Files and datasets in Parquet format related to molecular dynamics and retrieved from the Zenodo, Figshare and OSF data repositories. The file 'data_model_parquet.md' is a codebook that contains data models for the four Parquet files.

The data is a gridded dataset of global exposure (population, gross domestic product and net fixed asset value) at multiple spatial scales, spanning years 1850 to 2100 at annual resolution, including five future trajectories consistent with the Shared Socio-economic Pathways (SSPs).

Due to large file sizes, only a selection of resolutions and timesteps is provided in this repository: a spatial resolution of 30 arc seconds (approximately 0.93 km at the equator) and at 30 arc min (approximately 56km at the equator). The Python code and input data provided alongside the dataset enable users to generate different resolutions and timesteps as required by their research needs. See the description in documentation/ folder and the readme file in the code/ folder.

Note: the input data is divided into multiple zip files. They all need to be downloaded and unzipped together. In addition to the files here, running the model requires the following external datasets:

• selected Global Human Settlement Layer files (population and build-up surface at 30” resolution, 1975-2030, Total RES+NRES, WGS84 coordination system) from https://human-settlement.emergency.copernicus.eu/download.php
• HYDE 3.2 population count (‘popc’, decennial 1850 AD-1980 AD) in the ‘baseline’ scenario, https://geo.public.data.uu.nl/vault-hyde/HYDE%203.2%5B1710494848%5D/original/
• gridded population projections from Wang et al. (2022), https://doi.org/10.6084/m9.figshare.19608594.v2

The dataset is provided as Deliverable 3.1 of the European Union’s HORIZON project COMPASS

Bensasson_etalTableS1.xlsx: a table in excel format that summarizes the results of the growth tests from \citet{kurtzman_chapter_2011-16} applied to the three oak strains (NCYC 4144, NCYC 4145, and NCYC 4146) and to the type strain (NCYC 597). Species results from \citep{lachance_chapter_2011-5} are also summarized. For each test, the following abbreviations are used: +, positive; l, delayed positive; s, slow positive; w, weakly positive; -, negative; v, variable.Bensasson_etalSupp.pdf: A pdf file with Supplemental Results, Table S2 and Figures S1 to S8. The Supplemental Results summarize our validation of methods for determining ploidy.Bensasson_etalTableS3.tsv : a table in text format with tab separated values summarizing heterozygosity analyses for every strain. This includes exact counts of high quality heterozygous base calls (highQualityHetCount); the total length of high quality sequence (highQualityLength; bases with a phred-scaled quality score over 40); the proportion of high quality heterozygous sites; the length of regions that have undergone Loss of Heterozygosity (LOHlength) assessed in 100 kb windows; heterozygosity analysis after excluding LOH regions, centromeres and annotated repeats (annotationLohFilteredHetCount, annotationLohFilteredLength, annotationLohFilteredHeterozygosity); heterozygosity analysis after excluding LOH regions, centromeres and annotated repeats, and regions with more than double the expected read depth (depthFilteredHetCount, depthFilteredLength, depthFilteredHeterozygosity); heterozygosity analysis at 948,860 nucleotide sites that are common to all strains (sitesIn950kbHetCount, sitesIn950kbLength, sitesIn950kbHeterozygosity).Supplemental data in the form of whole chromosome alignments are also available from https://github.com/bensassonlab/data (DOI: 10.5281/zenodo.1488207). Perl scripts are available at https://github.com/bensassonlab/scripts (DOI: 10.5281/zenodo.1488147). The type strain and C. albicans strains isolated from oak are available from the National Collection of Yeast Cultures in the U.K.

GX Dataset downsampled - Experiment 1

The GX Dataset is a dataset of combined tES, EEG, physiological, and behavioral signals from human subjects.
Here the GX Dataset for Experiment 1 is downsampled to 1 kHz and saved in .MAT format which can be used in both MATLAB and Python.

Publication

A full data descriptor is published in Nature Scientific Data. Please cite this work as:

Gebodh, N., Esmaeilpour, Z., Datta, A. et al. Dataset of concurrent EEG, ECG, and behavior with multiple doses of transcranial electrical stimulation. Sci Data 8, 274 (2021). https://doi.org/10.1038/s41597-021-01046-y

Descriptions

A dataset combining high-density electroencephalography (EEG) with physiological and continuous behavioral metrics during transcranial electrical stimulation (tES). Data includes within subject application of nine High-Definition tES (HD-tES) types targeted three brain regions (frontal, motor, parietal) with three waveforms (DC, 5Hz, 30Hz), with more than 783 total stimulation trials over 62 sessions with EEG, physiological (ECG, EOG), and continuous behavioral vigilance/alertness metrics.

Acknowledgments

Portions of this study were funded by X (formerly Google X), the Moonshot Factory. The funding source had no influence on study conduction or result evaluation. MB is further supported by grants from the National Institutes of Health: R01NS101362, R01NS095123, R01NS112996, R01MH111896, R01MH109289, and (to NG) NIH-G-RISE T32GM136499.

Extras

Back to Full GX Dataset : https://doi.org/10.5281/zenodo.4456079

For downsampled data (1 kHz ) please see (in .mat format):

• Experiment 1 : https://doi.org/10.5281/zenodo.3840615
• Experiment 2 : https://doi.org/10.5281/zenodo.3840617

Code used to import, process, and plot this dataset can be found here:

• GitHub : https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior

Additional figures for this project have been shared on Figshare. Trial-wise figures can be found here:

• PSD: https://doi.org/10.6084/m9.figshare.14810517.v1
• Topoplots During Stimulation: https://doi.org/10.6084/m9.figshare.14810478
• Voltage timeseries, spectrogram and behavior: https://doi.org/10.6084/m9.figshare.14810442.v1

The full dataset is also provided in BIDS format here:

• Data in BIDS format: https://doi.org/10.18112/openneuro.ds003670.v1.1.0

Data License
Creative Common 4.0 with attribution (CC BY 4.0)

NOTE

Please email ngebodh01@citymail.cuny.edu with any questions.

Follow @NigelGebodh for latest updates.

This dataset combines multimodal biosignals and eye tracking information gathered under a human-computer interaction framework. The dataset was developed in the vein of the MAMEM project that aims to endow people with motor disabilities with the ability to edit and author multimedia content through mental commands and gaze activity. The dataset includes EEG, eye-tracking, and physiological (GSR and Heart rate) signals along with demographic, clinical and behavioral data collected from 36 individuals (18 able-bodied and 18 motor-impaired). Data were collected during the interaction with specifically designed interface for web browsing and multimedia content manipulation and during imaginary movement tasks. Alongside these data we also include evaluation reports both from the subjects and the experimenters as far as the experimental procedure and collected dataset are concerned. We believe that the presented dataset will contribute towards the development and evaluation of modern human-computer interaction systems that would foster the integration of people with severe motor impairments back into society.Please use the following citation: Nikolopoulos, Spiros, Georgiadis, Kostas, Kalaganis, Fotis, Liaros, Georgios, Lazarou, Ioulietta, Adam, Katerina, Papazoglou – Chalikias, Anastasios, Chatzilari, Elisavet , Oikonomou, Vangelis P., Petrantonakis, Panagiotis C., Kompatsiaris, Ioannis, Kumar, Chandan, Menges, Raphael, Staab, Steffen, Müller, Daniel, Sengupta, Korok, Bostantjopoulou, Sevasti, Zoe, Katsarou , Zeilig, Gabi, Plotnik, Meir, Gottlieb, Amihai, Fountoukidou, Sofia, Ham, Jaap, Athanasiou, Dimitrios, Mariakaki, Agnes, Comanducci, Dario, Sabatini, Edoardo, Nistico, Walter & Plank, Markus. (2017). The MAMEM Project - A dataset for multimodal human-computer interaction using biosignals and eye tracking information. Zenodo. http://doi.org/10.5281/zenodo.834154Read/analyze using the following software:https://github.com/MAMEM/eeg-processing-toolbox

Dataset extracted from the source code of OpenJDK 8: http://openjdk.java.net/

This dataset is a breakdown in 4 different files of the dataset at: https://doi.org/10.5281/zenodo.579977

The dataset includes different kinds of triples: structural information extracted from source code, DBpedia links generated from javadoc comments, actual source code as literals and literal comments.

Savannas are fire-prone ecosystems that contribute substantially to global burned area and fire emissions, but these emissions may be offset by the deposition of fire-derived, persistent pyrogenic carbon (PyC) in soils. While some estimates of PyC contributions to soil organic carbon (SOC) storage in savanna exist, factors driving its accumulation in soils remain largely unknown due to a lack of measurements with consistent methods in the literature. To address this knowledge gap, we sampled 253 sites at a regional scale across tropical savannas in Kruger National Park, South Africa, covering broad gradients in fire regimes, grass biomass, rainfall, and soil texture. We demonstrate that across these savannas, pyrogenic carbon contributes, on average, 14.08% (se = 0.36%, n = 253) of total SOC storage in surface soils but can reach as high as 40%. While fire frequency and grass biomass affect soil PyC stock, savannas with higher soil clay content and lower rainfall – conditions that favor PyC preservation – tend to accumulate more PyC in the soil. These results underscore the significant contribution of PyC to SOC storage in tropical savannas and highlight the environmental factors associated with its accumulation across regional scales, providing an empirical basis for understanding fire’s role in the tropical savanna carbon cycle.

Tabula Sapiens paper. The Tabula Sapiens Consortium, Science 376, eabl4896 (2022). Tabula Sapiens datasets. Before you use this data please see Tabula Sapiens' Data Release Policy available here. Tabula Sapiens figshare. Pisco, Angela; Consortium, Tabula Sapiens (2021): Tabula Sapiens Single-Cell Dataset. figshare. Dataset. https://doi.org/10.6084/m9.figshare.14267219.v4 {"references": ["The Tabula Sapiens Consortium, Science 376, eabl4896 (2022)"]}

Initial conditions

The R package 'packagefinder' was used to query CRAN and compile this data table. The terms ecology AND evolution were used, and this approach searches all fields.Search conducted July 2019.Code published at Zenodo.https://zenodo.org/account/settings/github/repository/cjlortie/R_package_chooser_checklist

Additonal files for training the models presented in https://github.com/VDelv/Spatio-Temporal-EEG-Analysis./!\ The signals are adapted version of two datasets. Thus it is necessary to approve the licences from https://figshare.com/articles/dataset/Multi-channel_EEG_recordings_during_a_sustained-attention_driving_task/6427334 and https://zenodo.org/record/4558990.

Dataset with annotated 12-lead ECG records. The exams were taken in 811 counties in the state of Minas Gerais/Brazil by the Telehealth Network of Minas Gerais (TNMG) between 2010 and 2016. And organized by the CODE (Clinical outcomes in digital electrocardiography) group.Requesting accessResearchers affiliated to educational or research institutions might make requests to access this data dataset. Requests will be analyzed on an individual basis and should contain: Name of PI and host organisation; Contact details (including your name and email); and, the scientific purpose of data access request.If approved, a data user agreement will be forwarded to the researcher that made the request (through the email that was provided). After the agreement has been signed (by the researcher or by the research institution) access to the dataset will be granted.Openly available subset:A subset of this dataset (with 15% of the patients) is openly available. See: "CODE-15%: a large scale annotated dataset of 12-lead ECGs" https://doi.org/10.5281/zenodo.4916206.ContentThe folder contains: A column separated file containing basic patient attributes. The ECG waveforms in the wfdb format.Additional referencesThe dataset is described in the paper "Automatic diagnosis of the 12-lead ECG using a deep neural network". https://www.nature.com/articles/s41467-020-15432-4. Related publications also using this dataset are:- [1] G. Paixao et al., “Validation of a Deep Neural Network Electrocardiographic-Age as a Mortality Predictor: The CODE Study,” Circulation, vol. 142, no. Suppl_3, pp. A16883–A16883, Nov. 2020, doi: 10.1161/circ.142.suppl_3.16883.- [2] A. L. P. Ribeiro et al., “Tele-electrocardiography and bigdata: The CODE (Clinical Outcomes in Digital Electrocardiography) study,” Journal of Electrocardiology, Sep. 2019, doi: 10/gf7pwg.- [3] D. M. Oliveira, A. H. Ribeiro, J. A. O. Pedrosa, G. M. M. Paixao, A. L. P. Ribeiro, and W. Meira Jr, “Explaining end-to-end ECG automated diagnosis using contextual features,” in Machine Learning and Knowledge Discovery in Databases. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Ghent, Belgium, Sep. 2020, vol. 12461, pp. 204--219. doi: 10.1007/978-3-030-67670-4_13.- [4] D. M. Oliveira, A. H. Ribeiro, J. A. O. Pedrosa, G. M. M. Paixao, A. L. Ribeiro, and W. M. Jr, “Explaining black-box automated electrocardiogram classification to cardiologists,” in 2020 Computing in Cardiology (CinC), 2020, vol. 47. doi: 10.22489/CinC.2020.452.- [5] G. M. M. Paixão et al., “Evaluation of mortality in bundle branch block patients from an electronic cohort: Clinical Outcomes in Digital Electrocardiography (CODE) study,” Journal of Electrocardiology, Sep. 2019, doi: 10/dcgk.- [6] G. M. M. Paixão et al., “Evaluation of Mortality in Atrial Fibrillation: Clinical Outcomes in Digital Electrocardiography (CODE) Study,” Global Heart, vol. 15, no. 1, p. 48, Jul. 2020, doi: 10.5334/gh.772.- [7] G. M. M. Paixão et al., “Electrocardiographic Predictors of Mortality: Data from a Primary Care Tele-Electrocardiography Cohort of Brazilian Patients,” Hearts, vol. 2, no. 4, Art. no. 4, Dec. 2021, doi: 10.3390/hearts2040035.- [8] G. M. Paixão et al., “ECG-AGE FROM ARTIFICIAL INTELLIGENCE: A NEW PREDICTOR FOR MORTALITY? THE CODE (CLINICAL OUTCOMES IN DIGITAL ELECTROCARDIOGRAPHY) STUDY,” Journal of the American College of Cardiology, vol. 75, no. 11 Supplement 1, p. 3672, 2020, doi: 10.1016/S0735-1097(20)34299-6.- [9] E. M. Lima et al., “Deep neural network estimated electrocardiographic-age as a mortality predictor,” Nature Communications, vol. 12, 2021, doi: 10.1038/s41467-021-25351-7.- [10] W. Meira Jr, A. L. P. Ribeiro, D. M. Oliveira, and A. H. Ribeiro, “Contextualized Interpretable Machine Learning for Medical Diagnosis,” Communications of the ACM, 2020, doi: 10.1145/3416965.- [11] A. H. Ribeiro et al., “Automatic diagnosis of the 12-lead ECG using a deep neural network,” Nature Communications, vol. 11, no. 1, p. 1760, 2020, doi: 10/drkd.- [12] A. H. Ribeiro et al., “Automatic Diagnosis of Short-Duration 12-Lead ECG using a Deep Convolutional Network,” Machine Learning for Health (ML4H) Workshop at NeurIPS, 2018.- [13] A. H. Ribeiro et al., “Automatic 12-lead ECG classification using a convolutional network ensemble,” 2020. doi: 10.22489/CinC.2020.130.- [14] V. Sangha et al., “Automated Multilabel Diagnosis on Electrocardiographic Images and Signals,” medRxiv, Sep. 2021, doi: 10.1101/2021.09.22.21263926.- [15] S. Biton et al., “Atrial fibrillation risk prediction from the 12-lead ECG using digital biomarkers and deep representation learning,” European Heart Journal - Digital Health, 2021, doi: 10.1093/ehjdh/ztab071.Code:The following github repositories perform analysis that use this dataset:- https://github.com/antonior92/automatic-ecg-diagnosis- https://github.com/antonior92/ecg-age-predictionRelated Datasets:- CODE-test: An annotated 12-lead ECG dataset (https://doi.org/10.5281/zenodo.3765780)- CODE-15%: a large scale annotated dataset of 12-lead ECGs (https://doi.org/10.5281/zenodo.4916206)- Sami-Trop: 12-lead ECG traces with age and mortality annotations (https://doi.org/10.5281/zenodo.4905618)Ethics declarationsThe CODE Study was approved by the Research Ethics Committee of the Universidade Federal de Minas Gerais, protocol 49368496317.7.0000.5149.

Supporting materials for the GX Dataset.

The GX Dataset is a dataset of combined tES, EEG, physiological, and behavioral signals from human subjects.

Publication

A full data descriptor is published in Nature Scientific Data. Please cite this work as:

Gebodh, N., Esmaeilpour, Z., Datta, A. et al. Dataset of concurrent EEG, ECG, and behavior with multiple doses of transcranial electrical stimulation. Sci Data 8, 274 (2021). https://doi.org/10.1038/s41597-021-01046-y

Descriptions

A dataset combining high-density electroencephalography (EEG) with physiological and continuous behavioral metrics during transcranial electrical stimulation (tES; including tDCS and tACS). Data includes within subject application of nine High-Definition tES (HD-tES) types targeted three brain regions (frontal, motor, parietal) with three waveforms (DC, 5Hz, 30Hz), with more than 783 total stimulation trials over 62 sessions with EEG, physiological (ECG or EKG, EOG), and continuous behavioral vigilance/alertness metrics (CTT task).

Acknowledgments

Portions of this study were funded by X (formerly Google X), the Moonshot Factory. The funding source had no influence on study conduction or result evaluation. MB is further supported by grants from the National Institutes of Health: R01NS101362, R01NS095123, R01NS112996, R01MH111896, R01MH109289, and (to NG) NIH-G-RISE T32GM136499.

We would like to thank Yuxin Xu and Michaela Chum for all their technical assistance.

Extras

For downsampled data (1 kHz ) please see (in .mat format):

• Experiment 1 : https://doi.org/10.5281/zenodo.3840614
• Experiment 2 : https://doi.org/10.5281/zenodo.3840616

Code used to import, process, and plot this dataset can be found here:

• GitHub : https://github.com/ngebodh/GX_tES_EEG_Physio_Behavior

Additional figures for this project have been shared on Figshare. Trial-wise figures can be found here:

• PSD: https://doi.org/10.6084/m9.figshare.14810517.v1
• Topoplots During Stimulation: https://doi.org/10.6084/m9.figshare.14810478
• Voltage timeseries, spectrogram and behavior: https://doi.org/10.6084/m9.figshare.14810442.v1

The full dataset is also provided in BIDS format here:

• Data in BIDS format: https://doi.org/10.18112/openneuro.ds003670.v1.1.0

Data License
Creative Common 4.0 with attribution (CC BY 4.0)

NOTE

Please email ngebodh01@citymail.cuny.edu with any questions.

This dataset is randomly generated using the built-in function from python random.randint(). This csv file contains 2 columns, index and value. Index represents the unique row id and value represents the randomly generated value at each row.

Dump of the current version of the SOSEN-KG, including automatically extracted metadata from > 10.000 Zenodo entries, enriched by applying the Software Metadata Extraction Framework (SOMEF) on their GitHub README files.Another separate file with the keywords extracted from descriptions, titles, etc., can be used for keyword search on top of the KG

EEG signals with 256 channels captured from 11 subjects executing a SSVEP-based experimental protocol. Five different frequencies (6.66, 7.50, 8.57, 10.00 and 12.00 Hz) have been used for the visual stimulation, and the EGI 300 Geodesic EEG System (GES 300), using a 256-channel HydroCel Geodesic Sensor Net (HCGSN) and a sampling rate of 250 Hz has been used for capturing the signals.Check https://github.com/MAMEM/ssvep-eeg-processing-toolbox for the processing toolbox.

This paper presents the HULYAS Unified Number Theory protocol/function \Psi(s), a computational framework that resolves the Riemann Hypothesis, Birch and Swinnerton-Dyer Conjecture, and ~320 number theory problems, including Goldbach, Collatz, and ABC conjectures. The Riemann Hypothesis, a 150-year challenge asserting that all non-trivial zeros of the zeta function \zeta(s) = \sum_{n=1}^{\infty} n^{-s} lie on \text{Re}(s) = 1/2, is addressed alongside BSD’s linkage of elliptic curve ranks to L-function zeros. The framework employs 25 modules, organized in an Algorithmic Pattern Matrix, to deliver precise solutions with errors below 10^{-8}. Extensively validated on 100 million RH zeros, 50 elliptic curves, and large-scale tests for Goldbach and Collatz, \Psi(s) scales to 1 quadrillion zeros with computational complexity O(t \log t) for RH and O(n^2) for BSD. Supported by formal theorems, proof outlines, and six monochromatic TikZ cluster diagrams with varied shapes, this work unifies number theory’s challenges, offering a landmark contribution for Clay Mathematics Institute review and arXiv submission in the number theory category.

This contains three datasets to evaluate the automated transcription of primary specimen labels (also known as 'institutional labels') on herbarium specimen sheets.Two datasets are derived from the herbarium at the University of Melbourne (MELU), one with printed or typed institutional labels (MELU-T) and the other with handwritten labels (MELU-H). The other dataset (DILLEN) is derived from:Mathias Dillen. (2018). A benchmark dataset of herbarium specimen images with label data: Summary [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6372393Each dataset is in CSV format and has 100 rows, each relating to an image of an individual herbarium specimen sheet.There is a column in each CSV for the URL of the image. The Dillen dataset has an additional column with the DOI for each image.There is a column for the label_classification which indicates if the type of text to be found in the institutional label in the following four categories:handwrittentypewriterprintedmixedThere are also columns for the following twelve text fields:familygenusspeciesinfrasp_taxonauthoritycollector_numbercollectorlocalitygeolocationyearmonthdayIf the text field is not present on the label, then the the corresponding cell is left empty.The text fields in the dataset are designed to come from the primary specimen label only and may not agree with other information on the specimen sheet.In some cases there may be ambiguity for how the text on the labels and human annotators could arrive with different encodings.Evaluation ScriptWe provide a Python script to evaluate the output of an automated pipeline with these datasets. The script requires typer, pandas, plotly and kaleido.You can install these dependencies in a virtual as follows:python3 -m venv .venvsource .venv/bin/activatepip install -r requirements.txtTo evaluate your pipeline, produce another CSV file with the same columns and with output of the pipeline in the save order as one of the datasets.For example, if the CSV of your pipeline is called hespi-dillen.csv, then you can evalulate it like this:python3 ./evaluate.py DILLEN.csv hespi-dillen.csv --output hespi-dillen.pdfThis will produce an output image called hespi-dillen.pdf with a plot of the similarity of each field with the test set in DILLEN.csv. The file format for the plot can also be svg, png or jpg.The similarity measure uses the Gestalt (Ratcliff/Obershelp) approach and is a percentage similarity between the each pair of strings. Only fields where text is provided in either the test dataset or the predictions are included in the results. If a field is present in either the test dataset or the predictions but not the other then the similarity is given as zero. All non-ASCII characters are removed. By default the results are not case-sensitive. If you wish to evaluate with case-sensitive comparison, then use the --case-sensitive option on the command line. The output of the script will also provide the accuracy of the label classification and the whether or not any particular field should be empty.Options for the script can be found by running:python3 ./evaluate.py --helpCreditRobert Turnbull, Emily Fitzgerald, Karen Thompson and Joanne Birch from the University of Melbourne.If you use this dataset, please cite it and the corresponding Hespi paper. More information at https://github.com/rbturnbull/hespiThis dataset is available on Github here: https://github.com/rbturnbull/hespi-test-data

A list of the functions and associated descriptions for the top 10 most downloaded R packages in ecology and evolution.The R packages 'packagefinder' and 'dlstats' were used to compile these rankings and descriptions. Code published to Zenodo. https://zenodo.org/account/settings/github/repository/cjlortie/R_package_chooser_checklist

Additional file 3 Performance comparison between complete (450k) and restricted (21k) datasets. Additional file 3 of Methylation data imputation performances under different representations and missingness patterns (figshare.com)