── cirrus_cloud_trajectories.ftr├── cluster_input_data.ftr├── cluster_models│ └── temperature_clustering_k4_12│ ├── cloud_ids.npy│ ├── model_params.json│ └── trained_model.hdf5

│ └── temperature_clustering_k4_24│ ├── cloud_ids.npy│ ├── model_params.json│ └── trained_model.hdf5

├── cluster_predictions.ftr└── readme.txtFor more info, please have a look at the readme.txtThis is an updated version of the data, containing updated models and predictions based on the Journal revisions

Description of the dataset

The Oldenburg Hearing Health Repository (OHHR) provides a publicly accessible dataset that can be used to advance hearing health research. It includes a constellation of data collected from 581 participants (aged 18–86 years; 255 female; Mean age = 67.31 years; SD = 11.93) between 2013 and 2015 at the Hörzentrum Oldenburg in collaboration with the Cluster of Excellence "Hearing4all". The data was anonymized in accordance with the General Data Protection Regulation (GDPR; Regulation (EU) 2016/679). Each participant was assigned a unique identifier to maintain anonymity while enabling multivariate individualized analyses.

All the different data types are listed below:Subjective Measures

Home Questionnaire

SF-12 Health Survey

Technology Readiness Questionnaire

Anamnesis

Audiological Tests

Pure Tone Audiometry

Adaptive Categorical Loudness Scaling

Digit Triplet Test (Speech Reception Threshold in Noise: Screening)

Göttingen Sentence Test (Speech Reception Threshold in Noise)

Cognitive Measures

DemTect

WortSchatz

Demographic Information

Socio-economic data

Scheuch-Winkler Index (calculated)

Supporting Documentation

MethodsDescription.rtf/.pdf: Provides detailed explanations of data type and collection procedures.data.zip/metadata: Includes schema and description files for all data tables.

A supporting paper of the same name detailing the dataset and methodologies will be published and made publicly available soon.

Associated data for Hartung et al. "Layer 1 NDNF Interneurons are Specialized Top-Down Master Regulators of Cortical Circuits".

• This is repository 3/3 and contains histological data for figure 3.
• Repository 1/3 (10.5281/zenodo.10938947) contains all data for figures 1, 2 & 4-6 of the paper, as well as electrophysiological data for figure 3.
• Repository 1/3 (10.5281/zenodo.10938947) contains all code generated for and used in the paper.
• Repository 3/3 (10.5281/zenodo.10938467) contains additional histological data for figure 3.
• The code can alternatively also be accessed via GitHub: https://github.com/janH-21/NDNF-interneurons-cortical-circuits
• Please consider citing our paper if you use our data or code (see GitHub repository for link).
• Please refer to the README file for orientation and contact JH or JJL if you have further questions (see paper for contact details).

Dataset with the data from the first experiment of the "Esposende Project" (November 2023) for the development of a semi-automatic analyser for verifying compliance with the W3C DWBP in datasets deposited in the Zenodo repository. The file contains the doi of the analysed dataset and the result of the verification of each of the 35 good practices. Dataset con los datos del primer experimento del "Proyecto Esposende" (noviembre de 2023) para el desarrollo de un analizador semiautomático de verificación del cumplimiento de las DWBP del W3C en conjuntos de datos depositados en el repositorio Ze. En el fichero aparece el doi del dataset analizado y el resultado de la verificación de cada una de las 35 buenas prácticas.

Data files for building: ES04 - Office - León (Spain) Languages: Spanish, English These files are part of the public benchmark repository created as a part of the crossCert EU project. This repository contains curated building data, certificate results and, where available, measured performance results. The repository is publicly available so that it can be used as a testbench for new Energy Performance Certificate (EPC) procedures. The files are organised in the following folders (note that not all files are always provided): Main data and Results, with: Neutral data inventory. Neutral results report. Original EPC certificate. Energy Consumption Data, with: Files, where available, with energy consumption data for the building, which can be used for validation of models and EPC results. Drawings Building drawings which can be used as an aid for generating the EPC, or for creating dynamic energy consumption models. Other Data Any other data that can be useful for the purposes of creating or validating an EPC or an energy consumption dynamic model for the building. Dynamic Model Data to run a dynamic model of the building, if available. The files have been redacted to exclude confidential information.