cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c5m32nn1r
title: MassIVE MSV000094548 - WDR76_SPIN1_Halo-AP_Enriched_Histones_AscendDIA
title: Histone PTM bottom-up profiling of Halo-WDR76 or Halo-SPIN1 Enriched or total histones (w Propionylation; no spike-in peptide) Supplementry data for WDR76-SPIN1 SCAP Analysis
DOI: 10.5258/soton/d2323
title: Dataset for the publication: 'Monolithically integrated polarization rotator and splitter with designed power ratio'

description: This dataset contains the raw data for the figures in the publication titled "Monolithically integrated polarization rotator and splitter with designed power ratio" in Optics Express. Datafiles are in .xlsx format.
DOI: 10.5281/zenodo.10213532
title: Formalized information from the study protocols of three decentralized studies on rare diseases within the CORD project
title: CORD - Collaboration on Rare Diseases

description: The file contains formalized information from the study protocols of decentralized studies on rare diseases within the CORD project. This includes the diagnoses that are coded with ICD-10-GM. The following diseases or use cases are described:

Cystic Fibrosis and Pregnancy or Delivery
Phenylketonuria, Comorbidities and Pregnancy or Delivery
Kawasaki and Pediatric Inflammatory Multisystem Syndrome (PIMS)
DOI: 10.5281/zenodo.10675312
title: Global Twenty-four Solar Terms phenological MODIS normalized difference vegetation index dataset in 2001-2022

description: Phenology reflects the life cycle of vegetation, crucial for monitoring global vegetation diversity, ecosystem stability, and agricultural security. However, there is currently no dataset related to phenology. The twenty-four solar terms (24STs), based on the Sun's annual motion, reflect the changing seasons, temperature fluctuations, and phenological phenomena. They serve as a vital means to characterize vegetation phenology. This study generate a global Normalized Difference Vegetation Index (NDVI) product based on 24STs using Moderate Resolution Imaging Spectroradiometer (MODIS) on the Google Earth Engine (GEE). The 24STs NDVI dataset adopted the maximum value compositing (MVC) to process the NDVI values between two adjacent 24STs. The product has a temporal resolution of 16-day and a spatial resolution of 250m. Comparing with the MOD13Q1, good spatiotemporal consistency between the two datasets was observed, confirming the reliability of the 24STs product. However, the 24STs product holds distinct phenological meanings. This product introduces, for the first time, a vegetation index dataset based on the 24STs, enriching the vegetation index dataset and facilitating further research on phenology.
DOI: 10.5281/zenodo.10790966
title: Supplementary material: How much hydrogen could we need in New Zealand? Understanding the diverse hydrogen applications and their regional mapping

description: Dataset on the future hydrogen demand for New Zealand, mapped to its 16 regions. Supplementary material of the publication: "How much hydrogen could we need in New Zealand? Understanding the diverse hydrogen applications and their regional mapping"
DOI: 10.5281/zenodo.10820823
title: Data associated with global-bird-detection-paper

description: Data for repository located at https://github.com/sarabsethi/global-bird-detection-paper

Unzip the file to the directory and follow the instructions in the repository to run the code.

(March 2024 update reflects modifications made for manuscript revision)
DOI: 10.5281/zenodo.11639608
title: Supplementary data for "Seasonal emergence and circulation coupling of moist layers over the tropical Atlantic"

description: The data provided here contains the derived moist layer characteristics from ERA5 reanalysis, as presented in the associated publication. Data is given on a 0.25° latitude/longitude grid between 30°N/S on the native vertical levels of the ERA5 dataset, cut off at 50 hPa. When a moist layer is detected at a given location and altitude, as identified by the method described in Sect. 2.2 of the publication, the moist layer characteristics (strength, pressure level, upper bound pressure, lower bound pressure) are written in the associated files at the given location (lat/lon) and pressure level of the moist layer. Here, we provide the 15 year means and standard deviations for the months January and July, and the 3 hourly data along the equatorial and 15°N cross-sections of the Atlantic used for the Hovmoller diagrams in Fig. 4 and Supporting Figure S1 of the publication.

Files containing "geographical_mean" indicate monthly means on the latitude/longitude grid. Files containing "moisture_space" indicate monthly means within moisture space over the tropical Atlantic, as defined in the paper. Code used to generate the data and to conduct the analysis presented in the publication are provided in the github repository URL listed in the Software section.
DOI: 10.5281/zenodo.11831299
title: Datasets for "Detecting rhythmic spiking through the power spectra of point process model residuals"

description: This repository contains synthetic and empirical datasets that were analyzed in the preprint "Detecting rhythmic spiking through the power spectra of point process model residuals."

Relative to the previous version (1.0.0), this new version (1.0.1) only entails changes in the naming of some folders, and the text of an explanatory text file.  The changes reflect the reordering of the figures in the newest version of the manuscript.
description: This research was funded in part by Aligning Science Across Parkinson's [ASAP-020519] through the Michael J. Fox Foundation for Parkinson's Research (MJFF). For the purpose of open access, the authors have applied a Creative Commons Attribution 4.0 International (CC BY) public copyright license to this dataset.
DOI: 10.5281/zenodo.3779037
title: Processed data and analysis results for 104 RBPs

description: This repository makes available the processed data and the results of our SURF paper. The paper presents the <strong>S</strong>tatistical <strong>U</strong>tility for <strong>R</strong>BP <strong>F</strong>unctions (SURF) for integrative analysis of RNA-seq and CLIP-seq data. The goal of SURF is to identify alternative splicing (AS), alternative transcription initiation (ATI), and alternative polyadenylation (APA) events regulated by individual RBPs and elucidate protein-RNA interactions governing these events. We applied the SURF pipeline to analyze 104 RBP data sets (from ENCODE) and performed downstream analysis. Check out the browsable results from this shiny app! The current repository includes: meme.326.input.zip -- input of 326 MEME runs on SURF-inferred location features meme.326.output.zip -- output of 326 MEME runs on SURF-inferred location features surf_inferred_feature.gtf -- SURF-inferred location features for 52 RBPs gencode.v24.annotation.filtered.gtf -- filtered genome annotation used for ENCODE data analysis Homo_sapiens.GRCh37.71.primary_assembly.protein_coding.gtf -- genome annotation used for simulation study simulation_truth.txt -- truth parameters used for RNA-seq simulation [RBP].results.rds -- SURF output (each an R object) for 104 RNA-binding proteins ([RBP] the protein name). For reproducing the results, the source code is available at DOI: 10.5281/zenodo.3779853.
DOI: 10.5281/zenodo.6384728
title: DBpedia RDF2Vec Graph Embeddings

description: DBpedia graph embeddings using RDF2Vec. RDF2Vec embedding generation code can be found here and is based on a publication by Portisch et al. [1]. The embeddings dataset consists of 200-dimensional vectors of DBpedia entities (from 1/9/2021). Figure of cosine similarities between a selected set of DBpedia entities are provided in the dataset here. <strong>Generating Embeddings</strong> The code for generating these embeddings can be found here. Run the run.sh script that wraps all the necessary commmands to generate embeddings <pre><code class="language-bash">bash run.sh</code></pre> The script downloads a set of DBpedia files, which are listed in <code>dbpedia_files.txt</code>. It then builds a Docker image and runs a container of that image that generates the embeddings for the DBpedia graph defined by the DBpedia files. A folder <code>files</code> is created containing all the downloaded DBpedia files, and a folder <code>embeddings/dbpedia</code> is created containing the embeddings in <code>vectors.txt</code> along a set of random walk files. <strong>Run Time of Embeddings Generation</strong> Generating embeddings can take more than a day, but it depends on the number of DBpedia files chosen to be downloaded. Following are some basic run time statistics when embeddings are generated on a 64 GB RAM, 8 cores (AMD EPYC), 1 TB SSD, 1996.221 MHz machine. <strong>Total</strong>: 1 day, 8 hours, 52 minutes, 41 seconds <strong>Walk generation</strong>: 0 days, 7 minutes, 24 minutes, 36 seconds <strong>Training</strong>: 1 day, 1 hour, 28 minutes, 5 seconds <strong>Parameters Used</strong> Here is listed the parameters used to generate the embeddings provided here: <strong>Number of walks per entity</strong>: 100 <strong>Depth (hops) per walk</strong>: 4 <strong>Walk generation mode</strong>: RANDOM_WALKS_DUPLICATE_FREE <strong>Threads</strong>: # of processors / 2 <strong>Training mode</strong>: sg <strong>Embeddings vector dimension</strong>: 200 <strong>Minimum word2vec word count</strong>: 1 <strong>Sample rate</strong>: 0.0 <strong>Training window size</strong>: 5 <strong>Training epochs</strong>: 5
description: {"references": ["Portisch, J., Hladik, M. and Paulheim, H., 2020. RDF2Vec Light--A Lightweight Approach for Knowledge Graph Embeddings. arXiv preprint arXiv:2009.07659."]}
DOI: 10.5281/zenodo.7839767
title: ARRIVE checklist for 'The role of proteomics analysis in carcinogenesis in a rat mammary cancer model induced by DMBA (7,12-Dimethylbenz[a]anthracene)

description: The ARRIVE guidelines for animal reporting: ‘The role of proteomics analysis in carcinogenesis in a rat mammary cancer model induced by DMBA (7,12-Dimethylbenz[a]anthracene)’
DOI: 10.5281/zenodo.8279707
title: Jet feature data from PAMIP model simulations

description: Author: Yvonne Anderson Contact: ee22ya@leeds.ac.uk Dataset created: 21/08/2023 Paper title: Minimal influence of future Arctic sea ice loss on North Atlantic jet stream morphology <strong>Dataset information</strong> CSV files contain arrays of daily jet feature data for all ensemble member winters for a given model. Dimensions of the arrays are (number of ensemble members, 90 winter days). <strong>Filename structure</strong> Filenames of CSV files can be interpreted as: timeperiod_jetfeature_model.csv <strong>Example filename structure</strong> Time period Jet feature Model Example filename Present-day Latitude AWI-CM-1-1-MR present-day_jet_latitude_AWI-CM-1-1-MR.csv Future Speed HadGEM3-GC31-MM future_jet_speed_HadGEM3-GC31-MM.csv <strong>Jet feature description</strong> Jet feature data are for the largest mass jet region found on each day of winter, where jet mass is the area weighted jet speed. The jet features and corresponding units contained in the csv files are as follows: Jet feature Units Latitude ° Speed ms<sup>-1</sup> Mass ms<sup>-1</sup> Tilt ° Area m<sup>2</sup> <strong>Time periods</strong> Time periods are present-day and future, which refer to simulations forced by present-day and future sea ice concentrations, from which the jet features have been extracted. <strong>Models</strong> Models are AWI-CM-1-1-MR, CanESM5, FGOALS-f3-L, HadGEM3-GC31-MM, IPSL-CM6A-LR and MIROC6 from the Polar Amplification Model Intercomparison Project (PAMIP; https://doi.org/10.5194/gmd-12-1139-2019) <strong>Spatial and temporal information</strong> Arrays contain daily jet feature data that has been constrained to the North Atlantic region (0-60 &deg; W, 15-75 &deg; N) and to the winter period (December, January and February) <strong>Prior processing</strong> Original dataset: netcdf files of daily zonal wind data from Polar Amplification Model Intercomparison Project simulations forced by present-day and future sea ice concentrations 850 hPa wind speed data was extracted and regridded to 2.81 ° x 2.81 ° resolution Constrained to North Atlantic region and winter period Wind speed data was filtered using a 10-day Lanczos filter with a 61 day window Jet feature data was extracted for each day in ensemble member winters and saved to numpy arrays <strong>Example code for loading jet variables from csv file</strong> To generate a numpy array of jet variable arrays contained in the csv file: <pre><code class="language-python">loaded_jet_variable_arrays = np.genfromtxt((path_to_file/filename.csv'), delimiter=',')</code></pre> To combine arrays for all ensemble member winters, which allows plotting of daily jet feature distributions: <pre><code class="language-python">jet_variable_array_all_winters = np.concatenate(loaded_jet_variable_arrays)</code></pre>
DOI: 10.5281/zenodo.8359714
title: A large comprehensive curated dataset of small molecules and their activities covering three cardiac ion channels: hERG, Cav1.2, and Nav1.5

description: The compressed data folder (dataset.rar) represents a data framework for researchers in the field of drug discovery to perform in depth analyses on a very large open-access unique and comprehensive hERG, Nav1.5, and Cav1.2 cardiotoxicity integrated database of small molecules and their activities. The database is organized as follows:



Each sub-folder represents a cardiac ion channel target: hERG, Nav1.5, and Cav1.2

Each target sub-folder consists of 3 files in CSV format: One file containing the development set (split into training and validation sets using an 80/20 ratio for hyperparameter tuning). The other 2 files contain external evaluation sets. The first test dataset consists of compounds with a structural similarity of no more than 60% (Tanimoto similarity  ≤ 0.6) to the remaining development set, while the second test dataset comprises compounds with a structural similarity of no more than 70% (Tanimoto similarity ≤ 0.7) to the remaining development set.

Each file contains data with 7 columns: "InChl Key" as a unique identifier of the chemical structure, "SMILES" as the string format of storage and exchange of the chemical structure, "Source" as the upstream data source from which the data was retrieved, "ChEMBL ID" as the ChEMBL identifier if the compound comes from ChEMBL database, "PubChem CID" as the PubChem compound identifier if the compound comes from PubChem database, "pIC50" as the negative logarithm of the half-maximal inhibitory concentration (IC50) to describe the potency of the compound, and "USED_AS" column specifying whether the compound was used for training or validation.


Upon usage, please cite this publication:



Issar Arab, Kristof Egghe, Kris Laukens, Ke Chen, Khaled Barakat, Wout Bittremieux, Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction, Journal of Chemical Information and Modeling, (2023). doi:10.1021/acs.jcim.3c01301
DOI: 10.5683/sp2/zggyvo
title: FlightRadar24 Aviation Density for 2019/20

description: This dataset contains gridded monthly-mean aviation densities (in flights/sqkm) for high-altitude (z&gt;18000ft) flights from Jan-July 2019 and 2020, as well as the script used to produce these files from proprietary FlightRadar24 files.
DOI: 10.6073/pasta/06559231aa04fd7fecd661f107985c8f
title: Leaf area index (LAI) recorded from a nitrogen (N), phosphorus (P) and N+P fertilization experiment at the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2016-2019 growing seasons

description: This file contains leaf area index (LAI) measurements from an nitrogen (N), phosphorus (P) and N+P fertilization experiment established in the southern section of the 2007 Anaktuvuk River fire in 2016. LAI was recorded using a handheld plant canopy analyzer (LI-COR 2200C; LI-COR, Lincoln, NE, USA) Data spans 4 years from 2016 (when fertilization began) until 2019. Data was recorded once a year at the peak of each growing season.
DOI: 10.6073/pasta/2b4df9a8b2609490f8dfbd7e821ee04f
title: Hubbard Brook Experimental Forest: Droughtnet soil moisture, temperature and soil water potential

description: The forest drought experiment prototype at Hubbard Brook was
 constructed in 2015, as part of the International Drought Experiment
 (IDE) coordinated by the DroughtNet Research Coordination Network. The
 throughfall exclusion experiment was designed to simulate a
 1-in-100-year drought during an average precipitation year by
 diverting ~50% of forest throughfall from each treatment plot starting
 in May 2015 (Asbjornsen et al., 2018). Throughfall was intercepted by
 reinforced polyethylene troughs and diverted passively to the
 downslope side of each plot. Each throughfall exclusion plot was 15 x
 15 meters in area. TFE plots were designated with the labels 7 and 8
 to avoid any confusion with the nearby CCASE plots (which are labeled
 1-6). Plots were not trenched to isolate them from the surrounding
 soil.  In May 2019 throughfall removal was increased to approximately
 95% (i.e. full coverage but with stemflow not fully excluded).
 Throughfall exclusion treatments ended in February 2020. Recovery and
 return to baseline conditions were monitored during 2020 (when a
 natural drought occurred) and 2021 (a more normal year).

 
 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.
DOI: 10.6073/pasta/81868b65c853d5eb2052d9f1a8397d0d
title: Characterization of burned and unburned moist acidic tundra sites for estimating C and N loss from the 2007 Anaktuvuk River Fire, sampled in 2008.

description: Burned and unburned moist acidic tundra sites used to estimate C and N loss from the Anaktuvuk River Fire (2007). These sites were sampled in summer of 2008. Unburned sites were used to develop a method for estimating soil organic layer depth and plant biomass, and for determining the characteristics of unburned soil organic layers. In burned sites, we characterized residual organic soils and used biometric measurements of tussocks to reconstruct pre-fire soil organic layer depth. Together, these measurements were used to reconstruct pre-fire soil and plant carbon and nitrogen pools and estimate ecosystem losses of these elements during the fire.
DOI: 10.1594/pangaea.96559
title: Irradiance at station A1/47A

description: Plymouth Marine Laboratory. Authorship was originally "BODC" and was changed by request of the BODC.
DOI: 10.18462/iir.icr.2015.1003
title: Hitting the bounds of chemistry: limits and tradeoffs for low-GWP refrigerants.

description: We explore the possibilities for low-GWP refrigerants through two approaches. First, we explore the performance of hypothetical refrigerants to define the limits of what is allowed by thermodynamics. In the second approach, we apply property estimation methods and screening parameters to identify best candidates from a database of 100 million chemicals. We find that the optimal thermodynamic characteristics are quite rare; additional fluids are eliminated because they are unstable or highly toxic. We then estimate the cycle performance (considering the basic cycle, economizer cycle, and a cycle with a liquid-line/suction-line heat exchanger) of the remaining candidates and apply efficiency as an additional screening criterion. The end result is not thousands of low-GWP candidates, but a handful, and even these present tradeoffs. We argue that these represent all the viable candidates for single-component, medium- and high-pressure refrigerants — in other words, we have hit the limits of what chemistry allows.
DOI: 10.25345/c55v3t
title: MassIVE MSV000086749 - WDR76-SPIN1_SCAP-E2-XL_mzTab and mgf
title: SCAP-XL of Halo-WDR76 and SNAP-SPIN1 from stable cell line in WDR76 KO background. DSSO crosslinked in E2 sample.
DOI: 10.25345/c5b79f
title: MassIVE MSV000086802 - WDR76-SPIN1_inWDR76KO_SCAP-MS_MudPIT
title: Serial Capture Affinity Purification (SCAP) samples analyzed using Multidimentional Protein identification Technology (MudPIT) on Orbitrap Velos Elite. Baits are SNAP-SPIN1 and Halo-WDR76. SNAP purification was performed first, then Halo purification was performed. SCAP was performed using whole cell extract from a stable cell line in WDR76 KO background. 3 biological replicates of E1, UB2 and E2 fractions.
DOI: 10.25345/c5cz1z
title: MassIVE MSV000086849 - Halo-WDR76_Purification_WDR76KObackground_MudPIT_wPTMs
title: Halo purification samples analyzed using Multidimentional Protein identification Technology (MudPIT) on Orbitrap Velos Elite. Baits Halo-WDR76. Purification was performed using whole cell extract from a stable cell line of 293FRT cells in WDR76 KO background. Control purification was performed on blank WDR76 KO293FRT cells. 3 biological replicates of control and Halo-WDR76.
DOI: 10.25345/c5s50c
title: MassIVE MSV000086846 - Halo-Control/WDR76/SPIN1/CBX5_purification_MudPIT_wPTMs
title: Halo purification samples analyzed using Multidimentional Protein identification Technology (MudPIT) on Orbitrap Velos Elite. Baits are Halo-WDR76, Halo-SPIN1, Halo-CBX5. The bait for Control Purification is Halo tag only. Purification for each bait was performed using whole cell extract from a stable cell line in 293FRT cells. 3 biological replicates for each type of bait.
DOI: 10.5281/zenodo.10960858
title: DNA-PAINT Dye Analysis Study

description: Localization Microscopy Datasets from this publication: https://dx.doi.org/10.1038/s41592-024-02374-8. Archive contains all relevant .hdf5 datasets.
DOI: 10.5281/zenodo.11066707
title: Library of Two Million Unique Small Molecules with Precalculated Fingerprints, Descriptors, and Cardiotoxicity Inhibition Data

description: This repository comprises a dataset of ~2 million unique compounds saved in an hdf5 small molecule library store, which includes the following fields for each molecule:



InChI key

Standardized SMILES string

Compound source

ChEMBL identifier if the compound exists in this open access database

1024-bit Morgan fingerprint

2048-bit Morgan fingerprint

881-bit PubChem fingerprints

854 vector-length of preprocessed and standardized Mordred descriptors

and cardiotoxicity inhibition predictions for each of the three cardiac ion channels (hERG, Nav1.5, and Cav1.2) using CtoxPred2 along with the model confidence scores.


The repository also includes a Jupyter notebook that serves as an initial guide for querying the small molecule library store. Export both files to the same folder, allocate approximately 40 GB of available memory disk space, unzip the library store, and then launch the notebook to begin querying.

Upon usage, please cite this publication:



Issar Arab, Kris Laukens, Wout Bittremieux, Semisupervised Learning to Boost hERG, Nav1.5, and Cav1.2 Cardiac Ion Channel Toxicity Prediction by Mining a Large Unlabeled Small Molecule Data Set, Journal of Chemical Information and Modeling, (2024). doi:10.1021/acs.jcim.4c01102