cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4225/87/usgeqs
title: National Drug Strategy Household Survey, 2013

description: The 2013 National Drug Strategy Household Survey was conducted between July and November of 2013 and examines current awareness of attitudes and behaviours toward drugs and drug problems within the Australian community, focussing on respondents' personal attitudes toward drugs, knowledge of drugs and drug histories. This is the eleventh survey in a series of national household surveys and is the sixth in the series conducted by the Australian Institute of Health and Welfare (AIHW). The series also includes the National Campaign Against Drug Abuse: Social Issues in Australia Surveys of 1985, 1988 and 1991. These studies were followed by the National Drug Strategy Household Surveys of 1994, 1995, 1998, 2001, 2004, 2007 and 2010. The survey included questions regarding respondents' personal drug and alcohol experience and behaviour, opinions on drug policy and legislation, availability of drugs and alcohol, injury and harm from drugs and alcohol, and attitudes towards the use of alcohol and other drugs. The drugs covered included: tobacco/cigarettes, alcohol, pain-killers/analgesics, tranquillisers, heroin, methadone, inhalants, ketamine, GHB, ecstasy, hallucinogens, cocaine, meth/amphetamines, other opiates, cannabis/marijuana, synthetic cannabis, other psychoactive substances, and steroids. Demographic and background variables included: state of residence, age, sex, marital status, self-assessed health status, sexuality, Indigenous status, country of birth, language spoken at home, employment status, occupation, level of education, income, index of socio-economic advantage and disadvantage, remoteness area and household composition.
DOI: 10.4225/87/vtuv4c
title: National Drug Strategy Household Survey, 2010

description: The 2010 National Drug Strategy Household Survey was conducted between April and September of 2010 and examines current awareness of attitudes and behaviours toward drugs and drug problems within the Australian community, focussing on respondents' personal attitudes toward drugs, knowledge of drugs and drug histories. This is the tenth survey in a series of national household surveys and is the fifth in the series conducted by the Australian Institute of Health and Welfare (AIHW). The series also includes the National Campaign Against Drug Abuse: Social Issues in Australia Surveys of 1985, 1988 and 1991. These studies were followed by the National Drug Strategy Household Surveys of 1994, 1995, 1998, 2001, 2004 and 2007. The survey included questions regarding respondents' personal drug and alcohol experience and behaviour, opinions on drug policy and legislation, availability of drugs and alcohol, injury and harm from drugs and alcohol, and attitudes towards the use of alcohol and other drugs. The drugs covered included: tobacco/cigarettes, alcohol, pain-killers/analgesics, tranquillisers, heroin, methadone, inhalants, ketamine, GHB, ecstasy, hallucinogens, cocaine, meth/amphetamines, other opiates, cannabis/marijuana, and steroids. Demographic and background variables included: state of residence, age, sex, marital status, self-assessed health status, sexuality, Indigenous status, country of birth, language spoken at home, employment status, occupation, level of education, income, index of socio-economic disadvantage and household composition.
DOI: 10.48420/21335622
title: Interactive information dashboards user performance, low-level interaction data and interaction patterns.

description: A study on four interaction and information presentation dashboard problems: 1. information overload, 2. inappropriate data order and grouping 3. ineffective data presentation 4. misalignment in visual literacy expectations <br> Each dashboard had two types: problematic and adapted. Then each type had two tasks. <br> <strong>File 1 (dependent and independent variables):</strong> 63 participants completed the experiment, so for each user, we have user graph literacy (0-4), effectiveness(0 or 1 for each task), efficiency (completion time in minutes for each task), perceived performance (0-20 for each task) and perceived cognitive demand (0-20 for each task). <br> <strong>File 2 (interaction data):</strong> 50 participants interacted with the dashboards while their low-level inttraction data was being collected using UCIVIT tool. The zip file contains 50 JSON files for each participant on eight dashboards. <br> <strong>File 3 (interaction patterns):</strong> Using VMSP sequential pattern mining algorithm, we mined the low-level interaction data to find the patterns of behaviour exhibited by the participants. This file explains the benchmarking used to arrive at the parameters used and lists all the patterns found on each dashboard. It also further explains all the interpretations of events on all elements.
DOI: 10.5067/fpsu0v1mwub6
title: MEaSUREs BedMachine Antarctica, Version 3
DOI: 10.5067/modis/mcd06cosp_m3_modis.062
title: MODIS (Aqua/Terra) Cloud Properties Level 3 monthly, 1x1 degree grid
DOI: 10.5281/zenodo.10875617
title: Infographics PBL NDC Tool March 2024

description: The dataset of the emissions projections of the NDCs and current policies for the PBL Climate Pledge NDC tool: https://themasites.pbl.nl/o/climate-ndc-policies-tool/

The PBL Climate Pledge NDC tool addresses the following three key questions:

1.    What are the countries’ emissions projections for the NDCs for 2030?

2.    Will the projected aggregated impact of the fully implemented NDCs on global emissions, for 2030, be sufficient to achieve the target of staying well below 2 °C / 1.5 oC?

3.    Are countries on track to meet their original NDCs for the period up to 2030?

To address these questions, the tool shows the projected impact of the emission reduction proposals (NDCs) and current policies, per country and globally, on greenhouse gas emissions up to 2030. This tool analyses the mitigation components of the NDCs of 144 of the 197 Parties (together representing about 95% of emissions in 2019). More specifically, the tool shows the effect of:



Pledges: national and global greenhouse gas emission projections for 2020, assuming that pledges will be fully achieved;

Nationally Determined Contributions (NDCs): national and global greenhouse gas emission projections for 2030, assuming that NDCs will be fully achieved;

Current policies: the impact of national climate and energy policies on emission levels up to 2030 (at least, unless the policy specifies a later end date), for 25 major emitting countries and regions.


Recommended citation:

den Elzen, M.G.J., Dafnomilis, Hooijschuur, E., Nascimento, L., Kuramochi, T., Forsell, N., Gutiérrez, Z.A., Gusti (2024), Infographics PBL NDC Tool 2024. Zenodo. 10.5281/zenodo.10875617
DOI: 10.5281/zenodo.13370207
title: GWAS summary statistics for The genetic architecture and evolutionary consequences of the human pelvic form
DOI: 10.5281/zenodo.13683421
title: Predictors of Substorm Onset Conjugate Displacement (Dataset)

description: We provide the mediated list containing the onset locations of substorm onsets in the northern and southern hemisphere. The northern hemisphere onsets have been obtained from Frey et al (2004), who use IMAGE WIC and SI13, and the southern hemisphere onsets have been selected in the accompanying paper using Polar VIS.
DOI: 10.5281/zenodo.13692162
title: zhgarfield/sociallearningdata: Update 1

description: This release includes updates and fixes to variable names and additional data associated with bias assessment methods, including data on ethnographer type and ethnographic source quality data publically available and provided by eHRAF.
DOI: 10.5281/zenodo.13984465
title: Importance of Longwave Radiative Forcing by Icy Clouds in Maintaining Miocene High-latitude Warmth

description: Supporting Information for Importance of Longwave Radiative Forcing by Icy Clouds in Maintaining Miocene High-latitude Warmth

NetCDF files: B.MIO_400_C5.cam2.h0.last50yr_climo_monthly.nc is from our control experiment, and B.MIO_400_C5_MK_TeoKor_Dem1SLF_2.cam2.h0.last50yr_climo_monthly is from our modified experiment.

cldwat2m_micro.F90, microp_aero.F90, microp_driver.F90, and zm_conv.F90 are the source code used in our modified experiment.
DOI: 10.5281/zenodo.14449495
title: Global human modification datasets of terrestrial ecosystems from 1990 to 2020

description: We developed datasets on the human modification of global terrestrial ecosystems from 1990 to 2020. The methods and data sources associated with these data are fully described in:

Theobald, D.M., Oakleaf, J.R., Moncrieff, G., Voigt, M., Kiesecker, J., and Kennedy, C.M. <in review>. Global extent and change in human modification of terrestrial ecosystems from 1990 to 2022. Scientific Data.

For each 5-year step from 1990 to 2020, 9 raster datasets are provided in cloud-optimized GeoTIFF format (300 m resolution, EPSG:4326). The naming convention is as follows: HMv2024080101_<year>c_<type>_300, where <year> is: 1990, 1995, 2000, 2005, 2010, 2015, or 2020; “c” signifies a data consistent for the change datasets for 1990-2020; and <type> is an intermediate aggregation of threats at for each IUCN taxonomy class, where: AA=all threats combined, AG=agricultural, BU=residential, commercial and recreation areas, EX=energy production and mining; FR=biological resource use; HA=human accessibility; NS=natural systems modification; PO=pollution; and TI=transportation and service corridors. Values are floating-point numbers, ranging from 0.0 - 1.0 (no modification, full modified).

These data are available as Google Earth Engine assets via this script (including 90 m): https://code.earthengine.google.com/1b7b5976fdd6189c6533ca00a46386d1

The Google Earth Engine script to calculate human modification is here: https://code.earthengine.google.com/59c0f7da25579422ce4d459abeae1b7d

The Google Earth Engine script to clip out custom extents and export to GeoTIFF is here: https://code.earthengine.google.com/44c9f092472edb9bac3c45096aa5091d

Please see companion repo here for datasets for 2022: https://zenodo.org/uploads/14502573
DOI: 10.5281/zenodo.14544791
title: AMIE-TIEGCM_24April 2023_Indian longitude
DOI: 10.5281/zenodo.14580147
title: Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from 21 CMIP6 models

description: Accurate snow depth datasets are of paramount importance for water resource management, comprehensive climate change assessments, and the sustainable development of the ice-and-snow economy. To create a high-resolution monthly snow depth dataset tailored for the Northern Hemisphere winter months (NHMSD), this study employed the Delta statistical downscaling method, in conjunction with a spatial feature transfer technique, to refine snow depth data derived from 21 major general circulation models and four shared socioeconomic pathways sourced from the CMIP6 project. The NHMSD stands as the world's pioneering long-term 0.05° snow depth dataset, encompassing the historical era from 1980 to 2014 and extending into future projections from 2015 to 2100. Validation using 2062 ground snow depth observations has confirmed that NHMSD outperforms reanalysis datasets, including ERA5-Land and GLDAS, in terms of root mean square error, bias, and mean absolute error for the periods 1980–2014 and 2015–2023. 

Due to data upload size limitation, we provide DOIs for different model data of NHMSD in (NHMSD 1980-2100.txt), which can be downloaded by users as needed.
DOI: 10.5281/zenodo.14606666
title: Diversity of radial variations of wood properties in European beech reveals the plastic nature of juvenile wood - Dataset

description: The long-term (as opposed to short-term intra-ring) radial variation of wood properties in European beech (Fagus sylvatica L.) from pith to bark are largest in the young ages of the tree (internal core). This so-called juvenility reflects both cambium ageing (ontogenetic juvenility) and adaptation to the changing mechanical constraints during secondary growth (adaptive juvenility). Ring width (RW), specific gravity (SG) and specific modulus (SM) are important parameters for each new wood layer, needed for the study of mechanical stability of a standing tree. They should be sensitive to the mechanical adaptation of growth. They were measured on diametrical boards (North/South direction) issued from 86 trees from several high forest stands in European countries. Analysis of variance showed very significant influence of position within the tree (core/external), of trees within a plot and of plots, but not for North/South orientation. The share of variance was similar for SG and SM (importance of tree effect) but different for RW (importance of plot effect). The occurrence of red heartwood in the core on some trees had a significant influence, mostly on SM, but the differences between white and red wood was very small. Globally the variability was high for RW, rather small for SM and very small for SG. Accordingly, the variations of the modulus of elasticity (product of SG and SM) were much more influenced by SM than by SG for beech. The radial variations of each parameter were fitted by both a linear (2 coefficients: zero value and mean slope) and a parabolic curve (3 coefficients: zero value, initial slope and curvature). They were used to classify types or radial profile in terms of flat, up & down and straight, convex & concave. Median values of coefficients per plot (or total) were used to draw median profiles for each parameter per plot and at the global level. The median global profiles differed from the typical radial pattern (TRP) of juvenility for plantation softwoods for SG (down concave instead of up concave) and SM (convex like TRP but with a clear decrease in the mature wood). The main result was the very large variability of profiles between trees or even between plots. Even if there is a part of ontogenetic influence in the juvenile patterns for RW, SG and SM, the results suggest that the influence of mechanical constraints on tree growth (adaptive juvenility) dominates largely. 

The provided file contains the data and the sheets developped to analyse them. It serves as supplementary material for the related paper submitted for recommendation to the PCI Forest and Wood Science.
DOI: 10.5281/zenodo.14625725
title: Detrended TEC data in 2022 (March to April)

description: This dataset is the remaining total electron contents subtracted from the contents smoothed with a 15-minute window. We used the dataset for Kogure et al. (2025) to investigate concentric traveling ionospheric disturbances. This link has the remaining total electron contents in March and April.

This data repository can be cited as follows.

Kogure, M. & Otsuka, Y. (2025). Detrended TEC data in 2022 (March and April), [Dataset], https://zenodo.org/records/14625725, Zenodo, V1.
DOI: 10.5281/zenodo.14625733
title: Deterened TEC data in 2022 (May to June)

description: This dataset is the remaining total electron contents subtracted from the contents smoothed with a 15-minute window. We used the dataset for Kogure et al. (2025) to investigate concentric traveling ionospheric disturbances. This link has the remaining total electron contents in May and June.

This data repository can be cited as follows.

Kogure, M. & Otsuka, (2025), Detrended TEC data in 2022 (May and June), [Dataset], https://zenodo.org/records/14625733, Zenodo, V1.
DOI: 10.5281/zenodo.14625756
title: Detrended TEC data in 2022 (September to October)

description: This dataset is the remaining total electron contents subtracted from the contents smoothed with a 15-minute window. We used the dataset for Kogure et al. (2025) to investigate concentric traveling ionospheric disturbances. This link has the remaining total electron contents in September and October.

This data repository can be cited as follows.

Kogure, M. & Otsuka, Y. (2025), Detrended TEC data in 2022 (September and October), [Dataset], https://zenodo.org/records/14625756, Zenodo, V1.
DOI: 10.5281/zenodo.14625777
title: Detrened TEC data in 2022 (November to December)

description: This dataset is the remaining total electron contents subtracted from the contents smoothed with a 15-minute window. We used the dataset for Kogure et al. (2025) to investigate concentric traveling ionospheric disturbances. This link has the remaining total electron contents in November to December.

This data repository can be cited as follows.

Kogure, M. & Otsuka, (2025), Detrended TEC data in 2022 (November and December), [Dataset], https://zenodo.org/records/14625777, Zenodo, V1.
DOI: 10.5281/zenodo.14628434
title: Data for "Coherent control of a superconducting qubit using light"

description: Data for the manuscript "Coherent control of a superconducting qubit using light."
DOI: 10.5281/zenodo.14810679
title: Supplementary dataset for GRL paper Gallovič et al. (2025)

description: Supplementary dataset for paper Gallovič et al. (2025), Complex dynamic rupture of an Mw5.8 intermediate-depth earthquake in the Hellenic slab, Geophys. Res. Lett.:

Apparent source time functions (ASTFs) deconvolved from the mainshock recordings by the empirical Green's function method.

 

rastfs.dat: ASTFs for individual stations (columns) with time in the first column.

stations.txt: Station list with their coordinates and names. The order is the same as the column order in rastfs.dat.
DOI: 10.5281/zenodo.14849259
title: W-band spectra collected under low turbulence conditions

description: This dataset is used in the AMT manuscript titled:"Empirical Model for Backscattering Polarimetric Variables in Rain at W-band: Motivation and Implications"Myagkov et al., 2025.

The dataset was collected using a W-band radar in a low-turbulence environment at three different locations. The selection of the spectra was conducted according to Chapters 3.1-3.3 of the AMT manuscript.

Data for the three locations (1, 2, and 3; see the publication for details) are saved in the following *.mat files:



'Germany.mat' (47°48’03"N, 11°00’40"E; July 2021 – October 2021; 2,127 spectra)

'China.mat' (31°14’N, 121°28’E; June 2022 – December 2022; 3,407 spectra)

'Italy.mat' (44°40’55"N, 8°37’29"E; May 2023 – May 2024; 9,021 spectra)


The files can be opened with matlab (tested with version R2022a).

Each file contains the following variables:



s.spH and s.spV: Spectra of radar reflectivity for horizontal and vertical channels, respectively [dBZ]

s.spR: Real part of covariance spectra [mm^6 m^-3]

s.spI: Imaginary part of covariance spectra [mm^6 m^-3]

s.nH and s.nV: Spectral noise power in horizontal and vertical channels, respectively [mm^6 m^-3]

s.Nyq: Nyquist velocity [m s^-1]

s.dR: Range resolution [m]

s.spZdr: Spectral differential reflectivity [dB]

s.spDelta: Spectral differential phase [°]

s.spRhohv: Spectral correlation coefficient

s.range: Range [m]

v: Doppler velocity [m s^-1], roughly corrected for air motions and adjusted for the elevation angle


Radar measurements were performed at an elevation angle of 30°.
DOI: 10.5281/zenodo.14932882
title: Dataset of airborne measurements of aerosol, cloud droplets and meteorology by tethered balloon during PaCE 2022

description: This dataset was generated by the Finnish Meteorological Institute's payload on tethered balloons during the Pallas Cloud Experiment (PaCE2022), which took place from September 19th to October 10th, 2022. It contains measurements of key meteorological variables, as well as aerosol and cloud properties. The dataset is available in both NetCDF and CSV formats.

A link to a paper describing the data structure and showing data examples will be provided after acceptance.
DOI: 10.5281/zenodo.15066461
title: Data for "Understanding the two-step nucleation of iron at Earth's inner core conditions: a comparative molecular dynamics study"

description: The source data for the figures in the manuscript "Understanding the two-step nucleation of iron at Earth’s inner core conditions: a comparative molecular dynamics study". The .xlsx files can be read via Microsoft Excel.
DOI: 10.5281/zenodo.15111584
title: Variant Risk Estimate Probabilities for IEI genes
DOI: 10.5281/zenodo.3371216
title: BPNet manuscript data 2

description: Training data used in the BPNet manuscript.
DOI: 10.5281/zenodo.3988487
title: Supporting Information for "How long is too long? Variogram analysis of AERONET data to aid aerosol validation and intercomparison studies"

description: This contains data sets and their description for the paper "How long is too long? Variogram analysis of AERONET data to aid aerosol validation and intercomparison studies" submitted to the journal Earth and Space Science by this author. This study investigates the uncertainty introduced into comparisons (between e.g. AERONET ground observations, satellite retrievals, or model simulations) of aerosol optical properties due to differences in time of observation between the data sets. This uncertainty source has historically been neglected but is potentially important in some situations. Journal policies are that Supporting Information data sets can no longer be archived with the paper, but instead should be uploaded to a separate repository. As a result, I have uploaded these data here. This data set consists of one pdf file describing the data sets, and then two zip archives containing the data (ASCII format). The total size is approximately 10 MB. These data files allow the reproduction of analysis results in this paper, and enable researchers to estimate temporal mismatch uncertainties in their own analyses involving AERONET sites.
DOI: 10.5281/zenodo.4294904
title: BPNet manuscript data 1

description: This repository holds the trained BPNet models and all the output files produced using the models including TF-MoDISco files and motif instances. <strong>Files:</strong> <strong>output.tar.gz</strong> Full output directory containing trained BPNet models, contribution scores, TF-MoDISco results, motif instances, etc. See https://github.com/kundajelab/bpnet-manuscript for more information (archived at https://zenodo.org/record/4294814 with DOI: https://doi.org/10.5281/zenodo.4294813). <strong>bpnet.model.h5</strong> BPNet Keras model. This model can be directly used via the model repository Kipoi: http://kipoi.org/models/BPNet-OSKN . <strong>regions.bed</strong> ChIP-nexus peak regions (resized to 1 kb width) where BPNet was trained and interpreted on. Third column denotes the TF for which the peak was called. <strong>motif-instances.bed</strong> BPNet motif instances for 11 representative motifs Stored as BED file with the following columns Chromosome Start End Motif name 'match' score - similarity to the CWM computed using the continuous Jaccard distance metric between the CWM and L1 normalized contribution scores Strand 'contrib' score - computed as the L1 norm of the contribution scores at the motif instance position The log odds score with respect to the PWM derived from the PFM (i.e. classical PWM score) <strong>&lt;TF&gt;.preds.&lt;strand&gt;.bw</strong> Model predictions in `regions.bed` for a particular TF in {Oct4, Sox2, Nanog, Klf4} and strand in {pos, neg}. <strong>&lt;TF&gt;.importance.counts.bw</strong> Total count contribution scores for a particular TF <strong>&lt;TF&gt;.importance.profile.bw</strong> Profile contribution scores. These were used to run TF-MoDISco and downstream analysis. <strong>PWM,CWM,ChIP-nexus-profiles.tar.gz</strong> Directory containing PFM, CWM and aggregated ChIP-nexus footprint for 11 representative motifs. <strong>Figure-5d-periodicity.csv</strong> Raw data corresponding to Figure 5d where the 10bp periodicity of contribution scores was computed for each motif. <strong>dfabf.Oct4-Sox2-subset.parq</strong> Parquet file (read with `pd.read_parquet`) of genomics motif instance pairs and along with the BPNet-predicted interaction score. See 08-motif-interactions.genomic.ipynb for more information on how to generate plots for Figure 5 from this data.