cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/dg64p8wmj9.1
title: Supplementary material 1 (S1) and 2 (S2): Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices

description: S1: DEM preparation and terrain indice modeling, Python Code and DEM

This folder contains all data needed to produce the terrain indices modeled within the paper.
It contains the Python Code for DEM aggregation, hydrological correction and terrain indice modeling

S2: Interactive OPLS analysis loading plot for the Krycklan catchment 
and DEM-derived terrain indices in respect of soil moisture predictions. 

This folder contains the Interactive version of the OPLS analysis loading plot (Fig 2.) 
for the Krycklan catchment and DEM-derived terrain indices in respect of soil moisture predictions.
In the interactive version of this graph (html), all labels are visible by moving the cursor on top of each circle. 
By clicking on a circle the terrain chosen index group will be highlighted

Variables that cluster closely within the same neighborhood along the far sides 
of the horizontal axis are the more robust soil moisture predictors across DEM 
scales. Colored guides connect terrain indices moving from small to large resolutions 
as depicted by the symbol size. In the loading plot, predictive performance increases 
with increased distance from 0 on the predictive axis (pq[1]). Negative and positive values 
on the (pq[1]) axis correspond to negative and positive correlations with Y. The orthogonal axis 
(poso[1]) represents how much of the variation for each variable was not correlated with the determinant (Y).
DOI: 10.6084/m9.figshare.20278098.v1
title: Sustainability of the regional financial system ENG (1).xlsx

description: Data set for research work on: Sustainability of the regional financial system: a case study of the Northwestern Federal District. Eng.
DOI: 10.5281/zenodo.640085
title: POPC with 0, 10, 20, and 30 mol-% of cholesterol at 310 K. Slipids force field.

description: Simulations of a POPC bilayer with varying amounts<br>
of cholesterol. This data is employed for the NMRlipids<br>
project, see <em>http://nmrlipids.blogspot.fi</em> for more information.

A POPC bilayer consisting of 200 lipids (100 per leaflet) <br>
is simulated in the presence of 0% (0 molecules), 10 %<br>
(22 molecules), 20 % (50 molecules), or 30 % (86 molecules)<br>
of cholesterol. The Slipids model [1–3] is employed for lipids,<br>
and the tip3p model for water.

The Slipids force field parameters were downloaded from<br>
http://mmkluster.fos.su.se/slipids/

––––––––––––––––––––––––––––––––––––––––––––––––––––––

The files are in GROMACS format. Trajectory (.xtc) is <br>
100 ns long with data saved every 100 ps. Additionally,<br>
the initial structure (.gro), topology (.top), index file (.ndx),<br>
binary run input file for GROMACS v. 4.6–&gt; (.tpr) and the<br>
energy output file (.edr) are provided together with the run<br>
parameter file (.mdp) employed to run all the simulations.

UPDATE: Longer 500 ns trajectories are updated together with the checkpoint files.

––––––––––––––––––––––––––––––––––––––––––––––––––––––

[1] Derivation and Systematic Validation of a Refined <br>
All-Atom Force Field for Phosphatidylcholine Lipids<br>
Joakim P. M. Jämbeck and Alexander P. Lyubartsev<br>
The Journal of Physical Chemistry B 2012 116 (10), 3164-3179<br>
DOI: 10.1021/jp212503e

[2] An Extension and Further Validation of an All-Atomistic <br>
Force Field for Biological Membranes<br>
Joakim P. M. Jämbeck and Alexander P. Lyubartsev<br>
Journal of Chemical Theory and Computation 2012 8 (8), 2938-2948<br>
DOI: 10.1021/ct300342n

[3] Another Piece of the Membrane Puzzle: Extending Slipids Further<br>
Joakim P. M. Jämbeck and Alexander P. Lyubartsev<br>
Journal of Chemical Theory and Computation 2013 9 (1), 774-784<br>
DOI: 10.1021/ct300777p
DOI: 10.5281/zenodo.7089022
title: A2TEA.Workflow test data

description: Test data for https://github.com/tgstoecker/A2TEA.Workflow
DOI: 10.5281/zenodo.7089606
title: A2TEA Brassicaceae example data

description: Example result object of A2TEA.Workflow output investigating drought stress in Brassicaceae species.
DOI: 10.5281/zenodo.7089608
title: A2TEA.Workflow Poaceae reduced example data

description: Example result object of A2TEA.Workflow output investigating drought stress in Poaceae species. Input for the workflow was reduced: Peptide FASTA files were reduced to 2000 proteins; the transcriptomic data is subsampled to 2M reads to allow for a quicker runtime.
DOI: 10.6084/m9.figshare.20740006
title: RADAR RNA sensor candidates for human genes

description: Pregenerated, untested RADAR RNA sensor designs for human genes from the publication "Modular, programmable RNA sensing using ADAR editing in living cells"
DOI: 10.6084/m9.figshare.20740009
title: RADAR RNA sensor candidates for mouse genes

description: Pregenerated, untested RADAR RNA sensor designs for mouse genes from the publication "Modular, programmable RNA sensing using ADAR editing in living cells"
DOI: 10.17189/1522525
title: MGN V RDRS 5 COMPOSITE DATA RECORD ALT/RAD V1.0
DOI: 10.18739/a2p55dg5n
title: River discharge data, National Petroleum Reserve, Alaska, 2001-2017
DOI: 10.5066/p94ygvwq
title: Creep rate models for California faults in the 2023 US National Seismic Hazard Model

description: Widespread surface creep is observed across a number of active faults included in the US National Seismic Hazard Model. In northern California, creep occurs on the central section of the San Andreas Fault, along the Hayward and Calaveras faults through the San Francisco Bay Area, and to the north coast region along the Maacama and Bartlett Springs faults. In southern California, creep is observed across the Coachella segment of the San Andreas Fault, through the Brawley Seismic Zone, and along the Imperial and Superstition Hills Faults. Seismic hazard assessments for California have accounted for creep using various data and methods, including the most recent Unified California Earthquake Rupture Forecast, version 3 (UCERF3) in 2013. [...]
DOI: 10.5281/zenodo.3730610
title: Raw diffraction data for structure of SARS-CoV-2 main protease with Z44592329 (ID: mpro-x0434 / PDB: 5R83)

description: Raw diffraction data for mpro-x0434 / PDB ID 5R83 (see: https://www.ebi.ac.uk/pdbe/entry/pdb/5R83) - SARS-CoV-2 main protease in complex with Z44592329 (SMILES:O=C(NC=1C=CC=CC1)NC=2C=CC=NC2) collected as part of an XChem crystallographic fragment screening campaign on beamline i04-1 at Diamond Light Source. The deposited structure was automatically processed with standard Diamond tools and PanDDA, however the raw data are being made available to allow reanalysis by any interested party. For more details see: https://www.diamond.ac.uk/covid-19/for-scientists/Main-protease-structure-and-XChem.html
DOI: 10.5281/zenodo.4738905
title: Knutson et al 2020 Tropical Cyclone Projections Data

description: 1) This is the data used to produce Figs 1b, 2b, 3b and 4b in Knutson et al (2020) (DOI: 10.1175/BAMS-D-18-0194.1). 2) Baseline conversion resources for this data 2.1) This paper describes how to convert this data to different risk model baselines: https://doi.org/10.1175/JAMC-D-21-0102.1 2.2) Source code for software tools in R, JS and Py to apply baseline conversion is here: https://doi.org/10.5281/zenodo.5578889 2.3) An online tool to apply baseline conversion is here: https://www.stephenjewson.com/k.html Notes: a) The sample sizes in this data differ slightly in some cases from the original publication. These are the correct values. b) v0.2 fixes the glitch in cell C19. c) For queries about the data values, contact T Knutson. d) The data was formatted and uploaded by S Jewson.
description: {"references": ["DOI:\u00a010.1175/BAMS-D-18-0194.1."]}
DOI: 10.5281/zenodo.5809553
title: Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning"

description: Predictors and predictand for "Repeatable high-resolution statistical downscaling through deep learning". Predictors from the ERA5 reanalysis and predictand from ReKIS (https://rekis.hydro.tu-dresden.de). Data is saved in ".rda" format, to be read from R.
DOI: 10.5281/zenodo.6453048
title: HEPMASS-IMB

description: <strong>HEPMASS-IMB</strong> is a benchmark dataset for <em>signal-background classification</em> in High-Energy Physics (HEP), derived from HEPMASS (Baldi et al.) by imbalancing it two times: on the class labels, as well as on the mass labels. It has 27 feature columns (named from <em>f0</em> to <em>f26</em>), and a 28-th mass feature (named <em>mass</em>). The 27 features are <em>already normalized</em> to have approximately zero-mean and unitary variance. The mass feature has five unique values: <em>500</em>, <em>750</em>, <em>1000</em>, <em>1250</em>, and <em>1500</em>. There are two class labels: 1 (signal), and 0 (background). The dataset describes the decay of an hypothetical particle: \(X \to t\bar{t}\to X-&gt;t\bar{t} \to W^+bW^-\bar{b}\). Further details about the original dataset are available here, whereas a description of our modifications is presented in our paper. NOTE: The files provided here represent only the <em>training-set</em>, since it's what is diverse compared to the original HEPMASS. The label column has been renamed from "# label" to "type". There are two new columns: <em>name</em>, and <em>weight</em>. Steps to adapt `all_test.csv` (from HEPMASS): <pre><code class="language-python"># 1. Load csv df = pd.read_csv('&lt;your-path&gt;/all_test.csv') # 2. Rename columns df.rename(columns={'# label': 'type'}, inplace=True) # 3. Adjust mass column mass = np.sort(df['mass'].unique()) df.loc[df['mass'] == mass[0], 'mass'] = 500.0 # 4. Finally save the new csv df.to_csv('&lt;your-path&gt;/test.csv', index=False)</code></pre>
description: {"references": ["Baldi et al. (2015) HEPMASS", "Baldi et al. (2016) Parameterized Machine Learning for High-Energy Physics"]}
DOI: 10.5281/zenodo.6555246
title: Monthly-Mean Model Output for Paper Titled "Do Nudging Tendencies Depend on the Nudging Timescale Chosen in Atmospheric Models?"

description: These tarballs contains monthly-mean model output, which was primarily what was presented in the AGU JAMES paper titled "Do nudging tendencies depend on the nudging timescale chosen in atmospheric models?". Also included are the scripts used to set up these simulations, allowing reproducibility of the portion of the paper that used 3-hourly output. The 3-hourly output was not included, as it totaled ~7TB.
DOI: 10.5281/zenodo.6560182
title: Supplementary Material for ICT Practitioners' Perception of Working from Home During the Covid-19 Pandemic: Exploring Gender Differences

description: Supplementary Material for the paper <em>ICT Practitioners' Perception of Working from Home During the Covid-19 Pandemic: Exploring Gender Differences. </em> The .<em>pdf</em> file contains the form questions used to conduct the survey and the other files contain responses from survey participants.
DOI: 10.5281/zenodo.6686247
title: Gravity Wave Morphology During the 2018 Sudden Stratospheric Warming Simulated by a Whole Neutral Atmosphere General Circulation Model

description: This dataset includes a complete set of raw data, metadata and saved session data which is necessary for re-producing figures in a paper entitled "Gravity Wave Morphology During the 2018 Sudden Stratospheric Warming Simulated by a Whole Neutral Atmosphere General Circulation Model" submitted to the Journal of Geophysical Research - Atmosphere.
DOI: 10.5281/zenodo.6994648
title: Dataset for "A comprehensive model for pickup ion formation at the Moon"

description: Dataset for "A comprehensive model for pickup ion formation at the Moon", submitted to J. Geophys. Res.: Planets for publication. The dataset consists of in-situ measurements of ion fluxes and densities around the Moon as observed by NASA's ARTEMIS mission, as described in the manuscript.
DOI: 10.5281/zenodo.3250679
title: Themed Evidence: Reading Experiences - Gold Standard

description: Exploring digital resources in search of pieces of evidence relevant to a certain research theme is a difficult and important task for humanities research. The concept of evidence is a particularly difficult one, as it relates to a fact being reported in a text, which is relevant to a certain subject of enquiry. 

The UK Reading Experience Database Project (UK RED) is an open-access dataset containing records that document the history of reading in Britain from 1450 to 1945. Evidence of reading presented in UK RED is drawn from published and unpublished sources as diverse as diaries, commonplace books, memoirs, sociological surveys, and criminal court and prison records. See  http://www.open.ac.uk/Arts/reading/UK. 

This dataset is a gold standard for supporting the evaluation of competing methods for detecting themed evidence of "reading experience".
DOI: 10.5281/zenodo.3693686
title: Unified Bug Dataset

description: <strong>A Public Unified Bug Dataset for Java and its Assessment Regarding Metrics and Bug Prediction.</strong> Online appendix for the paper submitted to the <strong><em>Software Quality Journal</em></strong>. Bug datasets have been created and used by many researchers to build and validate novel bug prediction models. In this work, our aim is to collect existing public source code metrics based bug datasets and unify their contents. Furthermore, we wish to assess the plethora of collected metrics and the capabilities of the unified bug dataset in bug prediction.<br> We considered 5 public datasets and we downloaded the corresponding source code for each system in the datasets and performed source code analysis to obtain a common set of source code metrics. This way, we produced a unified bug dataset at class and file level as well. We investigated the diversion of metric definitions and values of the different bug datasets. Finally, we used a decision tree algorithm to show the capabilities of the dataset in bug prediction.<br> We found that there are statistically significant differences in the values of the original and the newly calculated metrics, furthermore, notations and definitions can severely differ. We compared the bug prediction capabilities of the original and the extended metric suites (within-project learning). Afterwards, we merged all classes (and files) into one large dataset which consists of 47,618 elements (43,744 for files) and we evaluated the bug prediction model build on this large dataset as well. Finally, we also investigated cross-project capabilities of the bug prediction models and datasets. We made the unified dataset publicly available for everyone. By using a public unified dataset as an input for different bug prediction related investigations, researchers can make their studies reproducible, thus able to be validated and verified.
DOI: 10.5286/isis.e.rb1810466
title: Probing the ground state magnetic structure of single crystalline Mn2V2O7

description: Mn2V2O7 exhibits reversible structural transition from the high temperature monoclinic phase (C2/m) to low temperature triclinic phase (P-1) near room temperature where Mn2+ ions form regular and distorted honeycomb network respectively. An antiferromagnetic like transition below 20 K as well as magnetic anisotropy were observed in this compound. We performed powder neutron diffraction measurement and this data fitted well with commensurate propagation vector (0,0.5,0) between  12 K and TN =20 K and Mn2+ shows the antiferromagnetic allignment. But below 12 K the magnetic structure is still to be explored where splitting of magnetic peaks indicates incommensurate structure and it is nontrivial to determine based on powder data. Hence, we propose here to carry out single crystal neutron diffraction study on Mn2V2O7 which is important to explicitly solve the ground state magnetic structure.
DOI: 10.5291/ill-data.5-32-855
title: From chiral helimagnet to frustrated spin liquid by tuning the Mn concentration in CoxZnyMnz alloys
DOI: 10.5281/zenodo.3250645
title: Themed Evidence: Listening Experiences - Gold Standard

description: Exploring digital resources in search of pieces of evidence relevant to a certain research theme is a difficult and important task for humanities research. The concept of evidence is a particularly difficult one, as it relates to a fact being reported in a text, which is relevant to a certain subject of enquiry. 

The Listening Experience Database Project (LED) - http://www.listeningexperience.org/ - is an initiative funded by the UK's Arts and Humanities Research Council (AHRC) aimed at collecting accounts of people’s private experiences of listening to music.

This dataset is a gold standard for supporting the evaluation of competing methods for detecting themed evidence of "listening experience".
DOI: 10.5281/zenodo.5562629
title: The Piraeus AIS Dataset for Large-scale Maritime Data Analytics

description: <strong>AIS data collected by the University of Piraeus' AIS receiver</strong> <strong>Abstract</strong> The advent of Big Data and streaming technologies has resulted in a swarm of voluminous, heterogeneous information, especially in the domains of Internet of Things (IoT) and transportation. Focusing on the maritime field, we present a dataset that contains vessel position information transmitted by vessels of different types and collected via the Automatic Identification System (AIS). The AIS dataset comes along with spatially and temporally correlated data about the vessels and the area of interest, including weather information. It covers a time span of over 2.5 years, from May 9<sup>th</sup>, 2017 to December 26<sup>th</sup>, 2019 and provides anonymised vessel positions within the wider area of the port of Piraeus (Greece), one of the busiest ports in Europe and worldwide. The dataset consists of over 244 million AIS records, an average of more than 10,000 records per hour, which makes it an ideal input for large-scale mobility data processing and analytics purposes. <strong>Dataset related to the following publication</strong> Andreas Tritsarolis, Yannis Kontoulis, Yannis Theodoridis, The Piraeus AIS dataset for large-scale maritime data analytics, Data in Brief, Volume 40, 2022, 107782, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2021.107782. <strong>Files Description</strong> <strong>ais_static</strong>: CSV flat files containing vessels' static information and their corresponding types <strong>geodata</strong>: ESRI Shapefiles containing several geographic-related data (e.g. harbours, islands, etc.) <strong>noaa_weather</strong>: ESRI Shapefiles containing weather forecast from GRIB files (as provided by NOAA) <strong>unipi_ais_dynamic</strong>: CSV flat files containing AIS kinematic information <strong>unipi_ais_dynamic_synopses</strong>: CSV flat files containing metadata (i.e. synopses) regarding vessels' AIS positions <strong>Privacy Statement</strong> <strong>For privacy-related queries, please contact the authors</strong>
DOI: 10.5281/zenodo.6806984
title: LFW-Beautified: A Dataset of Face Images with Beautification and Augmented Reality Filters

description: <strong>LFW-Beautified: A Dataset of Face Images with Beautification and Augmented Reality Filters</strong> <strong>Usage</strong> Download the compressed files (13 in total) and uncompress them <strong>Please cite reference 1) below in your publications if you make use of the data of this repository</strong> <strong>People & Contact</strong> Fernando Alonso-Fernandez (contact person). <strong>References</strong> Hedman, P., Skepetzis, V., Hernandez-Diaz, K., Bigun, J., Alonso-Fernandez, F., "On the Effect of Selfie Beautification Filters on Face Detection and Recognition" https://arxiv.org/abs/2110.08934 Hedman, P., Skepetzis, V., The Effect of Beautification Filters on Image Recognition: "Are filtered social media images viable Open Source Intelligence?" Master Thesis at Halmstad University, Sweden (Master’s Programme in Network Forensics) http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44799
DOI: 10.5281/zenodo.6859071
title: Replication data [The who, what and how of the current research at the Brazilian Symposium on Software Engineering]

description: Replication Data for the SBES paper <em>"The who, what and how of the current research at the Brazilian Symposium on Software Engineering"</em> Dataset containing analysis (who, what and how) of 90 SBES papers: 27 from SBES’19, 43 from SBES’20, and 20 from SBES’21.
DOI: 10.5441/001/1.8d8385j0
title: Data from: Stink or swim: techniques to meet the challenges for the study and conservation of small critters that hide, swim or climb and may otherwise make themselves unpleasant

description: Powell RA, Ellwood S, Kays R (in prep) Stink or swim: techniques to meet the challenges for the study and conservation of small critters that hide, swim or climb and may otherwise make themselves unpleasant. In Harrington L, Macdonald DW, eds, Biology and Conservation of Mustelids and Procyonids.
description: GPS tracking units have limited performance below thick vegetation, despite technological improvements, and will always perform better on animals in or above forest canopy.
DOI: 10.6084/m9.figshare.19719898
title: DAVIS-NUIUIED: A DAVIS-based non-uniform illumination underwater image enhancement dataset.

description: A non-uniform illumination underwater image enhancement dataset was built using a neuromorphic vision sensor (DAVIS). The dataset contains event streams and frames, which can be used for research and experimental verification of events and frame fusion algorithms.
DOI: 10.6084/m9.figshare.19795243.v5
title: Material Complementar

description: Material Suplementar da Análise qualitativa do estudo - Desenvolvedores em foco! Análise da Developer Experience em uma ferramenta de modelagem colaborativa
DOI: 10.17037/data.00002442
title: Data for: "Sanitation-related Quality of Life (SanQoL) in Maputo, Mozambique"

description: An anonymised dataset of 424 individuals across 272 clusters (compounds) who gave written informed consent to participate in a study of sanitation-related quality of life in the Nhlamankulu district of Maputo, Mozambique in May 2019. The dataset contains variables on compound code, socio-demographic information, sanitation infrastructure, SanQoL outcomes, ranking of SanQoL attributes, mental wellbeing outcomes, and asset ownership.
description: Face-to-face interviews by enumerators in Portuguese using mobile phones and mWater software
DOI: 10.21947/1349273
title: Lidar - LLNL ZephIR Profiler, Arlington Airport - Reviewed Data

description: These are 10-min averaged data from a vertical-profiling ZephIR 300 lidar. Data collected at the Arlington airport for the WFIP2 project. March 10, 2016 - April 1, 2017
DOI: 10.21947/1412529
title: Radar - ARL Wind Profilerwith RASS, Boardman - Reviewed Data

description: Wind Profiler with Radio Acoustic Sounding System (RASS) data.