cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e191903v1
title: AP Calculus and Science Coursetaking: Their Relationships with Choosing a STEM Major and Expecting to Be in a STEM Occupation

description: Some researchers have questioned whether there is a causal connection between Advanced Placement (AP) STEM coursetaking and the choice of a STEM college major and a STEM occupation. Their research findings strongly suggest that if prior interest in STEM as well as other possible confounders are taken into account, the relationships of taking AP mathematics to expecting to be in a STEM major and/or a STEM occupation are small to non-existent. Results from the current study, which uses data from a national longitudinal sample, actual measures of who majors in STEM, and prior interest in STEM along with other possible confounders, indicate that taking AP mathematics and AP science courses is significantly related to majoring in STEM and/or expecting to be in a STEM occupation at age 30—the sole exception being the taking of AP statistics.
DOI: 10.6084/m9.figshare.22782212
title: Data and R script for Johnson et al., '<em>Into the danger zone: how the within-host distribution of parasites controls virulence</em>'

description: These files contain the data (field & laboratory), metadata, and R script from Johnson et al. "<em>Into the danger zone: how the within-host distribution of parasites controls virulence</em>"
DOI: 10.6084/m9.figshare.22782212.v3
title: Data and R script for Johnson et al., '<em>Into the danger zone: how the within-host distribution of parasites controls virulence</em>'

description: These files contain the data (field & laboratory), metadata, and R script from Johnson et al. "<em>Into the danger zone: how the within-host distribution of parasites controls virulence</em>"
DOI: 10.6084/m9.figshare.22782212.v2
title: Data for Johnson et al., '<em>Into the danger zone: how the within-host distribution of parasites controls virulence</em>'

description: These files contain the data (field & laboratory) and metadata from Johnson et al. "<em>Into the danger zone: how the within-host distribution of parasites controls virulence</em>"
DOI: 10.5517/ccdc.csd.cc28rcq4
title: CCDC 2107836: Experimental Crystal Structure Determination

description: Related Article: Qiqi He|2021|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: 10.25392/leicester.data.20391462
title: Monoamine Neuromodulation is a Bilaterian Innovation Data

description: Data used in the paper Goulty et al, 2022, Monoamine Neuromodulation is a Bilaterian Innovation. <br> Data used in phylogenomic analyses of monoamine pathway genes. Includes amino acid sequence data, BLAST search results, Broccoli orthogroup cluster information, multiple sequence alignments and species trees used for reconciliation. <br> <br>
DOI: 10.25392/leicester.data.20391462.v1
title: Monoamine Neuromodulation is a Bilaterian Innovation Data

description: Data used in the paper Goulty et al, 2022, Monoamine Neuromodulation is a Bilaterian Innovation. <br> Data used in phylogenomic analyses of monoamine pathway genes. Includes amino acid sequence data, BLAST search results, Broccoli orthogroup cluster information, multiple sequence alignments and species trees used for reconciliation. <br> <br>
DOI: 10.34740/kaggle/dsv/5755110
title: Mahanagar Yatayat Dataset
DOI: 10.5281/zenodo.7962916
title: EyeLink 1000 raw eye tracking data - Reading numbers is harder than reading words: An eye-tracking study

description: Reading Arabic numerals is a fundamentally different activity compared to word reading. This study aimed to investigate the eye movements of normal-reading adults when reading aloud short and long Arabic numerals (with or without a thousand separator) compared to matched-in-length words and pseudowords. This dataset contains the raw data of the article "Reading numbers is harder than reading words: An eye-tracking study" published in the journal <em>Acta Psychologica</em> (https://doi.org/10.1016/j.actpsy.2023.103942)
description: {"references": ["de Chambrier, A. F., Pedrotti, M., Ruggeri, P., Dewi, J., Atzemian, M., Thevenot, C., ... & Terrier, P. (2023). Reading numbers is harder than reading words: An eye-tracking study. Acta Psychologica, 237, 103942"]}
DOI: 10.5281/zenodo.7962917
title: EyeLink 1000 raw eye tracking data - Reading numbers is harder than reading words: An eye-tracking study

description: Reading Arabic numerals is a fundamentally different activity compared to word reading. This study aimed to investigate the eye movements of normal-reading adults when reading aloud short and long Arabic numerals (with or without a thousand separator) compared to matched-in-length words and pseudowords. This dataset contains the raw data of the article "Reading numbers is harder than reading words: An eye-tracking study" published in the journal <em>Acta Psychologica</em> (https://doi.org/10.1016/j.actpsy.2023.103942)
description: {"references": ["de Chambrier, A. F., Pedrotti, M., Ruggeri, P., Dewi, J., Atzemian, M., Thevenot, C., ... & Terrier, P. (2023). Reading numbers is harder than reading words: An eye-tracking study. Acta Psychologica, 237, 103942"]}
DOI: 10.6084/m9.figshare.20988010.v1
title: Plasma Wave and Particle Dynamics During Interchange Events in the Jovian Magnetosphere Using Juno Observations

description: Dataset used to produce figures for the paper and supporting information submitted to Geophysical Research Letters with the title "Plasma Wave and Particle Dynamics During Interchange Events in the Jovian Magnetosphere Using Juno Observations"
DOI: 10.18117/7eap-4x79
title: Automated Spatiotemporal Thermal Anomaly Detection (ASTAD) Algorithm Test Datasets

description: These datasets were created using the Automated Spatiotemporal Thermal Anomaly Detection (ASTAD) Algorithm that performs image preprocessing and statstical anyalyis to indenfity subtle thermal anomalies in L1T ASTER scenes. The code can be found at: https://github.com/jthompson2710/ASTAD. The following manuscript in Remote Sensing of the Environment describes the algorithm and its application in detail: "this will be added when published". This work was supported by NASA grants 80NSSC18K1001 and 80NSSC20K1336.
DOI: 10.6092/bz0y-na72
title: Arctic Ocean - Contaminants aggregated datasets 1974/2017 v2021

description: EMODnet Chemistry aims to provide access to marine chemistry data sets and derived data products concerning eutrophication, ocean acidification and contaminants. The chemicals chosen reflect importance to the Marine Strategy Framework Directive (MSFD). This regional aggregated dataset contains all unrestricted EMODnet Chemistry data on contaminants; temperature, salinity and additional sampling parameters are included when available. The spatial coverage is the Arctic Ocean with 780 CDI records divided per matrices: 249 water profiles and 531 sediment profiles. For water, vertical profiles temporal range is from 1974-03-25 to 2011-08-24.For sediment, vertical profiles temporal range is from 1974-08-07 to 2017-10-26. Data were aggregated and quality controlled by ‘Institute of Marine Research - Norwegian Marine Data Centre (NMD)’ from Norway. <br> Regional datasets concerning contaminants are automatically harvested. Parameter names in these datasets are based on P01, BODC Parameter Usage Vocabulary, which is available at: http://seadatanet.maris2.nl/bandit/browse_step.php . Each measurement value has a quality flag indicator. The resulting data collections for each Sea Basin are harmonised, and the collections are quality controlled by EMODnet Chemistry Regional Leaders using ODV Software and following a common methodology for all Sea Regions. <br> Harmonisation means that: (1) unit conversion is carried out to express contaminant concentrations with a limited set of measurement units (according to EU directives 2013/39/UE; Comm. Dec. EU 2017/848) and (2) merging of variables described by different “local names” ,but corresponding exactly to the same concepts in BODC P01 vocabulary.<br> Detailed documentation is available at: https://doi.org/10.6092/8b52e8d7-dc92-4305-9337-7634a5cae3f4<br> The harmonised dataset can be downloaded as ODV spreadsheet (TXT file), which is composed of metadata header followed by tab separated values. This worksheet can be imported to ODV Software for visualisation (More information can be found at: https://www.seadatanet.org/Software/ODV ).<br> The same dataset is offered also as TXT file in a long/vertical format, in which each P01 measurement is a record line. Additionally, there are a series of columns that split P01 terms in subcomponents (measure, substance, CAS number, matrix...).This transposed format is more adapted to worksheet applications users (e.g. LibreOffice Calc).<br> The original datasets can be searched and downloaded from EMODnet Chemistry Download Service: https://emodnet-chemistry.maris.nl/search
DOI: 10.6092/28a025c3-1f79-4f6d-b71a-86641e2b7346
title: Arctic region - DIVA 4D 6-year analysis of Water body chlorophyll-a 1980/2017 v2018

description: The "Arctic region - DIVA 4D 6-year analysis of Water body chlorophyll-a 1980/2017 v2018" is the 4D gridded analysis for water body chlorophyll-a in the Arctic region. The analysis was performed per season using DIVA software tool (Data-Interpolating Variational Analysis). The analyses products are stored as NetCDF CF files and made available as WMS layers for easy browsing and adding. Dynamic plots obtained as a combination of WPS/WMS (Density information available by colours, time series and vertical profiles) were uploaded to 'EMODnet Chemistry Viewing and Downloading Service'."" Every step of the time dimension corresponds to a 6-year average moving from 1980 to 2017. The depth dimension spans from surface to 300m with 14 vertical levels."" The data used as input for DIVA have been extracted from the EMODnet Chemistry Download Service: http://emodnet-chemistry.maris2.nl/v_cdi_v3/search.asp
DOI: 10.6092/c887f9f2-d6aa-4e73-8254-6a0b5963c08d
title: Arctic Ocean - DIVA 4D 6-year analysis of Water body dissolved oxygen concentration 1965/2017 v2021

description: Moving 6-year analysis of Water body dissolved oxygen concentration in the Arctic Ocean, for each season in the period 1965-2017. Every year of the time dimension corresponds to the 6-year centered average for each season. Winter: December-February, Spring: March-May, Summer: June-August, Autumn: September-November. Depth range (IODE standard depths): 0, 5, 10, 20, 30, 40, 50, 75, 100, 125, 150, 200, 250, 300, 400, ..., 1500, 1750, 2000, 2500m. Units: umol/l. Description of DIVA analysis: The computation was done with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.6.6, using GEBCO 30sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps grids is 0.1 degrees. Signal-to-noise ratio was fixed to 2.0, horizontal correlation length to 100 km, and vertical correlation length varying between 25 and 200 m. Logarithmic transformation is applied to the data prior to the analysis. Background field: the data mean value is subtracted from the data.
DOI: 10.6092/d0618e05-bc9b-4212-9aa3-12eb2a6b8756
title: Arctic Ocean - DIVA 4D 6-year analysis of Water body phosphate 1965/2017 v2021

description: Moving 6-year analysis of Water body phosphate in the Arctic Ocean, for each season in the period 1965-2017. Every year of the time dimension corresponds to the 6-year centered average for each season. Winter: December-February, Spring: March-May, Summer: June-August, Autumn: September-November. Depth range (IODE standard depths): 0, 5, 10, 20, 30, 40, 50, 75, 100, 125, 150, 200, 250, 300, 400, ..., 1500, 1750, 2000, 2500m. Units: umol/l. Description of DIVA analysis: The computation was done with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.6.6, using GEBCO 30sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps grids is 0.1 degrees. Signal-to-noise ratio was fixed to 2.0, horizontal correlation length to 100 km, and vertical correlation length varying between 25 and 200 m. Logarithmic transformation is applied to the data prior to the analysis. Background field: the data mean value is subtracted from the data.
DOI: 10.6092/3ebe4a8b-9ecb-4e00-b617-47576d49dc29
title: Arctic Ocean - DIVA 4D 6-year analysis of Water body silicate 1965/2017 v2021

description: Moving 6-year analysis of Water body silicate in the Arctic Ocean, for each season in the period 1965-2017. Every year of the time dimension corresponds to the 6-year centered average for each season. Winter: December-February, Spring: March-May, Summer: June-August, Autumn: September-November. Depth range (IODE standard depths): 0, 5, 10, 20, 30, 40, 50, 75, 100, 125, 150, 200, 250, 300, 400, ..., 1500, 1750, 2000, 2500m. Units: umol/l. Description of DIVA analysis: The computation was done with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.6.6, using GEBCO 30sec topography for the spatial connectivity of water masses. The horizontal resolution of the produced DIVAnd maps grids is 0.1 degrees. Signal-to-noise ratio was fixed to 2.0, horizontal correlation length to 100 km, and vertical correlation length varying between 25 and 200 m. Logarithmic transformation is applied to the data prior to the analysis. Background field: the data mean value is subtracted from the data.
DOI: 10.5281/zenodo.7956546
title: Empirical Models output - RECCAP2 Program

description: This data set contains the Empirical models' output data provided under RECCAP2 Program
DOI: 10.5281/zenodo.7956545
title: Empirical Models output - RECCAP2 Program

description: This data set contains the Empirical models' output data provided under RECCAP2 Program
DOI: 10.6092/xe56-z804
title: Arctic Ocean - Eutrophication and Acidity aggregated datasets 1925/2020 v2021

description: EMODnet Chemistry aims to provide access to marine chemistry data sets and derived data products concerning eutrophication, acidity and contaminants. The chemicals chosen reflect importance to the Marine Strategy Framework Directive (MSFD). This aggregated dataset contains all unrestricted EMODnet Chemistry data on Eutrophication and Acidity (14 parameters with quality flag indicators), and covers the Norwegian Sea, Barents Sea, Greenland Sea and Icelandic Waters with 114721 CDI stations. Data were aggregated and quality controlled by 'Institute of Marine Research - Norwegian Marine Data Centre (NMD)'.<br> <br> Regional datasets concerning eutrophication and acidity are automatically harvested and resulting collections are aggregated and quality controlled using ODV Software and following a common methodology for all Sea Regions ( https://doi.org/10.6092/9f75ad8a-ca32-4a72-bf69-167119b2cc12 ). When not present in original data, Water body nitrate plus nitrite was calculated by summing up the Nitrates and Nitrites. Same procedure was applied for Water body dissolved inorganic nitrogen (DIN) which was calculated by summing up the Nitrates, Nitrites and Ammonium.<br> <br> Parameter names are based on P35, EMODnet Chemistry aggregated parameter names vocabulary, which is available at: https://www.bodc.ac.uk/resources/vocabularies/vocabulary_search/P35/<br> <br> Detailed documentation is available at: https://dx.doi.org/10.6092/4e85717a-a2c9-454d-ba0d-30b89f742713<br> <br> The aggregated dataset can be downloaded as ODV collection and spreadsheet, which is composed of metadata header followed by tab separated values. This spreadsheet can be imported to ODV Software for visualisation (More information can be found at: https://www.seadatanet.org/Software/ODV )<br> <br> The original datasets can be searched and downloaded from EMODnet Chemistry Download Service: https://emodnet-chemistry.maris.nl/search
DOI: 10.6092/ea84d664-07d1-4f1c-b5d2-0ac7a7fad1e0
title: Arctic region - Eutrophication and Acidity aggregated datasets 1925/2017 v2018

description: EMODnet Chemistry aims to provide access to marine chemistry data sets and derived data products concerning eutrophication, ocean acidification and contaminants. The chemicals chosen reflect importance to the Marine Strategy Framework Directive (MSFD). ITS-90 water temperature and Water body salinity variables have been also included (as-is) to complete the Eutrophication and Acidity data. If you use these variables for calculations, please refer to SeaDataNet for having the quality flags: https://www.seadatanet.org/Products/Aggregated-datasets. This aggregated dataset contains all unrestricted EMODnet Chemistry data on Eutrophication and Ocean acidification (14 parameters with quality flag indicators), and covers the Norwegian Sea, Barents Sea, Greenland Sea and Icelandic Waters with 43000 CDI records. Data were aggregated and quality controlled by "Institute of Marine Research - Norwegian Marine Data Centre (NMD)" from Norway.
description: Regional datasets concerning eutrophication and ocean acidification are automatically harvested and resulting collections are aggregated and quality controlled using ODV Software and following a common methodology for all Sea Regions (https://doi.org/10.6092/9f75ad8a-ca32-4a72-bf69-167119b2cc12). When not present in original data, Water body nitrate plus nitrite was calculated by summing up the Nitrates and Nitrites. Same procedure was applied for Water body dissolved inorganic nitrogen (DIN) which was calculated by summing up the Nitrates, Nitrites and Ammonium."" Parameter names are based on P35, EMODnet Chemistry aggregated parameter names vocabulary, which is available at: https://www.bodc.ac.uk/resources/vocabularies/vocabulary_search/P35/."" Detailed documentation is available at: https://doi.org/10.6092/ec8207ef-ed81-4ee5-bf48-e26ff16bf02e"" The aggregated dataset can be downloaded as ODV spreadsheet, which is composed of metadata header followed by tab separated values. This worksheet can be imported to ODV Software for visualisation (More information can be found at: https://www.seadatanet.org/Software/ODV)."" The original datasets can be searched and downloaded from EMODnet Chemistry Download Service: http://emodnet-chemistry.maris2.nl/v_cdi_v3/search.asp
DOI: 10.6092/q0er-ss61
title: Baltic Sea - Contaminants aggregated datasets 1972/2017 v2018

description: EMODnet Chemistry aims to provide access to marine chemistry data sets and derived data products concerning eutrophication, ocean acidification and contaminants. The chemicals chosen reflect importance to the Marine Strategy Framework Directive (MSFD). This regional aggregated dataset contains all unrestricted EMODnet Chemistry data on contaminants (49 parameters), and covers the Baltic Sea with 3818 CDI records divided per matrices: 1358 biota (396 Vertical profiles and 962 Time series),906 water profiles, 1554 sediment profiles. Vertical profiles temporal range is from 1974-04-25 to 2016-09-27. Time series temporal range is from 1972-05-02 to 2017-10-30. Data were aggregated and quality controlled by ‘Swedish Meteorological and Hydrological Institute (SMHI)’ from Sweden.
description: Regional datasets concerning contaminants are automatically harvested. Parameter names in these datasets are based on P01, BODC Parameter Usage Vocabulary, which is available at: http://seadatanet.maris2.nl/bandit/browse_step.php . Each measurement value has a quality flag indicator. The resulting data collections for each Sea Basin are harmonised, and the collections are quality controlled by EMODnet Chemistry Regional Leaders using ODV Software and following a common methodology for all Sea Regions."" Harmonisation means that: (1) unit conversion is carried out to express contaminant concentrations with a limited set of measurement units (according to EU directives 2013/39/UE; Comm. Dec. EU 2017/848) and (2) merging of variables described by different “local names” ,but corresponding exactly to the same concepts in BODC P01 vocabulary.""The harmonised dataset can be downloaded as ODV spreadsheet (TXT file), which is composed of metadata header followed by tab separated values. This worksheet can be imported to ODV Software for visualisation (More information can be found at: https://www.seadatanet.org/Software/ODV ).""The same dataset is offered also as XLSX file in a long/vertical format, in which each P01 measurement is a record line. Additionally, there are a series of columns that split P01 terms in subcomponents (measure, substance, CAS number, matrix...).This transposed format is more adapted to worksheet applications users (e.g. LibreOffice Calc).The 49 parameter names in this metadata record are based on P02, SeaDataNet Parameter Discovery Vocabulary, which is available at: http://seadatanet.maris2.nl/v_bodc_vocab_v2/vocab_relations.asp?lib=P02 .""Detailed documentation will be published soon.""The original datasets can be searched and downloaded from EMODnet Chemistry Download Service: http://emodnet-chemistry.maris2.nl/v_cdi_v3/search.asp
DOI: 10.34740/kaggle/dsv/5755184
title: Nigerian Presidential Election 2023 Tweets
DOI: 10.6092/28y3-th08
title: Baltic Sea - Contaminants aggregated datasets 1974/2018 v2021

description: EMODnet Chemistry aims to provide access to marine chemistry data sets and derived data products concerning eutrophication, ocean acidification and contaminants. The chemicals chosen reflect importance to the Marine Strategy Framework Directive (MSFD). This regional aggregated dataset contains all unrestricted EMODnet Chemistry data on contaminants; temperature, salinity and additional sampling parameters are included when available. The spatial coverage is the Baltic Sea with 3186 CDI records divided per matrices: 808 water profiles, 1556 sediment profiles and 822 biota profiles. For water, vertical profiles temporal range is from 1999-05-15 to 2018-08-27. For sediment, vertical profiles temporal range is from 1974-04-25 to 2018-08-27. For biota, vertical profiles temporal range is from 1998-09-14 to 2018-10-01. Data were aggregated and quality controlled by ‘Swedish Meteorological and Hydrological Institute (SMHI)’ from Sweden. <br> Regional datasets concerning contaminants are automatically harvested. Parameter names in these datasets are based on P01, BODC Parameter Usage Vocabulary, which is available at: http://seadatanet.maris2.nl/bandit/browse_step.php . Each measurement value has a quality flag indicator. The resulting data collections for each Sea Basin are harmonised, and the collections are quality controlled by EMODnet Chemistry Regional Leaders using ODV Software and following a common methodology for all Sea Regions. <br> Harmonisation means that: (1) unit conversion is carried out to express contaminant concentrations with a limited set of measurement units (according to EU directives 2013/39/UE; Comm. Dec. EU 2017/848) and (2) merging of variables described by different “local names” ,but corresponding exactly to the same concepts in BODC P01 vocabulary.<br> Detailed documentation is available at: https://doi.org/10.6092/8b52e8d7-dc92-4305-9337-7634a5cae3f4<br> The harmonised dataset can be downloaded as ODV spreadsheet (TXT file), which is composed of metadata header followed by tab separated values. This worksheet can be imported to ODV Software for visualisation (More information can be found at: https://www.seadatanet.org/Software/ODV ).<br> The same dataset is offered also as XLSX file in a long/vertical format, in which each P01 measurement is a record line. Additionally, there are a series of columns that split P01 terms in subcomponents (measure, substance, CAS number, matrix...).This transposed format is more adapted to worksheet applications users (e.g. LibreOffice Calc).<br> The original datasets can be searched and downloaded from EMODnet Chemistry Download Service: https://emodnet-chemistry.maris.nl/search