cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7694896
title: Hominid Palaeoproteomic Reference Dataset

description: This dataset contains the 'Hominid Palaeoproteomic Reference Dataset'. We used PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline ) to generate a palaeoproteomic reference dataset of protein sequences from ancient and present-day hominids. Using the first two modules of PaleoProPhyler, we translated 195 publicly available whole genomes from extant hominid groups. Details on the processing of the sequences can be found in the supplementary materials of PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline/blob/main/GitHub_Tutorial/Supplementary.pdf ). We also translated 8 ancient hominin genomes from VCF files, including those of several Neanderthals and one Denisovan. Since the dataset is tailored for palaeoproteomic analyses, we chose to translate proteins that have previously been reported as present in either teeth or bone tissue. We compiled a list of 1,696 proteins from previous works and successfully translated 1,543 of them. For each protein, both the canonical and all alternative protein coding isoforms were translated, leading to a total of 10,058 protein sequences for each individual in the dataset. Content: The zipped file contains 4 files, two fasta files as well as two additional folders: - PalaeoProPhyler_Publication_Data_for_Tree.fa contains all of the sequences used to generate the phylogenetic tree presented at PalaeoProPhylers manuscript - ALL_PROT_REFERENCE.fa contains all of the sequences generated as part of the Hominid Palaeoproteomic Reference Dataset described above - PER_PROTEIN is a folder containing one fasta file for each protein within the Hominid Palaeoproteomic Reference Dataset, each protein fasta file has the sequences of all individuals for that particular protein - PER_SAMPLE is a folder containing one fasta file for each sample/individual within the Hominid Palaeoproteomic Reference Dataset, each sample fasta file has the sequences of all proteins for that particular sample. Important Note: The dataset is still not <em>fully </em>generated yet. However around 108 samples are complete and ready to be used in their appropriate folder (~/PER_SAMPLE /).
DOI: 10.5281/zenodo.7694978
title: Hominid Palaeoproteomic Reference Dataset

description: This dataset contains the 'Hominid Palaeoproteomic Reference Dataset'. We used PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline ) to generate a palaeoproteomic reference dataset of protein sequences from ancient and present-day hominids. Using the first two modules of PaleoProPhyler, we translated 195 publicly available whole genomes from extant hominid groups. Details on the processing of the sequences can be found in the supplementary materials of PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline/blob/main/GitHub_Tutorial/Supplementary.pdf ). We also translated 8 ancient hominin genomes from VCF files, including those of several Neanderthals and one Denisovan. Since the dataset is tailored for palaeoproteomic analyses, we chose to translate proteins that have previously been reported as present in either teeth or bone tissue. We compiled a list of 1,696 proteins from previous works and successfully translated 1,543 of them. For each protein, both the canonical and all alternative protein coding isoforms were translated, leading to a total of 10,058 protein sequences for each individual in the dataset. Content: The zipped file contains 4 files, two fasta files as well as two additional folders: - PalaeoProPhyler_Publication_Data_for_Tree.fa contains all of the sequences used to generate the phylogenetic tree presented at PalaeoProPhylers manuscript - ALL_PROT_REFERENCE.fa contains all of the sequences generated as part of the Hominid Palaeoproteomic Reference Dataset described above - PER_PROTEIN is a folder containing one fasta file for each protein within the Hominid Palaeoproteomic Reference Dataset, each protein fasta file has the sequences of all individuals for that particular protein - PER_SAMPLE is a folder containing one fasta file for each sample/individual within the Hominid Palaeoproteomic Reference Dataset, each sample fasta file has the sequences of all proteins for that particular sample. Important Note: The dataset is still not <em>fully </em>generated yet. However around 170 modern samples are complete and ready to be used in their appropriate folder (~/PER_SAMPLE /).
DOI: 10.5281/zenodo.7695164
title: Hominid Palaeoproteomic Reference Dataset

description: This dataset contains the 'Hominid Palaeoproteomic Reference Dataset'. We used PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline ) to generate a palaeoproteomic reference dataset of protein sequences from ancient and present-day hominids. Using the first two modules of PaleoProPhyler, we translated 195 publicly available whole genomes from extant hominid groups. Details on the processing of the sequences can be found in the supplementary materials of PaleoProPhyler ( https://github.com/johnpatramanis/Proteomic_Pipeline/blob/main/GitHub_Tutorial/Supplementary.pdf ). We also translated 8 ancient hominin genomes from VCF files, including those of several Neanderthals and one Denisovan. Since the dataset is tailored for palaeoproteomic analyses, we chose to translate proteins that have previously been reported as present in either teeth or bone tissue. We compiled a list of 1,696 proteins from previous works and successfully translated 1,543 of them. For each protein, both the canonical and all alternative protein coding isoforms were translated, leading to a total of 10,058 protein sequences for each individual in the dataset. Content: The zipped file contains 4 files, two fasta files as well as two additional folders: - PalaeoProPhyler_Publication_Data_for_Tree.fa contains all of the sequences used to generate the phylogenetic tree presented at PalaeoProPhylers manuscript - ALL_PROT_REFERENCE.fa contains all of the sequences generated as part of the Hominid Palaeoproteomic Reference Dataset described above - PER_PROTEIN is a folder containing one fasta file for each protein within the Hominid Palaeoproteomic Reference Dataset, each protein fasta file has the sequences of all individuals for that particular protein - PER_SAMPLE is a folder containing one fasta file for each sample/individual within the Hominid Palaeoproteomic Reference Dataset, each sample fasta file has the sequences of all proteins for that particular sample. Important Note: The dataset is still not <em>fully </em>generated yet. However around 170 modern samples are complete and ready to be used in their appropriate folder (~/PER_SAMPLE /).
DOI: 10.6084/m9.figshare.20496234
title: BCG dataset

description: BCG dataset: The bed based ballistocardiography dataset collected and shared by Carlson et al. [3]. Both ABP and PPG signals were recorded from 40 subjects, with one record per subject. The original sampling rate of the signals is 1000 Hz. We resampled them at 125 Hz to be consistent with the previous data sets. Then, we multiplied the ABP signals by a factor of 100 since they are scaled at 100 mmHg/volt in the downloaded dataset. After preprocessing, there are 2559 segments left, which is around 4 hours of recording. To sum up, the BCG dataset is a small to medium-sized set with less data variation given its low number of subjects; its remarkably high ratio of segments per subject (63.97); and a narrower BP distribution. We applied a 5-fold CV to partition the data. [3] Carlson, C. et al. Bed-based ballistocardiography dataset (2020). IEEE Dataport https://dx.doi.org/10.21227/77hc-py84.
DOI: 10.6084/m9.figshare.20496240
title: PPGBP dataset

description: PPGBP dataset: The short-recorded PPG dataset collected and shared by Liang et al. [4]. It contains 219 subjects, with one BP reading followed by three PPG segments with a duration of 2.1 seconds each. Thus, it is the smallest set in the number of segments (613) but with a relatively high number of subjects. The original sampling frequency of 1000 Hz was resampled at 125 Hz. We conducted a 5-fold CV to partition the data once it was preprocessed. [4] Liang, Y., Chen, Z., Liu, G. & Elgendi, M. PPG-BP database (2018). Figshare https://doi.org/10.6084/m9.figshare.5459299.
DOI: 10.6084/m9.figshare.20496243
title: Sensors dataset

description: Sensors dataset: A subset from the MIMIC-III Waveform Database Matched Subset provided by Aguirre et al. [1]. There were 5821 PPG and Arterial Blood Pressure (ABP) records of 1196 subjects, with a sampling rate of 125 Hz from the downloaded dataset. The authors kept only two 15 s segments spaced 5 min apart per record. We preprocessed the Sensors dataset with our data preparation pipeline, resulting in 9013 segments, which is approximately 13 hours of recording. As a result, the Sensors dataset has a medium to large number of segments and subjects have a high sample variability, a decent ratio of segments per subject (7.70), and a discrete data segmentation. Later, we applied a 5-fold Cross Validation (CV) to partition the data. [1] Aguirre, N., Grall-Maës, E., Cymberknop, L. J. & Armentano, R. L. Dataset corresponding to "blood pressure morphology assessment from photoplethysmogram and demographic information using deep learning with attention mechanism" (2021). Zenodo https://doi.org/10.5281/zenodo.4598938.
DOI: 10.6084/m9.figshare.20496258
title: UCI dataset

description: The Cuff-Less Blood Pressure Estimation Dataset [2] from the UCI Machine Learning Repository. It is a subset of the MIMIC-II Waveform Dataset that contains 12000 records of simultaneous PPG and ABP from 942 patients with a sampling rate of 125 Hz. The 12000 records were uniformly split into four parts with 3000 records each. However, as the subject information is lacking, the Hold-one-out strategy was utilized to generate training, validation, and test sets once the data was preprocessed. In the end, the UCI dataset had 291,078 segments, which was around 404 hours of recording, making it substantially the biggest data set with a considerably higher ratio of continuous segments per record (32.15). [2] Kachuee, M., Kiani, M. M., Mohammadzade, H. & Shabany, M. Cuff-less blood pressure estimation data set (2015). UCI repository https://archive.ics.uci.edu/ml/datasets/Cuff-Less+Blood+Pressure+Estimation.
DOI: 10.6084/m9.figshare.7246379
title: Baltic salt marsh plant communities

description: Species composition, species functional traits and environmental characteristics of 39 Baltic salt marsh plots
DOI: 10.6084/m9.figshare.7246379.v2
title: Baltic salt marsh plant communities

description: Species composition, species functional traits and environmental characteristics of 39 Baltic salt marsh plots
DOI: 10.6084/m9.figshare.7246379.v1
title: Baltic salt marsh plant communities

description: Species composition, species functional traits and environmental characteristics of 39 Baltic salt marsh plots
DOI: 10.6084/m9.figshare.15097236
title: Census data

description: Census data used in survival analysis to determine the effect of VIE on termite survival in <em>Zootermopsis</em> termites.
DOI: 10.6084/m9.figshare.15097236.v1
title: Census data

description: Census data used in survival analysis to determine the effect of VIE on termite survival in <em>Zootermopsis</em> termites.
DOI: 10.6084/m9.figshare.22262389
title: Antennation data

description: Data on antennation occurrence during nestmate recognition trials in <em>Zootermopsis</em> termites.
DOI: 10.6084/m9.figshare.22262389.v1
title: Antennation data

description: Data on antennation occurrence during nestmate recognition trials in <em>Zootermopsis</em> termites.
DOI: 10.5258/soton/d2266
title: Dataset supporting the thesis: Identification of genetic factors associated with myeloid neoplasms

description: Supplementary tables for Chapter 2,3 and 4 of the PhD Thesis "Identification of genetic factors associated with myeloid neoplasms"
DOI: 10.6084/m9.figshare.22262383
title: Recoiling data

description: Data on recoiling occurrence during nestmate recognition trials in <em>Zootermopsis</em> termites.
DOI: 10.6084/m9.figshare.22262383.v1
title: Recoiling data

description: Data on recoiling occurrence during nestmate recognition trials in <em>Zootermopsis</em> termites.
DOI: 10.34740/kaggle/dsv/5157138
title: YouTube Daily
DOI: 10.6084/m9.figshare.22262395
title: Allogrooming data

description: Data on allogrooming occurrence during nestmate recognition trials in <em>Zootermopsis</em> termites.
DOI: 10.6084/m9.figshare.22262395.v1
title: Allogrooming data

description: Data on allogrooming occurrence during nestmate recognition trials in <em>Zootermopsis</em> termites.
DOI: 10.20375/0000-000f-8250-3
title: Codebook_KIIS_Social Protest_2009-2021.xlsx
DOI: 10.20375/0000-000f-8251-2
title: Data_converted_KIIS_Social Protest_2009-2021_eng.xlsx
DOI: 10.20375/0000-000f-8252-1
title: Data Documentation KIIS Social Protest 2009-2021.pdf
DOI: 10.20375/0000-000f-8253-0
title: Data_KIIS_Social Protest_2009-2021_eng.sav
DOI: 10.20375/0000-000f-8254-f
title: Data_KIIS_Social Protest_2009-2021_ukr.sav
DOI: 10.5281/zenodo.7688674
title: Guideline for a FAIR Cultural Studies Research Data Management

description: Dies ist die <strong>Datenpublikation</strong> (Ausgangsdateien, Abbildungen, Materialien zur Nachnutzung) für die NFDI4Culture Handreichung „Handreichung für ein FAIRes Management kulturwissenschaftlicher Forschungsdaten“. Die Online-Handreichung ist verfügbar unter https://nfdi4culture.de/go/E3625.
description: Funded by the German Research Foundation (DFG) – 441958017 (NFDI4Culture)
DOI: 10.5281/zenodo.7688673
title: Guideline for a FAIR Cultural Studies Research Data Management

description: Dies ist die <strong>Datenpublikation</strong> (Ausgangsdateien, Abbildungen, Materialien zur Nachnutzung) für die NFDI4Culture Handreichung „Handreichung für ein FAIRes Management kulturwissenschaftlicher Forschungsdaten“. Die <strong>Online-Handreichung</strong> ist verfügbar unter https://nfdi4culture.de/go/E3625. Als <strong>PDF</strong> ist diese Handreichung verfügbar unter https://doi.org/10.5281/zenodo.7716941.
description: Funded by the German Research Foundation (DFG) – 441958017 (NFDI4Culture)
DOI: 10.5281/zenodo.7689172
title: Guideline for a FAIR Cultural Studies Research Data Management

description: Dies ist die <strong>Datenpublikation</strong> (Ausgangsdateien, Abbildungen, Materialien zur Nachnutzung) für die NFDI4Culture Handreichung „Handreichung für ein FAIRes Management kulturwissenschaftlicher Forschungsdaten“. Die <strong>Online-Handreichung</strong> ist verfügbar unter https://nfdi4culture.de/go/E3625. Als <strong>PDF</strong> ist diese Handreichung verfügbar unter https://zenodo.org/record/7716941.
description: Funded by the German Research Foundation (DFG) – 441958017 (NFDI4Culture)
DOI: 10.5281/zenodo.7727986
title: Guideline for a FAIR Cultural Studies Research Data Management

description: Dies ist die <strong>Datenpublikation</strong> (Ausgangsdateien, Abbildungen, Materialien zur Nachnutzung) für die NFDI4Culture Handreichung „Handreichung für ein FAIRes Management kulturwissenschaftlicher Forschungsdaten“. Die <strong>Online-Handreichung</strong> ist verfügbar unter https://nfdi4culture.de/go/E3625. Als <strong>PDF</strong> ist diese Handreichung verfügbar unter https://doi.org/10.5281/zenodo.7716941.
description: Funded by the German Research Foundation (DFG) – 441958017 (NFDI4Culture)
DOI: 10.5281/zenodo.7728122
title: Guideline for a FAIR Cultural Studies Research Data Management

description: Dies ist die <strong>Datenpublikation</strong> (Ausgangsdateien, Abbildungen, Materialien zur Nachnutzung) für die NFDI4Culture Handreichung „Handreichung für ein FAIRes Management kulturwissenschaftlicher Forschungsdaten“. Die <strong>Online-Handreichung</strong> ist verfügbar unter https://nfdi4culture.de/go/E3625. Als <strong>PDF</strong> ist diese Handreichung verfügbar unter https://doi.org/10.5281/zenodo.7716941.
description: Funded by the German Research Foundation (DFG) – 441958017 (NFDI4Culture)
DOI: 10.6084/m9.figshare.22262404
title: data 1. demographic and professional.xls

description: Demographic and professional attributes of teachers participating in the study
DOI: 10.5281/zenodo.7728139
title: Dataset: Addressing Practical Use of Viologen-Derivatives in Redox Flow Batteries through Molecular Engineering

description: Dataset for the results shown in the publications "Addressing Practical Use of Viologen-Derivatives in Redox Flow Batteries through Molecular Engineering"
DOI: 10.5281/zenodo.7728138
title: Dataset: Addressing Practical Use of Viologen-Derivatives in Redox Flow Batteries through Molecular Engineering

description: Dataset for the results shown in the publications "Addressing Practical Use of Viologen-Derivatives in Redox Flow Batteries through Molecular Engineering"
DOI: 10.5281/zenodo.7728175
title: Comprehensive benchmark and architectural analysis of deep learning models for Nanopore sequencing basecalling

description: Placeholder data for the Lambda phage data used in: Comprehensive benchmark and architectural analysis of deep learning models for Nanopore sequencing basecalling. For the complete dataset see the Sequence Read Archive under the PRJNA926802 bioproject ID.
DOI: 10.5281/zenodo.7728174
title: Comprehensive benchmark and architectural analysis of deep learning models for Nanopore sequencing basecalling

description: Placeholder data for the Lambda phage data used in: Comprehensive benchmark and architectural analysis of deep learning models for Nanopore sequencing basecalling. For the complete dataset see the Sequence Read Archive under the PRJNA926802 bioproject ID.
DOI: 10.5281/zenodo.7716782
title: Transcriptomic profiles of resected pancreatic adenocarcinoma, whole-slide match

description: RNA was extracted from the whole-slide tumor regions of 100 pancreatic adenocarcinomas, consecutively resected at the Beaujon hospital (Clichy, FRANCE). Tumors were sequenced in two batches, using 3' RNA-sequencing for FFPE compatibility.
DOI: 10.5281/zenodo.7716696
title: Transcriptomic profiles of resected pancreatic adenocarcinoma, whole-slide match

description: RNA was extracted from the whole-slide tumor regions of 100 pancreatic adenocarcinomas, consecutively resected at the Beaujon hospital (Clichy, FRANCE). Tumors were sequenced in two batches, using 3' RNA-sequencing for FFPE compatibility.
DOI: 10.6084/m9.figshare.22262455
title: The three scales and responses

description: Responses to the three scales used in the study
DOI: 10.20375/0000-000f-8256-d
title: Codebook_KIIS_Social Protest_2009-2021.xlsx
DOI: 10.20375/0000-000f-8257-c
title: Data_converted_KIIS_Social Protest_2009-2021_eng.xlsx
DOI: 10.20375/0000-000f-8258-b
title: Data Documentation KIIS Social Protest 2009-2021.pdf
DOI: 10.20375/0000-000f-8259-a
title: Data_KIIS_Social Protest_2009-2021_eng.sav
DOI: 10.20375/0000-000f-825a-9
title: Data_KIIS_Social Protest_2009-2021_ukr.sav
DOI: 10.5523/bris.3uu4ut9x9j5sz2blxojx55ez8z
title: Data from paper "Sketching phase diagrams using low-depth variational quantum algorithms"

description: The numerical results from the paper "Sketching phase diagrams using low-depth variational quantum algorithms" by Jan Lukas Bosse, Raul Santos and Ashley Montanaro.
DOI: 10.5281/zenodo.5483834
title: Noise data for the study of consonant-in-noise discrimination using an auditory model with different speech-based decision devices

description: The data stored in this repository correspond to two sets of 5000 speech-shaped noises (SSN) that were used in the conference paper titled "Consonant-in-noise discrimination using an auditory model with different speech-based decision devices" by the same authors, presented in the DAGA conference in Vienna, Austria, on 17/08/2021. The two zip files (<strong>osses2021c_S01</strong> and <strong>osses2021c_S02</strong> for participants S01 and S02, respectively) have following structure: <strong>NoiseStim-SSN</strong>: Folder containing the 5000 noises <strong>Results</strong>: Results of the listening experiment collected using the fastACI toolbox (https://github.com/aosses-tue/fastACI). To obtain similar results for other participants the same experiment has to be run using the fastACI toolbox. For instance, to collect new data for participant 'S03', you have to input the following command in MATLAB: <pre><code class="language-bash">fastACI_experiment('speechACI_varnet2013','S03','SSN');</code></pre>
DOI: 10.5281/zenodo.5483835
title: Noise data for the study of consonant-in-noise discrimination using an auditory model with different speech-based decision devices

description: The data stored in this repository correspond to two sets of 5000 speech-shaped noises (SSN) that were used in the conference paper titled "Consonant-in-noise discrimination using an auditory model with different speech-based decision devices" by the same authors, presented in the DAGA conference in Vienna, Austria, on 17/08/2021. The two zip files (<strong>osses2021c_S01</strong> and <strong>osses2021c_S02</strong> for participants S01 and S02, respectively) have following structure: <strong>NoiseStim-SSN</strong>: Folder containing the 5000 noises <strong>Results</strong>: Results of the listening experiment collected using the fastACI toolbox (https://github.com/aosses-tue/fastACI). To obtain similar results for other participants the same experiment has to be run using the fastACI toolbox. For instance, to collect new data for participant 'S03', you have to input the following command in MATLAB: <pre><code class="language-bash">fastACI_experiment('speechACI_varnet2013','S03','SSN');</code></pre>
DOI: 10.5281/zenodo.7728197
title: Morphological and calcification analysis of Gephyrocapsa during the Mid-Brunhes

description: Characterisation of the changes in morphology and calcification of Gephyrocapsa coccoliths during the Mid-Brunhes
DOI: 10.5281/zenodo.7728196
title: Morphological and calcification analysis of Gephyrocapsa during the Mid-Brunhes

description: Characterisation of the changes in morphology and calcification of Gephyrocapsa coccoliths during the Mid-Brunhes
DOI: 10.5281/zenodo.7424467
title: Awesome Spectral Indices

description: Awesome Spectral Indices is a standardized machine-readable catalogue of spectral indices for remote sensing in Earth system research. Currently, the catalogue has 228 spectral indices grouped in 7 application domains: vegetation, water, burn, snow, urban, radar, and kernel indices. Note that radar and kernel indices represent methodological approaches. Each index in the catalogue consists of an item with 9 attributes, listed as follows: short_name: Short name (acronym) of the index. long_name: Long name (original name) of the index. application_domain: Application domain of the index (one of the 7 above-mentioned groups). formula: Formula of the index given as a standardized expression. bands: Required bands and additional parameters for the index computation. platforms: List of platforms with the required bands for the index computation. reference: Link to the index source. date_of_addition: Date of addition to the catalogue. contributor: GitHub user link of the index contributor. The catalogue is released in two formats: JSON and CSV. The JSON file follows a key-value model with the index acronym as key and the 9 attributes as value. The CSV file follows a relational model with indices as rows and the 9 attributes as columns. The two filenames are: spectral-indices-dict.json (JSON file) spectral-indices-table.csv (CSV file) In addition, the following files describing bands and constants are included: bands.json constants.json For a complete and detailed description, please see github.com/awesome-spectral-indices/awesome-spectral-indices. The dynamic GitHub repository includes the source code used to create the catalogue.