cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.21758003.v2
title: Supplementary Data S6 - Figure 2C and D

description: A table AR rates per class. Used to generate Figure 2 C and D.
DOI: 10.6084/m9.figshare.21758024.v1
title: Supplementary Data 6 - Figure 3E

description: Prevalence of gene neighbors and same strand. Used to generate Figure 3E
DOI: 10.6084/m9.figshare.21758033.v1
title: Supplementary Data 5 - Figure 3B and C

description: Per-phylum prevalence of all gene groups. Used to generate Figure 3B and C
DOI: 10.6084/m9.figshare.21758039.v1
title: Supplementary Data 8 - Figure 4c, Figure 5d and c and Supplementary Figures S4 and S5

description: Junction expression across all samples - Figure 3 C, Figure 4 C and D, and Supplementary Figures S4 and S5
DOI: 10.6084/m9.figshare.21758063.v1
title: Supplementary Data 7 - Figure 4B

description: Example of D.melanogaster PRO-seq sample coverage per position - Figure 4B
DOI: 10.6084/m9.figshare.21757649
title: Supplementary Data S1

description: List of species mtDNA-sequenced species included in our mtDNA database and not in previous analyses (MitoZoa 2012).
DOI: 10.6084/m9.figshare.21757904
title: Supplementary Data S3 - Figure 1A-D

description: Data used to generate figure 1 A-. A table of species with full taxonomic data and their mtDNA gene order.
DOI: 10.6084/m9.figshare.21757904.v1
title: Supplementary Data 1 - Figure 1A-D

description: Data used to generate figure 1 A-. A table of species with full taxonomic data and their mtDNA gene order.
DOI: 10.6084/m9.figshare.21757904.v2
title: Supplementary Data S3 - Figure 1A-D

description: Data used to generate figure 1 A-. A table of species with full taxonomic data and their mtDNA gene order.
DOI: 10.6084/m9.figshare.21757940.v1
title: Supplementary Data 2 - Figures 1E and Supplementary Figures S1 and S3

description: Data on all tRNA genes and codon usage. used to generate Figures 1E and Supplementary Figures S1 and S2.
DOI: 10.6084/m9.figshare.21757646.v2
title: Shtolz_et_al_Supplementary_Table_S4 (Not referenced in text)

description: Summary of RNA-seq library selection methods used in the datasets we used for our RNA-seq analyses (datasets listed in Supplementary Table S3.
DOI: 10.6084/m9.figshare.21757652
title: Supplementary Data S2

description: List of RNA-seq and PRO-seq datasets used in the article.
DOI: 10.6084/m9.figshare.21757655
title: Supplementary Table S1

description: The number of species within each taxonomic group in our database (created in 2020) as compared to MitoZoa (2012).
DOI: 10.6084/m9.figshare.21757646.v1
title: Shtolz_et_al_Supplementary_Table_S4.xlsx

description: Summary of RNA-seq library selection methods used in the datasets we used for our RNA-seq analyses (datasets listed in Supplementary Table S3.
DOI: 10.6084/m9.figshare.21757649.v2
title: Supplementary Data S1

description: List of species mtDNA-sequenced species included in our mtDNA database and not in previous analyses (MitoZoa 2012).
DOI: 10.6084/m9.figshare.21757652.v2
title: Supplementary Data S2

description: List of RNA-seq and PRO-seq datasets used in the article.
DOI: 10.6084/m9.figshare.21758003.v1
title: Supplementary Data 4 - Figure 2C and D

description: A table AR rates per class. Used to generate Figure 2 C and D.
DOI: 10.6084/m9.figshare.21757649.v1
title: Shtolz_et_al_Supplementary_Table_S2.xlsx

description: List of species mtDNA-sequenced species included in our mtDNA database and not in previous analyses (MitoZoa 2012).
DOI: 10.6084/m9.figshare.21757652.v1
title: Shtolz_et_al_Supplementary_Table_S3.xlsx

description: List of RNA-seq and PRO-seq datasets used in the article.
DOI: 10.6084/m9.figshare.21757655.v2
title: Supplementary Table S1

description: The number of species within each taxonomic group in our database (created in 2020) as compared to MitoZoa (2012).
DOI: 10.6084/m9.figshare.21757655.v1
title: Shtolz_et_al_Supplementary_Table_S1.xlsx

description: The number of species within each taxonomic group in our database (created in 2020) as compared to MitoZoa (2012).
DOI: 10.6084/m9.figshare.21767678.v5
title: 20221222_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.21767678
title: 20221222_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.21767678.v7
title: 20221222_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.21767678.v6
title: 20221222

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.6084/m9.figshare.21757964
title: Supplementary Data S5 - Figure 2 A and B, and Supplementary Figure S3

description: A gene order-based distance matrix.Used to generate Figure 2 A and B, and Supplementary Figure S3 NxN array of the CREx based distance between all pairs of species <br> <br>
DOI: 10.6084/m9.figshare.21757964.v1
title: Supplementary Data S5 - Figure 2 A and B, and Supplementary Figure S3

description: A gene order-based distance matrix.Used to generate Figure 2 A and B, and Supplementary Figure S3 NxN array of the CREx based distance between all pairs of species <br> <br>
DOI: 10.6084/m9.figshare.21836397
title: 20221222_starrydata2

description: All data on Starrydata2, a database of experimental properties in the field of inorganic materials
DOI: 10.4225/08/556bcd6a38737
title: Baseline map of Australian soil organic carbon stocks and their uncertainty

description: This is a fine spatial resolution (90 x 90 m pixels) baseline map of organic Carbon stocks at the continental scale.  The uncertainty is described by the 95% confidence intervals and the standardised confidence intervals (range(95% CIs)/mean). Maps are provided in GeoTiff format with LZW compression.
DOI: 10.5281/zenodo.3637597
title: TCTracer Evaluation Data - International Conference on Software Engineering 2020

description: This repository provides the data artefacts for the experiments conducted using our tool TCtracer for the International Conference on Software Engineering (ICSE) 2020 paper "Establishing Multilevel Test-to-Code Traceability Links".
DOI: 10.5281/zenodo.3995641
title: Data and simulations files for the article "Accurate modeling and characterization of photothermal forces in optomechanics"

description: Data and simulations files for the article "Accurate modeling and characterization of photothermal forces in optomechanics".
DOI: 10.5281/zenodo.7276812
title: The dataset for the paper "Long-Lived and Continual Volcanic Eruptions, Tectonic Activity, Pit Chains Formation and Boulder Avalanches in Northern Tharsis Region: Implications for Late Amazonian Geodynamics and Seismo-Tectonic Processes on Mars" JGR: Planets.

description: This dataset pertains to the following paper in Journal of Geophysical Research: Planets. Krishnan, V., and Kumar, P.S. (2022), Long-Lived and Continual Volcanic Eruptions, Tectonic Activity, Pit Chains Formation and Boulder Avalanches in Northern Tharsis Region: Implications for Late Amazonian Geodynamics and Seismo-Tectonic Processes on Mars, Journal of Geophysical Research: Planets (for full citation, please see the journal). AGU Manuscript No. 2022JE007511.
DOI: 10.5284/1000291
title: The Archaeological Potential of Secondary Contexts

description: The project assesses the Palaeolithic stone tool assemblages recovered from the flood deposits of Middle and Late Pleistocene (787-11,000 BP) rivers in the UK (described as archaeological secondary contexts). The stone tool assemblages are distributed across southern Britain, while Pleistocene flood (fluvial) deposits are distributed throughout the UK. The stone tool assembalges and their associated fluvail deposits vary in age from at least 700,000 years old to the end of the Paleolithic period (c. 11,000 years BP). The importance of archaeological secondary contexts therefore stems from their widespread geopgraphical distribution and extensive chronological coverage.
This project assesses the value of the archaeological secondary context resource in terms of th eunique spatio-temporal structure of the data, assembalge taphononmy, appropriate analytical frameworks and the potential of the resource for current and future understanding of the Palaeolithic period. The project report discusses a series of key themes and case studies.
DOI: 10.5284/1000303
title: The Palaeolithic Archaeology of the Sussex/Hampshire Coastal Corridor

description: The unique Middle and Late Pleistocene sedimentary record preserved along the Sussex/Hampshire coastal corridor between Romsey and Brighton that formed over the last 500,000 years contains a wealth of Lower and Middle Palaeolithic archaeological remains. The importance of these deposits has been recognised through the various phases of excavation at the internationally renowned site at Boxgrove, through mapping work on the upper coastal plain and the field and analysis work associated with the Palaeolithic Archaeology of the Sussex/Hampshire Coastal Corridor (PASHCC). These various works have now identified a range of sequences of different archaeological potential across the region and have produced information suitable for both curatorial purposes (evaluation/mitigation) as well as academic focused study.
DOI: 10.5441/002/edbt.2017.20
title: Matching Web Tables To DBpedia - A Feature Utility Study
DOI: 10.6084/m9.figshare.1183512
title: Chessboard Dataset Low Magnification Overview Images

description: These are z-projections of ribbons imaged at 25x magnification for DAPI and YFP to serve as guides for subsequent higher-magnification imaging of desired subregions.
DOI: 10.5281/zenodo.1000885
title: Steam Review Dataset (2017)

description: The dataset contains over 6.4 million publicly available reviews in English from Steam Reviews portion of Steam store run by Valve. Each review is described by review text, the id of game it belongs to, review sentiment (positive or negative) and a number of users who tough review was helpful. This is essentially an extension to previously released Steam Review Dataset

The resource is provided as a bzip2 compressed CSV file.

Steam Reviews and Steam are owned by Valve. Authors are not affiliated with and are not endorsed by Valve / Steam
DOI: 10.5281/zenodo.3814197
title: Experimental Results for the study "A Modular Hybridization of Particle Swarm Optimization and Differential Evolution"

description: This repository contains the experiment results and R scripts to analyze the data for the study "A Modular Hybridization of Particle Swarm Optimization andDifferential Evolution", which is accepted in <em>The Genetic and Evolutionary Computation Conference</em> (GECCO) '20 conference: Rick Boks, Hao Wang, and Thomas Bäck. 2020. A Modular Hybridization of Particle Swarm Optimization and Differential Evolution. In <em>Genetic and Evolutionary Computation Conference Companion (GECCO ’20 Companion), July 8–12, 2020, Cancún, Mexico. </em>ACM, New York, NY, USA, 8 pages. https: //doi.org/10.1145/3377929.3398123 Bibtex: <pre><code class="language-markdown">@inproceedings{BoksWB20, author = {Rick Boks and Hao Wang and Thomas B\"ack}, title = {{A Modular Hybridization of Particle Swarm Optimization and Differential Evolution}}, booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, {GECCO} 2020, Canc\'un, Mexico, July 8-12, 2020}, publisher = {{ACM}}, year = {2020}, url = {https://doi.org/10.1145/3321707.3321816}, doi = {doi.org/10.1145/3377929.3398123, }</code></pre> <strong>Data description:</strong> we benchmarked <strong>800 </strong>different<strong> </strong>hybridizations of the Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms on a well-known continuous black-box problem set called COCO/BBOB, which consists of 24 test functions. 30 independent runs are conducted for each algorithm on each problem. 'ERT.csv': a data frame with columns DIM (5D or 20D), funcId (F1-24), algId (algorithm names), target (\(10^{\{-8,-7, \ldots, 1\}}\)), ERT (expected running time), and sd (standard deviation). 'raw-data.csv': the running time recorded in each independent run. 'analysis.R': the R script that generates ERT tables in the paper. 'ecdf.R': the R script that renders the ECDF (empirical cumulative distribution function) plots in the paper.
DOI: 10.6084/m9.figshare.1204999
title: Author Insights 2014

description: Raw data from Nature Publishing Group and Palgrave Macmillan's annual Author Insights survey. Verbatims and other identifying fields redacted.
DOI: 10.6084/m9.figshare.1234051
title: NPG - Open Access survey raw data

description: Raw data from NPG Open access survey
DOI: 10.21227/326k-qr90
title: "Tennessee-Eastman-Process" Alarm Management Case Study

description: This is an alarm management case study based on the “Tennessee-Eastman-Process” (TEP). The presented case study aims to provide a suitable benchmark dataset for the development and validation of alarm management methods in complex industrial processes using both quantitative data and qualitative information from different sources. Unlike real industrial processes, simulation of the TEP allows to design and generate abnormal sequences, which can be repeated and varied without risking the loss of equipment or harming the environment. In addition, as the simulation is supervised, all induced faults and process normalizations are explicitly known and therefore act as a ground truth, facilitating the utilization of external evaluation metrics (e.g. when using cluster analysis).Further details are described in a supplementary technical report.
DOI: 10.25966/9fv8-6q78
title: Pyrocumulonimbus Events over British Columbia in August 2017: Results from the NASA GEOS Earth System Model
DOI: 10.5255/ukda-sn-6852-2
title: Young Lives: an International Study of Childhood Poverty: Round 2, 2006
DOI: 10.5281/zenodo.3375494
title: Moving-block System: Requirements and Formal Models

description: A moving-block system (cf. https://en.wikipedia.org/wiki/Moving_block) is a railway signalling and distancing system aimed at reducing the headways between trains along a track, therefore increasing line capacity. In railways, a block is intended as a segment of track that is assigned to a train: a train cannot enter a block that is occupied by another train. With traditional fixed-block systems, each moving train is assigned a fixed block, regardless of its speed and position within the block itself. With moving-block systems, the block is dynamically computed based on the position and speed of the train, which is continuously computed onboard and communicated to the wayside control systems. This enables the possibility of routing more trains along the same track. For more details, please refer to: https://en.wikipedia.org/wiki/Communications-based_train_control The package includes a set of <strong>requirements</strong> and <strong>models</strong> for a railway moving-block system: (a) a PDF document named Moving-block Model and Requirements.pdf, which includes a UML model of a moving-block system together with a set of requirements for the system; (b) a set of 9 folders, each one associated to a formal or semi-formal development tool. Each folder contains one or more models of the moving-block system from (a), developed by means of the tool. The models were developed using the following tool versions. Other versions may still open and verify the models. Simulink (2017b) UMC (4.7) UPPAAL SMC (4.1.4) Atelier B (4.2.1) ProB (1.10.2018) NuSMV (2.6.0) SPIN (6.4.9) CADP (2019-a) FDR4 (4.2.3)
DOI: 10.5522/04/16566207.v1
title: Head and Neck Cancer Multivariate Blood Data

description: Data for the September 2021 submission to eLife. This is the source data for multivariate regression. Progression free survival outcome.<br>Blood samples from 56 HNSCC patients were prospectively obtained within a Phase 2 clinical trial (NCT02633800), before and after the first treatment cycle of platinum-based chemotherapy, to identify biological covariates predictive of outcome.<br>The baseline dataset contained 42 covariates (29 laboratory parameters at baseline, C1, and 13 clinical characteristics). The second, a combined predictive dataset, consists of 71 covariates, i.e. the 42 baseline covariates and a further 29 derived from the change in lab-based parameters between C1 and C2, measured by log fold change (LFC) of the variable of interest.<br>Drug: 1 = patient was given Patritumab, 0 = drug was not given<br>Age: Patient age in years at baselineSex: 1 = Female, 0 = MaleSite: one-hot encoded site of primary cancerPrev.Cetux: Cetuximab given previouslyHPV.status: 1 = positiveHereg.status: 1 = positiveSmoking: 2 = Current, 1 = Ex smoker, 0 = Never smoked
DOI: 10.25318/1010012901-eng
title:  Residential mortgage credit, outstanding balances of major private institutional lenders, Bank of Canada

description: This table contains 44 series, with data starting from 1969 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Monthly (2 items: At month-end; Average at month-end ...), Seasonal adjustment (2 items: Unadjusted; Seasonally adjusted ...), Components (11 items: Total outstanding balances; Finance companies and other institutions; Total of foregoing; Chartered banks ...).
DOI: 10.6084/m9.figshare.21828054
title: Supplementary Material for: An Exergame-Integrated IoT-Based Ergometer System Delivers Personalized Training Programs for Older Adults and Enhances Physical Fitness: A Pilot Randomized Controlled Trial

description: <b><i>Introduction:</i></b> Regular physical exercise is believed to counteract the adverse physiological consequences of aging. However, smart fitness equipment specifically designed for older adults is quite rare. Here we designed an exergame-integrated internet of things (IoT)-based ergometer system (EIoT-ergo) that delivers personalized exercise prescriptions for older adults. First, physical fitness was evaluated using the Senior Fitness Test (SFT) application. Then, radio frequency identification (RFID) triggered the EIoT-ergo to deliver the corresponding exercise session based on the individual level of physical fitness. The exercise intensity during each workout was measured to generate the next exercise session. Further, EIoT-ergo provides an exergame to help users control and maintain their optimal cadence while engaging in exercise. <b><i>Methods:</i></b> This was a randomized controlled trial with 1:1 randomization. Participants were older adults, 50+ years of age (<i>N</i> = 35), who are active in their community. Participants in the EIoT-ergo group received a 12-week personalized exercise program delivered by EIoT-ergo for 30 min per session, with 2 sessions per week. Participants in the control group continued with their usual activities. A senior’s fitness test and a health questionnaire were assessed at baseline and at a 13-week reassessment. The Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST) was used to evaluate the satisfaction of EIoT-ergo. <b><i>Results:</i></b> Compared with the control group, the EIoT-ergo group showed significant improvements in muscle strength (time-by-group interaction, sit-to-stand: β = 5.013, <i>p</i> &lt; 0.001), flexibility (back stretch: β = 4.008, <i>p</i> = 0.005; and sit-and-reach: β = 4.730, <i>p</i> = 0.04), and aerobic endurance (2-min step: β = 9.262, <i>p</i> = 0.03). The body composition was also improved in the EIoT-ergo group (body mass index: β = −0.737, <i>p</i> &lt; 0.001; and skeletal muscle index: β = 0.268, <i>p</i> = 0.03). Satisfaction with EIoT-ergo was shown in QUEST, with an average score of 4.4 ± 0.32 (5 for very satisfied). The percentage maximum heart rate in each session also indicated that EIoT-ergo can gradually build up the exercise intensity of users. <b><i>Conclusions:</i></b> EIoT-ergo was developed to provide personal identification, exergames, intelligent exercise prescriptions, and remote monitoring, as well as to significantly enhance the physical fitness of the elderly individuals under study.
DOI: 10.4121/uuid:b64986dd-3203-445e-9b93-13a5ac7ef999
title: Beyond the Hype: Deep Neural Networks Outperform Established Methods Using A ChEMBL Bioactivity Benchmark Set

description: This dataset contains the (standardized) data used in the experiments, alongside the scripts used to perform Deep Neural Nets (DNN_Scripts), and the other machine learning methods in both Pipeline Pilot (PP_protocols) and Python/Scikit-Learn (PY_scripts)
DOI: 10.5281/zenodo.3834921
title: iNRACM: Incorporating 15N into the Regional Atmospheric Chemistry Mechanism (RACM) for assessing the role photochemistry plays in controlling the isotopic composition of NOx, NOy, and atmospheric nitrate

description: <sup>15</sup>N compounds and reactions were incorporated into Regional Atmospheric Chemistry Mechanism (RACM), based on recent experimental or calculated values of isotope fractionation factors (α), to simulate δ<sup>15</sup>N values in NO<sub>y</sub> compounds.
description: {"references": ["Stockwell, W. R., Kirchner, F., Kuhn, M., & Seefeld, S. (1997). A new mechanism for regional atmospheric chemistry modeling. Journal of Geophysical Research: Atmospheres, 102(D22), 25847-25879.", "Huan Fang (2019), \"SboxN15,\" https://mygeohub.org/resources/sbox."]}
DOI: 10.5281/zenodo.4240204
title: Supplemental material for "The influence of temperature on the seasonality of historical plague outbreaks"

description: This repository provides the code and raw data for the project.
DOI: 10.4121/15097500.v1
title: Simulations underlying the analysis in the publication: Climate Change Projections of Terrestrial Primary Productivity over the Hindu Kush Himalayan Forests

description: Outputs from the LPJ-GUESS dynamic global vegetation model (Ahlström et al., 2012) are provided for three historical-future simulations for the period 1851-2100, forced by bias-corrected climate from the CCSM4, MPI-ESM-LR and IPSL-CM5A-MR Earth system models (ESM). Two future climate scenarios are considered, RCP 2.6 and RCP 8.5. Simulations with potential natural vegetation everywhere (PNV, only for RCP 8.5) and with consideration of agricultural land use (LU), including transient changes were conducted. All simulations are at 0.5 x 0.5 degree spatial resolution. For information on the simulation protocol, please refer to Ahlström et al. (2012).<br>
DOI: 10.4121/16896406.v1
title: Papyrus - A large scale curated dataset aimed at bioactivity predictions

description: This repository contains the Papyrus dataset, an aggregated dataset of small molecule bioactivities, as described in the manuscript "Papyrus - A large scale curated dataset aimed at bioactivity predictions" (Work in Progress).With the recent rapid growth of publicly available ligand-protein bioactivity data, there is a trove of viable data that can be used to train machine learning algorithms. However, not all data is equal in terms of size and quality, and a significant portion of researcher’s time is needed to adapt the data to their needs. On top of that, finding the right data for a research question can often be a challenge on its own. As an answer to that, we have constructed the Papyrus dataset, comprised of around 60 million datapoints. This dataset contains multiple large publicly available datasets such as ChEMBL and ExCAPE-DB combined with smaller datasets containing high quality data. This aggregated data has been standardised and normalised in a manner that is suitable for machine learning. We show how data can be filtered in a variety of ways, and also perform some rudimentary quantitative structure-activity relationship and proteochemometrics modeling. Our ambition is to create a benchmark set that can be used for constructing predictive models, while also providing a solid baseline for related research.