cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15214847
title: AWS Spot Price History

description: AWS Spot Price History

This dataset tracks historical prices for AWS spot prices across all regions. It is updated automatically on the 1st of each month to contain data from the previous month.

Data format

Each month of data is stored as a ZStandard-compressed .tsv.zst file.

The data format matches that returned by AWS's describe-spot-instance-prices, with the exception that availability zones have been replaced by their global ID. For instance, here are some example lines from one capture:

euc1-az2        i4i.8xlarge     Linux/UNIX      1.231800        2023-02-28T23:59:57+00:00euc1-az3        r5b.8xlarge     Red Hat Enterprise Linux        0.749600        2023-02-28T23:59:58+00:00euc1-az3        r5b.8xlarge     SUSE Linux      0.744600        2023-02-28T23:59:58+00:00euc1-az3        r5b.8xlarge     Linux/UNIX      0.619600        2023-02-28T23:59:58+00:00euc1-az3        m5n.4xlarge     Red Hat Enterprise Linux        0.476000        2023-02-28T23:59:59+00:00euc1-az2        m5n.4xlarge     Red Hat Enterprise Linux        0.492000        2023-02-28T23:59:59+00:00euc1-az3        m5n.4xlarge     SUSE Linux      0.471000        2023-02-28T23:59:59+00:00euc1-az2        m5n.4xlarge     SUSE Linux      0.487000        2023-02-28T23:59:59+00:00euc1-az3        m5n.4xlarge     Linux/UNIX      0.346000        2023-02-28T23:59:59+00:00euc1-az2        m5n.4xlarge     Linux/UNIX      0.362000        2023-02-28T23:59:59+00:00

When fetching spot instance pricing from AWS, results contain some prices from the previous month so that the price is known at the start of the month. These prices are adjusted in this dataset to be at the exact start of the month UTC:

euw3-az2        g4dn.4xlarge    Linux/UNIX      0.558600        2023-01-01T00:00:00+00:00

For data from 2023-01 and before, this data was fetched more than one month at a time. This should have no negative impact unless, for example, an instance type was retired before the month began (and there should therefore be no price). These older files also only contain default regions. Data from 2023-02 and later contains all regions, including opt-in regions.

Using data

You can process each month individually. If you need the entire data stream at once, you can cat all files to zst together:

cat prices/*/*.tsv.zst | zstd -d
DOI: 10.5281/zenodo.15314622
title: AWS Spot Price History

description: AWS Spot Price History

This dataset tracks historical prices for AWS spot prices across all regions. It is updated automatically on the 1st of each month to contain data from the previous month.

Data format

Each month of data is stored as a ZStandard-compressed .tsv.zst file.

The data format matches that returned by AWS's describe-spot-instance-prices, with the exception that availability zones have been replaced by their global ID. For instance, here are some example lines from one capture:

euc1-az2        i4i.8xlarge     Linux/UNIX      1.231800        2023-02-28T23:59:57+00:00euc1-az3        r5b.8xlarge     Red Hat Enterprise Linux        0.749600        2023-02-28T23:59:58+00:00euc1-az3        r5b.8xlarge     SUSE Linux      0.744600        2023-02-28T23:59:58+00:00euc1-az3        r5b.8xlarge     Linux/UNIX      0.619600        2023-02-28T23:59:58+00:00euc1-az3        m5n.4xlarge     Red Hat Enterprise Linux        0.476000        2023-02-28T23:59:59+00:00euc1-az2        m5n.4xlarge     Red Hat Enterprise Linux        0.492000        2023-02-28T23:59:59+00:00euc1-az3        m5n.4xlarge     SUSE Linux      0.471000        2023-02-28T23:59:59+00:00euc1-az2        m5n.4xlarge     SUSE Linux      0.487000        2023-02-28T23:59:59+00:00euc1-az3        m5n.4xlarge     Linux/UNIX      0.346000        2023-02-28T23:59:59+00:00euc1-az2        m5n.4xlarge     Linux/UNIX      0.362000        2023-02-28T23:59:59+00:00

When fetching spot instance pricing from AWS, results contain some prices from the previous month so that the price is known at the start of the month. These prices are adjusted in this dataset to be at the exact start of the month UTC:

euw3-az2        g4dn.4xlarge    Linux/UNIX      0.558600        2023-01-01T00:00:00+00:00

For data from 2023-01 and before, this data was fetched more than one month at a time. This should have no negative impact unless, for example, an instance type was retired before the month began (and there should therefore be no price). These older files also only contain default regions. Data from 2023-02 and later contains all regions, including opt-in regions.

Using data

You can process each month individually. If you need the entire data stream at once, you can cat all files to zst together:

cat prices/*/*.tsv.zst | zstd -d
DOI: 10.5281/zenodo.15568489
title: AWS Spot Price History

description: AWS Spot Price History

This dataset tracks historical prices for AWS spot prices across all regions. It is updated automatically on the 1st of each month to contain data from the previous month.

Data format

Each month of data is stored as a ZStandard-compressed .tsv.zst file.

The data format matches that returned by AWS's describe-spot-instance-prices, with the exception that availability zones have been replaced by their global ID. For instance, here are some example lines from one capture:

euc1-az2        i4i.8xlarge     Linux/UNIX      1.231800        2023-02-28T23:59:57+00:00euc1-az3        r5b.8xlarge     Red Hat Enterprise Linux        0.749600        2023-02-28T23:59:58+00:00euc1-az3        r5b.8xlarge     SUSE Linux      0.744600        2023-02-28T23:59:58+00:00euc1-az3        r5b.8xlarge     Linux/UNIX      0.619600        2023-02-28T23:59:58+00:00euc1-az3        m5n.4xlarge     Red Hat Enterprise Linux        0.476000        2023-02-28T23:59:59+00:00euc1-az2        m5n.4xlarge     Red Hat Enterprise Linux        0.492000        2023-02-28T23:59:59+00:00euc1-az3        m5n.4xlarge     SUSE Linux      0.471000        2023-02-28T23:59:59+00:00euc1-az2        m5n.4xlarge     SUSE Linux      0.487000        2023-02-28T23:59:59+00:00euc1-az3        m5n.4xlarge     Linux/UNIX      0.346000        2023-02-28T23:59:59+00:00euc1-az2        m5n.4xlarge     Linux/UNIX      0.362000        2023-02-28T23:59:59+00:00

When fetching spot instance pricing from AWS, results contain some prices from the previous month so that the price is known at the start of the month. These prices are adjusted in this dataset to be at the exact start of the month UTC:

euw3-az2        g4dn.4xlarge    Linux/UNIX      0.558600        2023-01-01T00:00:00+00:00

For data from 2023-01 and before, this data was fetched more than one month at a time. This should have no negative impact unless, for example, an instance type was retired before the month began (and there should therefore be no price). These older files also only contain default regions. Data from 2023-02 and later contains all regions, including opt-in regions.

Using data

You can process each month individually. If you need the entire data stream at once, you can cat all files to zst together:

cat prices/*/*.tsv.zst | zstd -d
DOI: 10.6084/m9.figshare.21747461.v9
title: OpenCitations Meta CSV dataset of all bibliographic metadata

description: Compared to the previous version, this release includes metadata related to citing and cited bibliographic resources added in the March 2024 version of Crossref.This dataset contains all the bibliographic metadata (in CSV format) included in OpenCitations Meta. In particular, each line of the CSV file defines a bibliographic resource, and includes the following information:<b>[field "id"]</b> the IDs for the document described within the line;<b>[field "title"]</b> the document's title;<b>[field "author"]</b> the authors of the document;<b>[field "pub_date"]</b> the date of publication;<b>[field "venue"]</b> information about the venue, i.e. the bibliographical resource to which the document belongs;<b>[field "volume"]</b> the volume sequence identifier (e.g. a number) to which the entity belongs;<b>[field "issue"]</b> the issuesequence identifier (e.g. a number) to which the entity belongs;<b>[field "page"]</b> the page range of the resource described in the row;<b>[field "type"]</b> the type of resource described in the row;<b>[field "publisher"]</b> the entity responsible for making the resource available;<b>[field "editor"] </b>the editors of the document.This version of the dataset contains:116,605,079 bibliographic entities348,844,164 authors and 2,561,339 editors (counted by their roles, without disambiguating individual724,563 publication venues242,362 publishersThe compressed dataset weighs 11G, while, when extracted, it weighs 47G on an ext4 filesystem.Additional information about OpenCitations Meta at official webpage.
DOI: 10.6084/m9.figshare.28237673.v1
title: BlockchainDataset

description: This dataset contains documents related to blockchain technology and sustainable supply chains retrieved from the Scopus database.
DOI: 10.6084/m9.figshare.28588874.v1
title: Data from: Flap-necked chameleons change colour to match their background

description: Data and code for Major et al flap-necked chameleon colour change. Columns in .csv files are outputs from imageJ.
DOI: 10.6084/m9.figshare.879661.v1
title: EEG, EMG, accelerometer and gyroscope data for a FES-mediated brain-computer interface (BCI) aiming to suppress neurological tremor

description: 001FN03.mat_EEG.csv<br>EEG data of patient 001FN03. 15 EEG channels (Fs: 1000Hz) : FC3, FCz, FC4, C5, C3, C1, CZ, C2, C4, C6, CP3, CPZ, and CP4 (POz: ground; linked ear-lobes: reference). Each column represents the above channels in the cited order. 001FN03.mat_IMU.acce.csv<br>Accelerometer data of patient 001FN03. 3 channels (Fs: 1000Hz) : x, y, z. Those data are presented here as complementary information. Scroll down sheet to view values. 001FN03.mat_IMU.gyro.csv<br>Gyroscope data of patient 001FN03. 3 channels (Fs: 1000Hz) : x, y, z. This is used for the kinematic module. The first second (more or less 1000 records) is silent and corresponds to the difference in time made while turning on the different acquisition devices. Scroll down sheet to view values. 001FN03.mat_sEMG.biceps.csv<br>Biceps EMG data of patient 001FN03. 15 channels (Fs: 1000Hz) : Resampled EMG data. This is used in the cortico-muscular module. Crossed with EEG data, cortico-muscular coherence has been computed for all possible couples of EEG-EMG data. 001FN03.mat_sEMG.extensors.csv<br>Extensors EMG data of patient 001FN03. 15 channels (Fs: 1000Hz) : Resampled EMG data. This is used in the cortico-muscular module. Crossed with EEG data, cortico-muscular coherence has been computed for all possible couples of EEG-EMG data. 001FN03.mat_sEMG.flexors.csv<br>Flexors EMG data of patient 001FN03. 15 channels (Fs: 1000Hz) : Resampled EMG data. This is used in the cortico-muscular module. Crossed with EEG data, cortico-muscular coherence has been computed for all possible couples of EEG-EMG data. 001FN03.mat_sEMG.triceps.csv<br>Triceps EMG data of patient 001FN03. 15 channels (Fs: 1000Hz) : Resampled EMG data. This is used in the cortico-muscular module. Crossed with EEG data, cortico-muscular coherence has been computed for all possible couples of EEG-EMG data. QPs.csv<br>Quality paramter vs threshold numerical values (simple ratio). This is to find exact values corresponding to what is observed on the (corresponding) figure of the article. Each column is a different threshold value. QPsq.csv<br>Quality paramters vs threshold numerical values (squared ratio). This is to find exact values corresponding to what is observed on the (corresponding) figure. Each column is a different threshold value. Ts.csv<br>Intermediate threshold results. This can be discarded. Tsq.csv<br>Intermediate threshold results. This can be discarded.
DOI: 10.6084/m9.figshare.24521968.v1
title: <b>Genome-wide summary statistics from a cross-ancestry meta-analysis of depression</b>

description: GWAS summary statistics for cross-ancestry meta-analysis of depression, AGDS and 23andMe cohorts were left out.
DOI: 10.6084/m9.figshare.28087637.v3
title: 2024 12 23 Updated Research Description

description: Research Summary originally produced as a submission to Journal of Zoology. This research summary includes significant anthropology results documenting how the Allaire Data corroborate the accuracy of Indigenous knowledge on this disputed species. The summary also includes Tables providing an overview of all field visits and data collected.
DOI: 10.6084/m9.figshare.5505856.v1
title: Hair Dye Substance Database (HDSD)

description: Fully 2D-curated database of 313 QSAR-ready hair dye substances available for download as Excel spreadsheet or SDF (structure-data file). Includes information such as substance name, structure, CASRNs, DTXSIDs, classes, color (if applicable), and computed properties. SDF can be opened using chemical drawing software such as ChemDraw Professional or MarvinView. Stay tuned for peer-reviewed journal article available for details regarding database development, analysis, and potential applications. We are seeking to improve the HDSD. Please contact Tova N. Williams at tnwilli7@ncsu.edu for suggestions.
DOI: 10.6084/m9.figshare.9701795.v4
title: DocGraph Datasets

description: CareSet Journal was born out of the DocGraph community. In 2010 DocGraph was formed as a movement to create open health data transparency. The efforts of the DocGraph community, guided by Fred Trotter, led to the first national Provider Referral data release by the US government. The original “DocGraph Data” has helped researchers, journalists, and companies around the nation to provide data-backed healthcare solutions.
DOI: 10.17632/5cffwxxv7t
title: FracSym: Automated symbolic computation of Lie symmetries of fractional differential equations

description: Abstract In this paper, we present an algorithm for the systematic calculation of Lie point symmetries for fractional order differential equations (FDEs) using the method as described by Buckwar & Luchko (1998) and Gazizov, Kasatkin & Lukashchuk (2007, 2009, 2011). The method has been generalised here to allow for the determination of symmetries for FDEs with n independent variables and for systems of partial FDEs. The algorithm has been implemented in the new MAPLE package FracSym (Jefferson and Ca... Title of program: FracSym Catalogue Id: AERA_v1_0 Nature of problem Determination of the Lie point symmetries of fractional differential equations (FDEs). Versions of this program held in the CPC repository in Mendeley Data AERA_v1_0; FracSym; 10.1016/j.cpc.2013.09.019 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)
DOI: 10.6084/m9.figshare.14685858.v1
title: Classifying the Offensive Language in Hate Speeches by Kpop Fans on Twitter

description: This is a dataset that have been streamed on Twitter and have also been manually annotated. This dataset contains the Classification of Offensive Language in Hate Speeches by Kpop Fans on Twitter. This dataset is done for a final year project.
DOI: 10.6084/m9.figshare.20521848.v1
title: MetroStation Dataset

description: This dataset contains 2123 annotated images with 12,974 labels. And it orientes to stairs, escalator,gate and platform in metro station. This dataset is designed for passenger detection in crowd and occluded scenes of metro station.
DOI: 10.6084/m9.figshare.24967536.v1
title: <b>Rapid</b><b>Analysis of </b><b>FMR1</b><b>5'UTR CGG Repeat Expansions</b><b>by long-read PCR and </b><b>Oxford Nanopore Third Generation Sequencing</b>

description: <b>Background:</b> Conventional genetic testsfor fragile X syndrome still presentedsome limitations, such ascomplicated proceduresand large sample amount for Southern blot, and limitied mutation types for triplet repeat-primed polymerase chain reaction(TP-PCR). Currently,third-generation long-read sequencing is a potential technology for thesimultaneous detection of CGG repeat expansions, point mutations, and deletions. However, no simple method exists for obtainingthe target long-fragment CGG repeat region with ultra-high GC content forthird-generationsequencing.<b>Methods:</b><b> </b>We developed a rapid long-fragment ultra-high GC polymerase chain reaction (PCR) amplificationapproach to directly prepare long-readlibraries, followed by Oxford Nanopore sequencingto detect the full spectrum of FMR1 5′UTR CGG repeat mutations. This approach was applied to 10 standard cell line samples, 53 retrospectiveclinical blood samples, and 9 prenatal amniotic fluid samples.<b>Results:</b> We found that the FMR15’UTR CGG expansion genotypes identified usingour approach were completely consistentwith the results of TP-PCR inall10 cell line samples,53 clinical samples, and 9 prenatal amniotic fluid samples. The recognizable number of CGG repeatsin normal and pre-mutation types was also significantly correlated(male cell lines, n=3×triplicate, R2=0.9996; femalecell lines, n=4×triplicate, R2=0.9972; clinical male blood samples, n=11, R2=1.0000; clinical femalebloodsamples, n=22, R2=0.9854).<b>Conclusion: </b>This studyprovided a simple andcost-effective approachbased on long-fragment ultra-high GC PCRto obtain the FMR1 5′UTR CGG repeatregion and direct library for Oxford Nanopore Third Generation Sequencing, and it may be used in Fragile X syndrome diagnosis and prenatal care, and as a reference method for detecting other previously unsolved diseases caused by short tandem repeat expansions with ultra-high GC content, such as myotonic dystrophy and Huntington’ s disease.
DOI: 10.6084/m9.figshare.26409091.v1
title: MCLP analysis

description: MCLP python code and dataset.
DOI: 10.60893/figshare.aip.28956731.v1
title: Supplementary material for Electron charge dynamics in metal-dot QCA cell using gate reflectometry

description: additional text and figures
DOI: 10.6084/m9.figshare.16669228.v1
title: ImSURE Isotropic Phantom

description: Isotropic phantom
DOI: 10.6084/m9.figshare.27132423.v1
title: PPG_CHOLESTEROL .MAT

description: This information is related to the project "PPG-Based Cholesterol Assessment," which showcases different PPG signals along with the cholesterol levels of the subjects. The purpose is to validate techniques or tools for estimating total cholesterol levels in the blood using the PPG signal.
DOI: 10.6084/m9.figshare.5554642.v1
title: Experiment HaLT-SubjectM-161124-6States

description: Experiment in which subject performed 6 imageries of left and right hand motion, left and right leg motion, tongue motion, and a passive state.
DOI: 10.17632/s7c74xw673
title: Electronic noses synthetic database

description: Four work scenarios are generated artificially with the chemosensors package to address the deviations present in artificial odor systems. This research leads to a total of ten databases generated from the four scenarios, in which, noise and drift concentration present are parameterized in the generated data. Prior knowledge of these values allows analysis of results using raw data instead of the traditional method for artificial odor systems, which employs feature selection.
DOI: 10.6084/m9.figshare.1492431.v2
title: pairs_metadata

description: Lists, for all strains in the study, the ENA/ERS accessions (to access read data, assembly+annotation respectively) of the blood and CSF isolate, and the bacterial species they correspond to
DOI: 10.6084/m9.figshare.17124125.v1
title: Neurological Needs of Refugees Round 3 Survey.xlsx

description: Round 3 survey results from Delphi method process.
DOI: 10.6084/m9.figshare.3559338.v1
title: Supplement 1. C++ code and R script files for summary statistics, distance metrics, population simulation, and sequential Monte Carlo algorithm used for parameter estimation of a stochastic stage-structured population model.

description: File List distanceCode.R (MD5: 263965622e4839af0d19193c0cf32195) obsDataC.R (MD5: 0347a95c067dddee0cf34fe286b7ca9c) popSimCodeC_E.R (MD5: 531aafb2d51a23449756138cd8ea994a) simCohortVec.so (MD5: 3ebd32ff70131efa625f378c59e1e46a) SMCcode_E.R (MD5: d7819abf935cec9b4d6f17962eb4fd78) execSMC_C_E.R (MD5: c2583fd6716ac06fb377f75f0fd10e4b) Description We provide functions to compute distances using all candidate summary statistics and distance metrics in distanceCode.R. obsDataC.R is a data file that was used as our "observed" data set. We simulated many data sets using popSimCodeC_E.R. This source file contains both an R function to simulate a data set and an interface to call C++ code simCohortVec.co to simulate a data set. The actual ABC SMC algorithm is coded in a function in SMCcode_E.R.
DOI: 10.6084/m9.figshare.5593291.v1
title: What’s System Engineering Philosophy?

description: Object of science material that is studied is system at field of technique.<br>
DOI: 10.6084/m9.figshare.6900566.v1
title: Diamond open access venn

description: Venn diagram highlighting the different levels of open access in scholarly publishing, as a function of cost to the readers and authors, copyright retention, and peer review.
DOI: 10.17632/xn3dkjjjkj
title: Mendeley Supplemental Data: Perianal and anal skin cancers treated with Mohs micrographic surgery and interdisciplinary care

description: Mendeley Supplemental Data for the article "Perianal and anal skin cancers treated with Mohs micrographic surgery and interdisciplinary care: Local recurrence rates and patient-reported outcomes"
DOI: 10.17632/yxx5kf3vdj
title: Data for: Proteomics of the acid-soluble fraction of whole and major gland saliva in burning mouth syndrome patients.

description: Fig. s1: XIC peak areas (mean ± SD) of salivary cystatins (a), histatins (b), bPRPs (c) analysed in the acid-soluble fraction of resting whole saliva (1), stimulated whole saliva (2), parotid saliva (3) and submandibular-sublingual saliva (4) of BMS patients (in grey) and healthy controls (in black). Only Significant p values are indicated. Fig. s2: XIC peak areas (mean ± SD) of statherin and its proteoforms (a), P-B peptide (b), cystatin C and B (c), α-defensins (d) and Ps-1 and II-2 (e) analysed in the acid-soluble fraction of resting whole saliva (1), stimulated whole saliva (2), parotid saliva (3) and submandibular-sublingual saliva (4) of BMS patients (in grey) and healthy controls (in black). Table s1. Elution times, experimental and theoretical average Mass values, and m/z multiply charged ions used for XIC search, of cystatins, histatins, α-defensins and some derivatives. Table s2. Elution times, experimental and theoretical average Mass Values, and m/z multiply charged ions used for XIC search, of salivary acidic and basic proline-rich proteins, statherin, P-B peptide and derivatives.
DOI: 10.6084/m9.figshare.15062718.v1
title: Tinnitus Qualities Questionnaire (TQQ): Validation Data

description: This data set was generated as a part of four clinical trials aimed at evaluating the efficacy of the Internet-based cognitive behavioural therapy for tinnitus in the US population. This is a combined data set from two pertest-post single group trials and two randomized controlled trials (n=308). The data pertaining to newly developed Tinnitus Qualities Questionnaire (TQQ) is included here.<br>The attached documents include the dataset, Tinnitus Qualities Questionnaire (TQQ) questionnaire and metadata which provide additional details about the dataset.
DOI: 10.6084/m9.figshare.23397089.v1
title: DATASET SRQR CHECKLIST QUESTIONNAIRE IN ENGLISH AND ALBANIAN LANGUAGE

description: Data for the paper
DOI: 10.6084/m9.figshare.24975597.v1
title: Phage Protein Annotations

description: 1) AMG mapping: Auxilliary metabolic genes pulled out and predicted by VIBRANT and then reads were mapped.2) Gene_Mapping_annotated_total.csv: Annotations for all phage genes and read mapping values associated with them.