cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8010139
title: forpus_passerinus_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8010178
title: Data for Cryogenic InGaAs HEMT-Based Switches For Quantum Signal Routing

description: Data for "Data for Cryogenic InGaAs HEMT-Based Switches For Quantum Signal Routing".
DOI: 10.5281/zenodo.8010177
title: Data for Cryogenic InGaAs HEMT-Based Switches For Quantum Signal Routing

description: Data for "Data for Cryogenic InGaAs HEMT-Based Switches For Quantum Signal Routing".
DOI: 10.5281/zenodo.8010172
title: amphibolips_quercusostensackenii_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8010171
title: amphibolips_quercusostensackenii_zanfona_1.fasta.gz

description: .
DOI: 10.5281/zenodo.8006958
title: Large Datasets for Observation Paper

description: This repository contains the large ERA5 data files.
DOI: 10.5281/zenodo.8006957
title: Large Datasets for Observation Paper

description: This repository contains the large ERA5 data files.
DOI: 10.5281/zenodo.5571278
title: NDC factsheets

description: <strong>Nationally Determined Contribution (NDC) Factsheets</strong> Raw JSON data used for the NDC Factsheets available at https://www.climate-resource.com/tools/ndcs
DOI: 10.5281/zenodo.5707584
title: NDC factsheets

description: <strong>Nationally Determined Contribution (NDC) Factsheets</strong> Raw JSON data used for the NDC Factsheets available at https://www.climate-resource.com/tools/ndcs
DOI: 10.5281/zenodo.5709225
title: NDC factsheets

description: <strong>Nationally Determined Contribution (NDC) Factsheets</strong> Raw JSON data used for the NDC Factsheets available at https://www.climate-resource.com/tools/ndcs. The previous versions of the NDC Factsheets are available at https://www.climatecollege.unimelb.edu.au/ndc-indc-factsheets and https://www.pik-potsdam.de/paris-reality-check/indc-factsheets/. <strong>Files included in the dataset</strong> For each country/region there are 6 files, each run with different settings to explore the different possible types of commitments. The format of the filename is as follows: {country/region code}_ndc_{date}_{conditionality}_{hotAir}.json Where country/region code is the 3 letter ISO-3166-1 code for the country or a shortcode for a region, conditionality is one of the following: conditional range (the high/low ambitions represent min-max range of commitments) unconditional For some countries, their pledged targets are greater than what a "no additional climate policy" reference scenario would suggest. The "hotAir" component of the filename state whether these hot air pledges are included or excluded (the reference scenario for a country is used instead). In addition to the archive of region/country level data, some additional global timeseries are provided in the "total_*.json" files. These files follow the same file template shown above.
DOI: 10.5281/zenodo.5709420
title: NDC factsheets

description: <strong>Nationally Determined Contribution (NDC) Factsheets</strong> Raw JSON data used for the NDC Factsheets available at https://www.climate-resource.com/tools/ndcs. The previous versions of the NDC Factsheets are available at https://www.climatecollege.unimelb.edu.au/ndc-indc-factsheets and https://www.pik-potsdam.de/paris-reality-check/indc-factsheets/. <strong>Files included in the dataset</strong> For each country/region there are 6 files, each run with different settings to explore the different possible types of commitments. The format of the filename is as follows: {country/region code}_ndc_{date}_{conditionality}_{hotAir}.json Where country/region code is the 3 letter ISO-3166-1 code for the country or a shortcode for a region, conditionality is one of the following: conditional range (the high/low ambitions represent min-max range of commitments) unconditional For some countries, their pledged targets are greater than what a "no additional climate policy" reference scenario would suggest. The "hotAir" component of the filename state whether these hot air pledges are included or excluded (the reference scenario for a country is used instead). In addition to the archive of region/country level data, some additional global timeseries are provided in the "total_*.json" files. These files follow the same file template shown above.
DOI: 10.5281/zenodo.5709456
title: NDC factsheets

description: <strong>Nationally Determined Contribution (NDC) Factsheets</strong> Raw JSON data used for the NDC Factsheets available at https://www.climate-resource.com/tools/ndcs. The previous versions of the NDC Factsheets are available at https://www.climatecollege.unimelb.edu.au/ndc-indc-factsheets and https://www.pik-potsdam.de/paris-reality-check/indc-factsheets/. <strong>Files included in the dataset</strong> For each country/region there are 6 files, each run with different settings to explore the different possible types of commitments. The format of the filename is as follows: {country/region code}_ndc_{date}_{conditionality}_{hotAir}.json Where country/region code is the 3 letter ISO-3166-1 code for the country or a shortcode for a region, conditionality is one of the following: conditional range (the high/low ambitions represent min-max range of commitments) unconditional For some countries, their pledged targets are greater than what a "no additional climate policy" reference scenario would suggest. The "hotAir" component of the filename state whether these hot air pledges are included or excluded (the reference scenario for a country is used instead). In addition to the archive of region/country level data, some additional global timeseries are provided in the "total_*.json" files. These files follow the same file template shown above.
DOI: 10.5281/zenodo.5710394
title: NDC factsheets

description: <strong>Nationally Determined Contribution (NDC) Factsheets</strong> Raw JSON data used for the NDC Factsheets available at https://www.climate-resource.com/tools/ndcs. The previous versions of the NDC Factsheets are available at https://www.climatecollege.unimelb.edu.au/ndc-indc-factsheets and https://www.pik-potsdam.de/paris-reality-check/indc-factsheets/. <strong>Files included in the dataset</strong> For each country/region there are 6 files, each run with different settings to explore the different possible types of commitments. The format of the filename is as follows: {country/region code}_ndc_{date}_{conditionality}_{hotAir}.json Where country/region code is the 3 letter ISO-3166-1 code for the country or a shortcode for a region, conditionality is one of the following: conditional range (the high/low ambitions represent min-max range of commitments) unconditional For some countries, their pledged targets are greater than what a "no additional climate policy" reference scenario would suggest. The "hotAir" component of the filename state whether these hot air pledges are included or excluded (the reference scenario for a country is used instead). In addition to the archive of region/country level data, some additional global timeseries are provided in the "total_*.json" files. These files follow the same file template shown above.
DOI: 10.5281/zenodo.5768669
title: NDC factsheets

description: <strong>Nationally Determined Contribution (NDC) Factsheets</strong> Raw JSON data used for the NDC Factsheets available at https://www.climate-resource.com/tools/ndcs. The previous versions of the NDC Factsheets are available at https://www.climatecollege.unimelb.edu.au/ndc-indc-factsheets and https://www.pik-potsdam.de/paris-reality-check/indc-factsheets/. <strong>Files included in the dataset</strong> For each country/region there are 6 files, each run with different settings to explore the different possible types of commitments. The format of the filename is as follows: {country/region code}_ndc_{date}_{conditionality}_{hotAir}.json Where country/region code is the 3 letter ISO-3166-1 code for the country or a shortcode for a region, conditionality is one of the following: conditional range (the high/low ambitions represent min-max range of commitments) unconditional For some countries, their pledged targets are greater than what a "no additional climate policy" reference scenario would suggest. The "hotAir" component of the filename state whether these hot air pledges are included or excluded (the reference scenario for a country is used instead). In addition to the archive of region/country level data, some additional global timeseries are provided in the "total_*.json" files. These files follow the same file template shown above.
DOI: 10.5281/zenodo.6383612
title: NDC factsheets

description: <strong>Nationally Determined Contribution (NDC) Factsheets</strong> Raw JSON data used for the NDC Factsheets available at https://www.climate-resource.com/tools/ndcs. The previous versions of the NDC Factsheets are available at https://www.climatecollege.unimelb.edu.au/ndc-indc-factsheets and https://www.pik-potsdam.de/paris-reality-check/indc-factsheets/. <strong>Files included in the dataset</strong> For each country/region there are 6 files, each run with different settings to explore the different possible types of commitments. The format of the filename is as follows: {country/region code}_ndc_{date}_{conditionality}_{hotAir}.json Where country/region code is the 3 letter ISO-3166-1 code for the country or a shortcode for a region, conditionality is one of the following: conditional range (the high/low ambitions represent min-max range of commitments) unconditional For some countries, their pledged targets are greater than what a "no additional climate policy" reference scenario would suggest. The "hotAir" component of the filename state whether these hot air pledges are included or excluded (the reference scenario for a country is used instead). In addition to the archive of region/country level data, some additional global timeseries are provided in the "total_*.json" files. These files follow the same file template shown above. The cutoff date for NDC submissions for version 14Feb2022b is 31st December 2022.
DOI: 10.5281/zenodo.7308982
title: NDC factsheets

description: <strong>Nationally Determined Contribution (NDC) Factsheets</strong> Raw JSON data used for the NDC Factsheets available at https://www.climate-resource.com/tools/ndcs. The previous versions of the NDC Factsheets are available at https://www.climatecollege.unimelb.edu.au/ndc-indc-factsheets and https://www.pik-potsdam.de/paris-reality-check/indc-factsheets/. <strong>Files included in the dataset</strong> For each country/region there are 6 files, each run with different settings to explore the different possible types of commitments. The format of the filename is as follows: {country/region code}_ndc_{date}_{conditionality}_{hotAir}.json Where country/region code is the 3 letter ISO-3166-1 code for the country or a shortcode for a region, conditionality is one of the following: conditional range (the high/low ambitions represent min-max range of commitments) unconditional For some countries, their pledged targets are greater than what a "no additional climate policy" reference scenario would suggest. The "hotAir" component of the filename state whether these hot air pledges are included or excluded (the reference scenario for a country is used instead). In addition to the archive of region/country level data, some additional global timeseries are provided in the "total_*.json" files. These files follow the same file template shown above.
DOI: 10.5281/zenodo.8010081
title: NDC factsheets

description: <strong>Nationally Determined Contribution (NDC) Factsheets</strong> Raw JSON data used for the NDC Factsheets available at https://www.climate-resource.com/tools/ndcs. The previous versions of the NDC Factsheets are available at https://www.climatecollege.unimelb.edu.au/ndc-indc-factsheets and https://www.pik-potsdam.de/paris-reality-check/indc-factsheets/. <strong>Files included in the dataset</strong> For each country/region there are 6 files, each run with different settings to explore the different possible types of commitments. The format of the filename is as follows: {country/region code}_ndc_{date}_{conditionality}_{hotAir}.json Where country/region code is the 3 letter ISO-3166-1 code for the country or a shortcode for a region, conditionality is one of the following: conditional range (the high/low ambitions represent min-max range of commitments) unconditional For some countries, their pledged targets are greater than what a "no additional climate policy" reference scenario would suggest. The "hotAir" component of the filename state whether these hot air pledges are included or excluded (the reference scenario for a country is used instead). In addition to the archive of region/country level data, some additional global timeseries are provided in the "total_*.json" files. These files follow the same file template shown above.
DOI: 10.5281/zenodo.5571277
title: NDC factsheets

description: <strong>Nationally Determined Contribution (NDC) Factsheets</strong> Raw JSON data used for the NDC Factsheets available at https://www.climate-resource.com/tools/ndcs. The previous versions of the NDC Factsheets are available at https://www.climatecollege.unimelb.edu.au/ndc-indc-factsheets and https://www.pik-potsdam.de/paris-reality-check/indc-factsheets/. <strong>Files included in the dataset</strong> For each country/region there are 6 files, each run with different settings to explore the different possible types of commitments. The format of the filename is as follows: {country/region code}_ndc_{date}_{conditionality}_{hotAir}.json Where country/region code is the 3 letter ISO-3166-1 code for the country or a shortcode for a region, conditionality is one of the following: conditional range (the high/low ambitions represent min-max range of commitments) unconditional For some countries, their pledged targets are greater than what a "no additional climate policy" reference scenario would suggest. The "hotAir" component of the filename state whether these hot air pledges are included or excluded (the reference scenario for a country is used instead). In addition to the archive of region/country level data, some additional global timeseries are provided in the "total_*.json" files. These files follow the same file template shown above.
DOI: 10.34740/kaggle/dsv/5856265
title: 2021 World Population (updated daily)
DOI: 10.34740/kaggle/dsv/5856264
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5281/zenodo.5906809
title: Changes in the peatlands use during 20th century in north western Poland (the Tuchola Pinewoods). The density of peatlands in the kilometer grid.

description: A vector data set in ESRI Shapefile format (EPSG: 2180). The changes in peatland areas were analysed on the basis of 1:100,000 Prussian maps – Karte des Deutschen Reiches – from the years 1876-1879 (11 map sheets) and 1:50,000 Polish topographic maps showing the state of land cover in the years 1966-86 The analyses resulted in a map of the density of peatland distribution in the Tuchola Pinewoods in 1879. 744 peatlands were identified in the Tuchola Pinewoods in the late 19th century, totalling 10,762 ha, which constitutes 3.03% of the study area. The smallest marked peatland covered 0.12 ha, while the largest was 792.8 ha. There are 595 items of less than 15 ha, totalling 2,645 ha, while there are only 149 peatlands exceeding 15 ha, but they covered an area of 8,117 ha. Data used in the scientific publication: <em>Natural and anthropogenic factors influencing changes in peatland management in Poland,</em> Szumińska D., Czapiewski S., Sewerniak, P. 2023, Regional Environmental Change, Vol. 23, iss. 1, art. no 5, doi: 10.1007/s10113-022-02001-2
DOI: 10.5281/zenodo.5906808
title: Changes in the peatlands use during 20th century in north western Poland (the Tuchola Pinewoods). The density of peatlands in the kilometer grid.

description: A vector data set in ESRI Shapefile format (EPSG: 2180). The changes in peatland areas were analysed on the basis of 1:100,000 Prussian maps – Karte des Deutschen Reiches – from the years 1876-1879 (11 map sheets) and 1:50,000 Polish topographic maps showing the state of land cover in the years 1966-86 The analyses resulted in a map of the density of peatland distribution in the Tuchola Pinewoods in 1879. 744 peatlands were identified in the Tuchola Pinewoods in the late 19th century, totalling 10,762 ha, which constitutes 3.03% of the study area. The smallest marked peatland covered 0.12 ha, while the largest was 792.8 ha. There are 595 items of less than 15 ha, totalling 2,645 ha, while there are only 149 peatlands exceeding 15 ha, but they covered an area of 8,117 ha. Data used in the scientific publication: <em>Natural and anthropogenic factors influencing changes in peatland management in Poland,</em> Szumińska D., Czapiewski S., Sewerniak, P. 2023, Regional Environmental Change, Vol. 23, iss. 1, art. no 5, doi: 10.1007/s10113-022-02001-2
DOI: 10.6084/m9.figshare.23101466
title: Dataset peruvian athletes.csv

description: Data from a cross-sectional study with an analytical component to evaluate amateur athletes who practiced table tennis, basketball, and athletics and are part of the Peruvian Sports Institute of the district of Ica. Between October and November 2018, we evaluated 130 athletes selected for convenience, in continuous activity, and who practice sports such as tennis, basketball, and athletics.
DOI: 10.6084/m9.figshare.23101466.v1
title: Dataset peruvian athletes.csv

description: Data from a cross-sectional study with an analytical component to evaluate amateur athletes who practiced table tennis, basketball, and athletics and are part of the Peruvian Sports Institute of the district of Ica. Between October and November 2018, we evaluated 130 athletes selected for convenience, in continuous activity, and who practice sports such as tennis, basketball, and athletics.
DOI: 10.6084/m9.figshare.23000573.v1
title: vacancydb

description: https://arxiv.org/abs/2205.08366
DOI: 10.5066/p9ool3l4
title: Mangrove Peat Radiocarbon Ages From Snipe and Swan Key, FL

description: In 2016, researchers collected cores of mangrove peat from two islands in the Florida Keys: Snipe Key (24.679 degrees N, 81.653 degrees W) and Swan Key (25.349 degrees N, 80.251 degrees W) (Fig. 1). This data release contains the radiocarbon ages and associated data for peat samples analyzed throughout the two cores. These data were used by Khan and others (2022) to reconstruct the millennial-scale sea level variability of the two locations.
DOI: 10.5517/ccdc.csd.cc2f4hrv
title: CCDC 2238657: Experimental Crystal Structure Determination

description: Related Article: Emanuele Priola, Alessia Giordana, Eliano Diana, Saule Vicario|2023|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.8010188
title: BIKE Key-Recovery: Combining Power Consumption Analysis and Information-Set Decoding

description: Data used in the paper: "BIKE Key-Recovery: Combining Power Consumption Analysis and Information-Set Decoding". The paper has been accepted at ACNS-2023. The available dataset contains a file with a few power consumption curves taken from a Cortex-M4 (STM32F4) on a CW308 board. The file is a numpy array stored using the np.save API.<br> The file can be directly used for running the notebooks provided in the publication github.
description: We will potentially update the database with more curves or curves from different targets.
DOI: 10.5281/zenodo.8010187
title: BIKE Key-Recovery: Combining Power Consumption Analysis and Information-Set Decoding

description: Data used in the paper: "BIKE Key-Recovery: Combining Power Consumption Analysis and Information-Set Decoding". The paper has been accepted at ACNS-2023. The available dataset contains a file with a few power consumption curves taken from a Cortex-M4 (STM32F4) on a CW308 board. The file is a numpy array stored using the np.save API.<br> The file can be directly used for running the notebooks provided in the publication github.
description: We will potentially update the database with more curves or curves from different targets.
DOI: 10.6084/m9.figshare.23301605.v1
title: Table S1

description: Mean values, standard deviation (SD) and sample sizes of individual observations of reproductive traits. Studies involved in the meta-analysis are highlighted in bold.
DOI: 10.6084/m9.figshare.23301605
title: Table S1

description: Mean values, standard deviation (SD) and sample sizes of individual observations of reproductive traits. Studies involved in the meta-analysis are highlighted in bold.
DOI: 10.6084/m9.figshare.23294765
title: Size-Extensive Molecular Machine Learning with Global Representations.

description: Original data of Jung H, Stocker S, Kunkel C, Oberhofer H, Han B, Reuter K, Margraf JT. Size‐Extensive Molecular Machine Learning with Global Representations. ChemSystemsChem. 2020 Jul;2(4):e1900052. doi.org/10.1002/syst.201900052<br> <br> "qm9_light_molecules.xyz" has been used for 4-fold cross validation within small molecules (Table 1) "qm9_heavy_9_2000ea.xyz" has been used for prediction of rather larger molecules (Figure 3) "OE_qm9_same_elements_32467.xyz" has been used for prediciton of large molecules is OE62 dataset. (Figure 4) <br> second entry of comment line in xyz file indicates atomization energy of molecule in kcal/mol
DOI: 10.25345/c5sb3x83f
title: MassIVE MSV000092110 - Constitutively active autophagy dampens inflammation through metabolic and post-transcriptional regulation of macrophage cytokine production
title: Xu J, Kong L, Oliver B, Li B, Creasey EA, Guzman G, Schenone M, Carey KL, Carr SA, Graham DB, Deguine J, Xavier RJ. 2023. Autophagy is an essential cellular process that is deeply integrated with innate immune signaling; however, studies that examine autophagy in the context of inflammatory conditions are lacking. Here, using mouse models with a constitutively active variant of the autophagy gene Beclin1, we show that increased autophagy dampens cytokine production in models of inflammation and adherent-invasive Escherichia coli (AIEC) infection. We further analyzed primary macrophages from these animals with a combination of transcriptomics and proteomics to identify novel mechanistic targets downstream of autophagy. Our study reveals glutamine/glutathione metabolism and the RNF128/TBK1 axis as independent regulators of inflammation. Altogether, our work highlights increased autophagic flux as a potential approach to reduce inflammation and defines independent mechanistic cascades involved in this control.
DOI: 10.5522/04/23277284
title: Chatbot as Advisers dataset

description: A dataset from an online studies on a simulated social trading platform using a chatbot to give participants advice on investment. 64 participants interacted with a chatbot across 4 conditions: human-like/not human-like, and with reply suggestion buttons/without reply suggestion buttons embedded in the user interface. They were shown 10 different portfolios to follow or unfollow at 5 separate month intervals, basing their decision on the advice of the chatbot or a separate news feed that would try to predict the next change in portfolio value. Participants were assigned an initial virtual balance of £1000. Image tagging was included as a distracting secondary task. All the messages exchanged to and from the chatbot are included, as well as the user actions and image tagging. Participant demographic data included in a separate file.