cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17903/fk2/awssd3
title: What2vote: Voting Advice Application Data for the May and June 2023 Parliamentary Elections in Greece. Friedrich-Ebert-Stiftung Athens.

description: The data presented in the accompanying files were collected through the Voting Advice Application (VAA) What2vote (www. what2vote.gr), which was designed for the May and June 2023 parliamentary elections in Greece. VAAs are contemporary technological solutions that aim to assist voters in making informed decisions on their vote choice. They do so primarily by comparing the positions of voters to the positions of candidates or political parties on a set of relevant policy issues and informing voters about which is the closest party or candidate to their preferences. This dataset contains user responses of the online survey and the associated positions of political parties.
DOI: 10.17909/787k-c897
title: SN 2023ixf UV Spectra Day 14-66

description: HST/STIS UV Spectra of SN 2023ixf on day 14, 19, 24, and 66
DOI: 10.24432/c5r88h
title: gene expression cancer RNA-Seq
DOI: 10.25318/2210014301-eng
title: Smartphone personal use and selected smartphone habits by gender and age group

description: Percentage of Canadians using a smartphone for personal use and selected habits of use during a typical day.
DOI: 10.25452/figshare.plus.25330501
title: A dataset of over one thousand computed tomography scans of battery cells

description: 1015 commercially available batteries were scanned using a X-ray computed tomography system. These scans were processed into a set of images that capture the major axes of the battery. For cylindrical cells, these are radial and axial views of the battery. For pouch and prismatic cells, these are x-y, x-z, and y-z views of the battery.This dataset is available under a CC-BY-NC-SA-4.0 license.<br><b>500</b> 2170 cylindrical cells (114GB)<b>400</b> 18650 cylindrical cells (85GB)<b>50</b> 18650 sodium-ion cylindrical cells (10.4GB)<b>25</b> 2170 high-energy density cylindrical cells (5.8GB)<b>25</b> 4680 cells (4.4GB)<b>10</b> Pouch cells (2.0GB)<b>5</b> Prismatic cells (2.2GB)<i>For additional details about this dataset, including the directory structure of the dataset and details of our methods, see our data paper "A dataset of over one thousand computed tomography scans of battery cells" at </i><i>https://doi.org/10.48550/</i><i>arXiv.2403.02527</i>
DOI: 10.5281/zenodo.10560140
title: Measurable indicators of the Slovak state institutions´ websites

description: Directly measurable indicators of the Slovak state institutions´ websites collected by using online tools.
DOI: 10.5281/zenodo.10706416
title: Dataset for Authentication and Authorization using Physical Layer Properties in Indoor Environment
DOI: 10.5281/zenodo.11467142
title: Simulation data for: Ocean Circulation on Tide-locked Lava Worlds Simulated by an Idealized 2D Model

description: The simulation data are used to produce the figures in the following two manuscripts: “Ocean Circulation on Tide-locked Lava Worlds, Part I: An Idealized 2D Numerical Model” and “Ocean Circulation on Tide-locked Lava Worlds, Part II: Scalings”. All files are nc files created by Matlab scripts. For the codes in detail, please refer to https://github.com/YanhongLai/Two-dimensional-model-for-magma-ocean-on-lava-worlds.git .

 

In each RAR file, several nc files are contained, including the snapshot results (named as Snapshot*.nc) and time series results (named as twod*.nc) of the simulation. The model setup of each simulation is described in detail below. 

 

Simulation data used in the manuscript of Part I: An Idealized 2D Numerical Model

1)     “PartI-thermal-forcing-Az1E1.rar”: simulation data for the simulation under thermal forcing only, including potential temperature, density, zonal velocity, vertical velocity, pressure, sea surface height, and so on. The baseline values of oceanic and planetary parameters used in this simulation can be found in Table 1 in the manuscript. Figures 1(c & d), 4, 5(a), 7(a & d), and A1(a-c) in the manuscript are generated by these data.

2)     “PartI-wind-forcing-Az1E2.rar”: simulation data for the simulation under both thermal and wind forcings. Figures 5(b), 6, 7(b & e), and A1(d-f) in the manuscript are generated by these data.

 

Simulation data used in the manuscript of Part II: Scalings

1)     Simulation data under varying vertical viscosities (Az), and the baseline values used in the simulations are summarized in Table 1 of the manuscript. Only thermal forcing is imposed.

“PartII-thermal-forcing-Az1E0.rar”: Az = 1e0 m2s-1

“PartII-thermal-forcing-Az1E2.rar”: Az = 1e2 m2s-1

 

2)     Simulation data under varying vertical diffusivities (kz). Only thermal forcing is imposed.

“PartII-thermal-forcing-kz1E-5.rar”: kz = 1e-5 m2s-1

“PartII-thermal-forcing-kz1E-6.rar”: kz = 1e-6 m2s-1

 

3)     Simulation data under varying substellar temperatures (Tsub). Only thermal forcing is imposed.

“PartII-thermal-forcing-Tsub2500K.rar”: Tsub = 2500 K

“PartII-thermal-forcing-Tsub2750K.rar”: Tsub = 2750 K

“PartII-thermal-forcing-Tsub3250K.rar”: Tsub = 3250 K

 

4)     Simulation data under varying wind stresses (taux). Both thermal and wind forcings are imposed.

“PartII-wind-forcing-over3-Az1E1.rar”: taux = taux0/3, taux0 = 100 Nm-2 is the wind stress amplitude of the control simulation.

“PartII-wind-forcing-over5-Az1E1.rar”: taux = taux0/5

“PartII-wind-forcing-over10-Az1E1.rar”: taux = taux0/10

 

All simulation data in Part II are used to create the scatter points in Figures 3 & 4 in the manuscript.
DOI: 10.5281/zenodo.2539147
title: Atmospheric climate model output of the COSMO-CLM2 regional climate model hindcast run over Antarctica (1987-2016)

description: The dataset contains monthly output of a COSMO-CLM² (COSMO-CLM coupled to the Community Land Model) atmospheric hindcast simulation over Antarctica which is described and evaluated in the following paper: 

Souverijns, N., Gossart, A., Demuzere, M., Lenaerts, J.T.M., Medley, B., Gorodetskaya, I.V., Vanden Broucke, S., van Lipzig, N.P.M., 2019. A new Regional Climate Model for POLAR-CORDEX: Evaluation of a 30-year Hindcast with COSMO-CLM² over Antarctica. Journal of Geophysical Research: Atmospheres, 124, 1405-1427. (doi:10.1029/2018JD028862)

Details of the model simulation:<br>
- COSMO-CLM version 5.0_clm6<br>
- Community Land Model version 4.5<br>
- Horizontal resolution: 0.25°x0.25°<br>
- Vertical resolution: 40 levels<br>
- Time period: 1987-2016 (excluding 4 years of spin-up)<br>
- Driving model: ERA-Interim<br>
 

The data provided here has a monthly time resolution and contains the monthly average of all variables except denoted otherwise below. As such, each file consists of 360 time steps.<br>
- AEVAP_S: Surface evaporation [kg m-2] (summed value for each month)<br>
- ALB: Surface albedo [-] (only for austral summer months)<br>
- ALHFL_S: Surface latent heat flux [W m-2]<br>
- ALWD_S: Downward longwave radiation at the surface [W m-2]<br>
- ALWU_S: Upward longwave radiation at the surface [W m-2]<br>
- ASHFL_S: Surface sensible heat flux [W m-2]<br>
- ASOB_S: Surface net downward shortwave radiation [W m-2]<br>
- ASWDIFD_S: Diffuse downward shortwave radiation at the surface [W m-2]<br>
- ASWDIFU_S: Diffuse upward shortwave radiation at the surface [W m-2]<br>
- ASWDIR_S: Direct downward shortwave radiation at the surface [W m-2]<br>
- ATHB_S: Surface net downward longwave radiation at the surface [W m-2]<br>
- P: Pressure at 40 vertical levels [Pa]<br>
- QV: Specific humidity at 40 vertical levels [kg kg-1]<br>
- RH2M: Relative humidity at 2 meter [%]<br>
- SNOW_GSP: Surface snowfall amount [kg m-2] (summed value for each month)<br>
- T2M: Temperature at 2 meter [K]<br>
- T: Temperature at 40 vertical levels [K]<br>
- WS10M: Wind speed at 10 meter [m s-1]<br>
- WS: Wind speed at 40 vertical levels [m s-1]
DOI: 10.5281/zenodo.3256298
title: Preprints in biology as a fraction of the biomedical literature

description: These data and chart present an approximate calculation of the proportion of preprints in biology when compared to publications in PubMed, based on monthly figures and incorporating monthly preprint submissions (or counts) across a selection of servers relevant to biology. 

Version 1.0 of these data represents data from January 2007 until May 31, 2019 for preprint servers: arXiv q-bio, Nature Precedings, F1000Research*, PeerJ Preprints*, bioRxiv**, Winnower*, preprints.org, Wellcome Open Research*. 

* Counts may not be specific to biology preprints only; ** Counts may include all versions posted that month, so may be an overestimate for version 1 submissions.

From January 2019, data has been gathered manually by the authors, as per the methods described in the .csv here, and is included here in 'Preprints_per_month_direct_2019-01to05.csv'. Until December 2018, monthly preprint submissions data are based on those contributed by Jordan Anaya (ORCID: https://orcid.org/0000-0002-6166-4113) for PrePubMed, source: https://raw.githubusercontent.com/OmnesRes/prepub/master/analyses/preprint_data.txt; Github repository: https://github.com/OmnesRes/prepub; website: http://www.prepubmed.org). Data are not included here, they are provided from the source linked above under MIT license associated with the website code: https://github.com/OmnesRes/prepub/blob/master/LICENSE.

A live version of these data and the chart are available from this GSheet: https://docs.google.com/spreadsheets/d/1bkGEcfQcL0LpIanVqNHci1ZFY6oVNGz7IQbEugzkqkU/edit?usp=sharing. Between version updates here, please refer to this sheet for updated counts and method updates e.g. to include more servers and ensure only version 1 submissions are counted.

For more information, please contact naomi.penfold@asapbio.org.

When presenting these data and/or chart, please attribute to ASAPbio (https://asapbio.org, twitter: @ASAPbio_).
DOI: 10.5281/zenodo.3934932
title: IDTReeS 2020 Competition Data

description: Data provided by the Integrating Data science with Trees and Remote Sensing (IDTReeS) research group for use in the IDTReeS Competition. Geospatial and tabular data to be used in two data science tasks focused on using remote sensing data to quantify the locations, sizes and species identities of millions of trees and on determining how these methods generalize to other forests. Vector data are the geographic extents of Individual Tree Crown boundaries that have been identified by researchers in the IDTReeS group. The data were generated primarily by Sarah Graves, Sergio Marconi, and Benjamin Weinstein, with support from Stephanie Bohlman, Ethan White, and members of the IDTReeS group. Remote Sensing and Field data were generated by the National Ecological Observatory Network (NEON, Copyright © 2017 Battelle). Data were selected, downloaded, and packaged by Sergio Marconi. The most recent available data of the following products are provided: National Ecological Observatory Network. 2020. Data Product DP1.30010.001, High-resolution orthorectified camera imagery. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020. National Ecological Observatory Network. 2020. Data Product DP1.30003.001, Discrete return LiDAR point cloud. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020. National Ecological Observatory Network. 2020. Data Product DP1.10098.001, Woody plant vegetation structure. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020. National Ecological Observatory Network. 2020. Data Product DP3.30015.001, Ecosystem structure. Provisional data downloaded from http://data.neonscience.org on March 4, 2020. Battelle, Boulder, CO, USA NEON. 2020. NEON has the following data policy: ‘The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle Memorial Institute. This material is based in part upon work supported by the National Science Foundation through the NEON Program.’ THE NEON DATA PRODUCTS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE NEON DATA PRODUCTS BE LIABLE FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE NEON DATA PRODUCTS.
description: This data is supported by the National Science Foundation through grant 1926542 and by the Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative through grant GBMF4563 to E.P. White, and the NSF Dimension of Biodiversity program grant (DEB-1442280) and USDA/NIFA McIntire-Stennis program (FLA-FOR-005470).
DOI: 10.5281/zenodo.4709851
title: MISOMIP2 ocean interpolation test cases

description: Here we provide a few <strong>test cases for the MISOMIP2 ocean interpolation package</strong> (https://github.com/nicojourdain/misomip2). The aims are : help users check that this package works well in their python environment before adapting it to their model outputs. help developers check that new developments work for all types of model. The test cases characteristics are the following: <strong>NEMO_test</strong> : 2 months, a small NEMO domain in the Amundsen Sea (smaller than the MISOMIP2 domain) at medium resolution, in Z coordinates, with all required variables [provided by Nico Jourdain]. <strong>MITGCM_test</strong> : 2 months, a small MITGCM domain in the Amundsen Sea (smaller than the MISOMIP2 domain) at relatively high resolution in stereographic coordinates; vertical Z coordinates; does not contain the required sea-ice or surface fluxes [provided by Jan De Rydt]. <strong>eORCA025_test</strong> : 2 months, a global NEMO domain at medium resolution, in Zstar coordinates; most variables calculated [provided by Pierre Mathiot]. <strong>ROMS_test</strong> : 3 months, a circum-Antarctic simulation at medium resolution, in sigma coordinates; does not contain sea-ice or surface fluxes [provided by Ole Richter]. We do not provide more information on the individual model configurations as the aim is here only to test interpolation scripts.
DOI: 10.5281/zenodo.5914554
title: Data for the NeonTreeEvaluation Benchmark

description: This dataset is the data files for the NeonTreeEvaluation Benchmark for individual tree detection from airborne imagery. For each geographic site, given by the NEON four letter code (e.g HARV -&gt; Harvard Forest), there are up to 4 files: a RGB image, a LiDAR tile, and a 426 band hyperpspectral file, and a 1m canopy height file. For more information on the benchmark, and the corresponding R package, see https://github.com/weecology/NeonTreeEvaluation_package Training.zip and Evaluation.zip both have the same folder structure with RGB, Hyperspectral, LiDAR and CHM folders. All annotations are in the annotation.zip. Not all files in the evaluation.zip have corresponding annotations. We have included these to allow users to test their approaches, annotate new data, or use semi-supervised methods.
DOI: 10.5445/ir/1000127148
title: Restrictive and Opportunistic Method in Design for Manufacturing

description: The main objective of design for manufacturing (DfM) is to ensure and simplify the manufacturing of products and reduce manufacturing costs. The problem is, that it remains unclear, which challenges and difficulties are most frequently encountered when considering the manufacturing process during the design phase. Only few studies have objectively investigated the influence of a restrictive method on the design results. The aim of this research is to investigate the effect of restrictive method on manufacturability and the manufacturing effort of the design outcome for design engineers and students. Design engineers as well as students designed and selected concepts with a high manufacturing effort. Successful approaches were those, which designed concepts completely without welding joints by cleverly redesigning the design and still fulfil the function. Hence, we present successful approaches as an opportunistic method in DfM, which can be transferred to further manufacturing processes.
DOI: 10.6073/pasta/86d8d176e91410566b4de51df44c2624
title: Missouri Lakes and Reservoirs Long-term Limnological Dataset

description: This data set compiles 43 years of limnological data from Missouri
 lakes and reservoirs collected by the University of Missouri Limnology
 Lab. Although the dataset includes information from nine different
 projects, the bulk of the data (~75%) come from the Statewide Lake
 Assessment Project and the Lakes of Missouri Volunteer Program, both
 of them funded primarily by Missouri Department of Natural Resources.
 The Statewide Lake Assessment Project began in 1978 sampling a small
 set of reservoirs. In 1989 the assessment expanded to include regular
 annual summer monthly collections between May and August, though
 monitoring was extended for some reservoirs in certain years. We
 monitored 240 lakes to create the dataset, which represents over 2600
 lake-years. The Lakes of Missouri Volunteer Program began in 1992
 monitoring 5 lakes and reservoirs and has expanded to 121 sites on 65
 waterbodies. Volunteer community scientists monitor their respective
 sites approximately 8 times per season (April through September). This
 dataset represents over 15,000 sample events. Lake Ozarks is a
 long-term (1976-2014) spatial examination of a single large reservoir
 during summer. Table Rock Monitoring is another multi-year (1995-2009)
 spatial examination of a large reservoir, but includes year-round
 data. The rest of the projects included in the dataset monitored
 Missouri lakes and reservoirs at various intervals including daily
 (Woodrail, Daily), weekly (icubed), and biweekly (High Res).
DOI: 10.6084/m9.figshare.19161206.v1
title: Do Microcredentials Help New Workers Enter the Market? Evidence from an Online Labor Platform

description: <b>Do Microcredentials Help New Workers Enter the Market? Evidence from an Online Labor Platform </b><br>Code and data accompanying the work to produce figures of the paper 'Do Microcredentials Help New Workers Enter the Market? Evidence from an Online Labor Platform'. (forthcoming in the Journal of Human Resources)<br><b>Authors</b>Otto Kässi (otto.kassi@etla.fi)Vili Lehdonvirta<br><b>Abstract</b>We investigate the effects of a voluntary microcredentialing scheme on an online freelancing labor market. Drawing on transaction-level data, we show that obtaining a microcredential increases workers’ earnings. This effect is not driven by increased worker productivity but by decreased employer uncertainty. The increase in worker earnings is realized through an increase in the value of the projects won rather than an increase in the number of projects. We also find that the effect of microcredentials is lower for more experienced workers, which suggests that signaling by microcredentials and other forms of verified information are partial substitutes. <br><b>Citation</b>Kässi, O., & Lehdonvirta, V. (2018, November 15). Do Microcredentials Help New Workers Enter the Market? Evidence from an Online Labor Platform. https://doi.org/10.31235/osf.io/7tybd<br><b>Data</b>The data for this project are proprietary and we cannot legally share them. However, if any researchers are interested in getting similar data, we will try to help them do this. Please contact otto.kassi@etla.fi.<br>See <i>readme.md</i> for details.<br>
DOI: 10.6084/m9.figshare.24480850
title: Meta-analysis of the make-up and properties of in vitro models of the healthy and diseased blood–brain barrier

description: Source data for a meta-analysis of the BBB permeability, TEER, and tight junction protein expression
DOI: 10.6084/m9.figshare.24902100
title: Collection of pearl millet RNA sequencing data

description: Pearl millet (<i>Pennisetum glaucum</i>, also known as <i>Cenchrus americanus</i>) is a C4 cereal crop that can tolerate stressed conditions including drought-stressed, high temperature-stressed and nutrient-poor conditions. Transcriptomes of pearl millet were studied by RNA sequencing (RNA-Seq) to understand mechanisms regulating its development and tolerance to such stressed conditions in previous studies. We collected RNA-Seq reads from as many of such studies in the NCBI (National Center for Biotechnology Information) BioProject database as popssible, and mapped them to the pearl millet reference genome to obtain read counts and transcripts per million (TPM) for each pearl millet gene. Here, the resulting count and TPM data as well as the attributes of the samples used for the RNA-Seq are provided. These data can be updated when a new study with RNA-Seq of pearl millet samples has become available.
DOI: 10.6084/m9.figshare.26641888
title: Supplementary Material for: Local climate change adaptation under the lenses of public health: a case study from Porto, Portugal

description: Introduction: Climate change is a pressing public health issue. Urban populations, especially in coastal areas, are highly vulnerable. As climate change progresses, local adaptation becomes increasingly important. We present a case study about the inclusion of public health concerns in local climate change adaptation in Porto (Portugal). 
Methods: We analyzed two local adaptation plans using qualitative content analysis and conducted semi-structured interviews with a purposeful sample of 6 key stakeholders with different profiles. We did a qualitative content analysis of the respective transcripts.
Results: Porto is undergoing health-relevant consequences of climate change, which are expected to further worsen in the future. Porto's geographical and demographic characteristics, coupled with its urban environment, make its population highly vulnerable to climate change-related health risks. Public health is recognized as a central element in local adaptation efforts. Drivers for integrating health concerns include growing climate change awareness, a sense of urgency, social capital, institutional networks, access to resources, and political commitment. Nevertheless, challenges like data limitations, resource constraints, climate knowledge gaps, communication issues, and political cycles hinder both local adaptation and the integration of health considerations.
Discussion/Conclusion: In Porto, health seems both a powerful mobilizing issue and a central topic concerning local adaptation. However, the complex and long-term nature of climate change and the associated uncertainty hinder adaptation efforts. High-quality data about both the local climate and population health are thus essential. The transversal nature of risk is recognized and multi-sectorial approaches, public participation, mainstreaming and policy integration are necessary to prevent imbalances. Local adaptation efforts, including health-related efforts, are shaped by the international (belonging to the European Union), national and local contexts. Successful local adaptation and inclusion of health aspects requires mainstreaming and policy integration across different areas, involvement of multiple stakeholders, including the population, in order to maximize resources and avoid undesired trade-offs.