cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4980131
title: Modeling cyanobacteria life cycle dynamics and historical nitrogen fixation in the Baltic Proper - Datasets

description: Observational and model datasets used in: Modeling cyanobacteria life cycle dynamics and historical nitrogen fixation in the Baltic Proper.
DOI: 10.23729/b6e7c1bc-69a9-401d-aad2-af4b0c062a25
title: Terrestrial laser scanning point clouds from Evo test site, 37 sample plots, autumn 2019
title: Maastolaserkeilauspistepilviaineisto Evon tutkimusalueelta, 37 koealaa, syksy 2019

description: Terrestrial laser scanning point clouds acquired in the autumn of 2019 from 37 sample plots located in Evo, Finland. A Leica RTC 360 A 3D (Leica Geosystems, St. Gallen, Switzerland) time-of-flight laser scanner was used in the data acquisition. A multi-scan approach has been applied in the data acquisition, that is, each sample plot has been scanned from five separate scanning locations: one scan from the plot centre and four scans from quadrant directions (i.e. north-east, south-east, south-west, and north-west of the plot centre). Individual scans have been co-registered using artificial reference targets whose exact locations have been measured using real-time kinematic GNSS. Topography has been removed from the point clouds by normalizing the Z-coordinates to heights above the ground using LAStools-software (http://rapidlasso.com). For a more detailed description of the data acquisition campaign, please see Yrttimaa et al. 2020 (https://doi.org/10.3390/rs12172672).
description: Syksyllä 2019 kerätty maastolaserkeilausaineisto, joka käsittää pistepilvet 37:ltä Evon tutkimusalueella sijaitsevalta metsikkökoealalta (32 x 32 m). Aineiston keruussa käytettiin Leica RTC 360 3D (Leica Geosystems, St. Gallen, Sveitsi) time-of-flight laserkeilainta. Kultakin metsikkökoealalta on kerätty viisi maastolaserkeilauspistepilveä: yksi koealan keskipisteestä ja neljä keskipisteen ympäriltä kohti koealan kulmapisteitä.Yksittäiset pistepilvet on rekisteröity ja yhdistetty toisiinsa tähyspallojen avulla, joiden tarkat sijainnit on mitattu RTK-GNSS:n avulla. Pistepilvien Z-coordinaatit on muunnettu korkeuksiksi maanpinnasta LAStools-ohjelmiston (http://rapidlasso.com) työkalujen avulla. Ks. aineistonkeruun tarkempi kuvaus esim. Yrttimaa et al. 2020 (https://doi.org/10.3390/rs12172672).
DOI: 10.5517/ccdc.csd.cc212cn5
title: CCDC 1879116: Experimental Crystal Structure Determination

description: Related Article: Kevin A. Stewart, Dylan Shuster, Maria Leising, Isaac Coolidge, Erica Lee, Charles Stevens, Andrew J. Peloquin, Daniel Kure, Abby R. Jennings, Scott T. Iacono|2021|Macromolecules|54|4871|doi:10.1021/acs.macromol.1c00333
DOI: 10.5517/ccdc.csd.cc27r6nw
title: CCDC 2077888: Experimental Crystal Structure Determination

description: Related Article: Mareike Liebertseder, Silke Wolf, Claus Feldmann|2021|Z.Anorg.Allg.Chem.|647|2147|doi:10.1002/zaac.202100146
DOI: 10.5517/ccdc.csd.cc27r6px
title: CCDC 2077889: Experimental Crystal Structure Determination

description: Related Article: Mareike Liebertseder, Silke Wolf, Claus Feldmann|2021|Z.Anorg.Allg.Chem.|647|2147|doi:10.1002/zaac.202100146
DOI: 10.5517/ccdc.csd.cc27r6qy
title: CCDC 2077890: Experimental Crystal Structure Determination

description: Related Article: Mareike Liebertseder, Silke Wolf, Claus Feldmann|2021|Z.Anorg.Allg.Chem.|647|2147|doi:10.1002/zaac.202100146
DOI: 10.6084/m9.figshare.17075486
title: Welti_etal._GMTN_data.xlsx

description: Among the many concerns for biodiversity in the Anthropocene, recent reports of flying insect loss are particularly alarming, given their importance as pollinators, pest control agents, and as a food source. Few insect monitoring programmes cover the large spatial scales required to provide more generalizable estimates of insect responses to global change drivers.We ask how climate and surrounding habitat affect flying insect biomass using data from the first year of a new monitoring network at 84 locations across Germany comprising a spatial gradient of land cover types from protected to urban and crop areas.Flying insect biomass increased linearly with temperature across Germany. However, the effect of temperature on flying insect biomass flipped to negative in the hot months of June and July when local temperatures most exceeded long-term averages.Land cover explained little variation in insect biomass, but biomass was lowest in forests. Grasslands, pastures, and orchards harboured the highest insect biomass. The date of peak biomass was primarily driven by surrounding land cover, with grasslands especially having earlier insect biomass phenologies.Standardised, large-scale monitoring provides key insights into the underlying processes of insect decline and is pivotal for the development of climate-adapted strategies to promote insect diversity. In a temperate climate region, we find that the positive effects of temperature on flying insect biomass diminish in a German summer at locations where temperatures most exceeded long-term averages. Our results highlight the importance of local adaptation in climate change-driven impacts on insect communities.
DOI: 10.6084/m9.figshare.17075486.v1
title: Welti_etal._GMTN_data.xlsx

description: Among the many concerns for biodiversity in the Anthropocene, recent reports of flying insect loss are particularly alarming, given their importance as pollinators, pest control agents, and as a food source. Few insect monitoring programmes cover the large spatial scales required to provide more generalizable estimates of insect responses to global change drivers.We ask how climate and surrounding habitat affect flying insect biomass using data from the first year of a new monitoring network at 84 locations across Germany comprising a spatial gradient of land cover types from protected to urban and crop areas.Flying insect biomass increased linearly with temperature across Germany. However, the effect of temperature on flying insect biomass flipped to negative in the hot months of June and July when local temperatures most exceeded long-term averages.Land cover explained little variation in insect biomass, but biomass was lowest in forests. Grasslands, pastures, and orchards harboured the highest insect biomass. The date of peak biomass was primarily driven by surrounding land cover, with grasslands especially having earlier insect biomass phenologies.Standardised, large-scale monitoring provides key insights into the underlying processes of insect decline and is pivotal for the development of climate-adapted strategies to promote insect diversity. In a temperate climate region, we find that the positive effects of temperature on flying insect biomass diminish in a German summer at locations where temperatures most exceeded long-term averages. Our results highlight the importance of local adaptation in climate change-driven impacts on insect communities.
DOI: 10.23729/e417a814-5fde-45fb-9b4e-1a878366f662
title: Terrestrial laser scanning point clouds from Evo test site, 37 sample plots, spring/summer 2014
title: Maastolaserkeilauspistepilviaineisto Evon tutkimusalueelta, 37 koealaa, kevät/kesä 2014

description: Terrestrial laser scanning point clouds acquired in the spring/summer of 2014 from 37 sample plots located in Evo, Finland. A Leica HDS6100 (Leica Geosystems, St. Gallen, Switzerland) and a Faro Focus 3D X330 (Faro Technologies Inc., Lake Mary, FL, USA) phase shift scanners. A multi-scan approach has been applied in the data acquisition, that is, each sample plot has been scanned from five separate scanning locations: one scan from the plot centre and four scans from quadrant directions (i.e. north-east, south-east, south-west, and north-west of the plot centre). Individual scans have been co-registered using artificial reference targets whose exact locations have been measured using real-time kinematic GNSS. Topography has been removed from the point clouds by normalizing the Z-coordinates to heights above the ground using LAStools-software (http://rapidlasso.com). For a more detailed description of the data acquisition campaign, please see Liang et al. 2018 (https://doi.org/10.1016/j.isprsjprs.2018.06.021)
description: Keväällä/kesällä 2014 kerätty maastolaserkeilausaineisto, joka käsittää pistepilvet 37:ltä Evon tutkimusalueella sijaitsevalta metsikkökoealalta (32 x 32 m). Aineisto on kerätty Leica HDS6100 (Leica Geosystems, St. Gallen, Switzerland) ja Faro Focus 3D X330 (Faro Technologies Inc., Lake Mary, FL, USA) vaihe-erolaserkeilaimilla. Kultakin metsikkökoealalta on kerätty viisi maastolaserkeilauspistepilveä: yksi koealan keskipisteestä ja neljä keskipisteen ympäriltä kohti koealan kulmapisteitä. Yksittäiset pistepilvet on rekisteröity ja yhdistetty toisiinsa tähyspallojen avulla, joiden tarkat sijainnit on mitattu RTK-GNSS:llä. Pistepilvien Z-koordinaatit on muunnettu korkeuksiksi maanpinnasta LAStools-ohjelmiston (http://rapidlasso.com) työkalujen avulla. Ks. aineistonkeruun tarkempi kuvaus esim. Liang et al. 2018 (https://doi.org/10.1016/j.isprsjprs.2018.06.021).
DOI: 10.5281/zenodo.5724125
title: Mormyrus lacerda SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5724124
title: Mormyrus lacerda SPAdes preassembly

description: .
DOI: 10.18728/igb-fred-598.0
title: Lake Stechlin ice observations 1961-1997

description: Ice observations at Lake Stechlin including percentage of ice cover and percentage of snow cover on ice in the years 1961-1997.
DOI: 10.15468/2eytw6
title: First insights into the phylogeny of tok-tokkie beetles (Tenebrionidae: Molurina, Phanerotomeina) and examination of the status of the Psammodes vialis species-group

description: This dataset contains the digitized treatments in Plazi based on the original journal article Kamiński, Marcin J., Gearner, Olivia M., Kanda, Kojun, Swichtenberg, Kali, Purchart, Luboš, Smith, Aaron D. (2021): First insights into the phylogeny of tok-tokkie beetles (Tenebrionidae: Molurina, Phanerotomeina) and examination of the status of the Psammodes vialis species-group. Zoological Journal of the Linnean Society 191: 883-901, DOI: 10.1093/zoolinnean/zlaa052
DOI: 10.5281/zenodo.5645609
title: Fink: early supernovae Ia classification using active learning

description: Data and code used to obtain results presented in Leoni et al., 2021, Fink: early supernovae Ia classification using active learning
DOI: 10.5281/zenodo.5645608
title: Fink: early supernovae Ia classification using active learning

description: Data and code used to obtain results presented in Leoni et al., 2021, Fink: early supernovae Ia classification using active learning
DOI: 10.6084/m9.figshare.16863949.v2
title: data of Design and Implementation of Modular 6-DOF Light-weight Cooperative Handling Robot-revised

description: <b>Simulation and </b><b> </b><b>Experimental results of Modular 6-DOF Light-weight Cooperative Handling Robot</b>
DOI: 10.18728/igb-fred-600.0
title: Upper Lake Nehmitz ice observations 2008-2020

description: Ice observations at Upper Lake Nehmitz including percentage of ice cover and percentage of snow cover on ice in the years 2008-2020.
DOI: 10.34740/kaggle/dsv/2847883
title: COVID Vaccination in World (updated daily)
DOI: 10.6084/m9.figshare.17075660.v1
title: Global heat wave records

description: In this study, we first use 3 historical climate datasets of CRU-JAR, ERA5, and GLDAS, as well as 1 future climate dataset of GFDL-ESM4 under 3 SSPs (SSP1-2.6, SSP2-4.5, and SSP5-8.5) to generate longtime global heat wave records from 1971 to 2100.
DOI: 10.6084/m9.figshare.17075660.v2
title: Global heat wave records from 1971 to 2100

description: We employed three historical hourly climate data of CRU-JAR, ERA5, and GLDAS, as well as future daily data of GFDL-ESM4 under 3 SSPs (Shared Socioeconomic Pathways) to produce a longtime global heat wave record dataset and investigate the spatiotemporal variation of global heat waves.
DOI: 10.6084/m9.figshare.17075702.v1
title: Explicit Value Quantification of Demand-side Adjustment on Reducing Distribution Network Expansion Cost

description: The detailed parameters of cases studied in “Explicit Value Quantification of Demand-side Adjustment on Reducing Distribution Network Expansion Cost ”.
DOI: 10.6084/m9.figshare.17075702
title: Explicit Value Quantification of Demand-side Adjustment on Reducing Distribution Network Expansion Cost

description: The detailed parameters of cases studied in “Explicit Value Quantification of Demand-side Adjustment on Reducing Distribution Network Expansion Cost ”.
DOI: 10.5281/zenodo.5724362
title: PEMS-BAY and METR-LA in csv

description: PEMS-BAY and METR-LA dataset in csv <pre><code>@inproceedings{li2018dcrnn_traffic, title={Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting}, author={Li, Yaguang and Yu, Rose and Shahabi, Cyrus and Liu, Yan}, booktitle={International Conference on Learning Representations (ICLR '18)}, year={2018} }</code></pre>
DOI: 10.5281/zenodo.4263965
title: PEMS-BAY and METR-LA in csv

description: PEMS-BAY and METR-LA dataset in csv <pre><code>@inproceedings{li2018dcrnn_traffic, title={Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting}, author={Li, Yaguang and Yu, Rose and Shahabi, Cyrus and Liu, Yan}, booktitle={International Conference on Learning Representations (ICLR '18)}, year={2018} }</code></pre>
DOI: 10.3886/e155381
title: Risk and protective factors of college students’ psychological well-being during the COVID-19 pandemic: emotional stability, mental health, and household resources

description: This is the data deposit file for the article "Risk and protective factors of college students’ psychological well-being during the COVID-19 pandemic: emotional stability, mental health, and household resources", published by the journal AERA OPEN.<br><br>The P.I. of the project is Richard Arum. <br>The authors of this article are: Julia Moeller, Luise von Keyserlingk, Marion Spengler, Hanna Gaspard, Hye Rin Lee, Katsumi Yamaguchi-Pedroza; Renzhe Yu, Christian Fischer, & Richard Arum.<br><br>We used the following data in this study: <b>A pilot study of experience sampling method data, combined with pre- and a post- online surveys collected as part of the UCI Measuring Undergraduate Success Trajectories project (UCI-MUST).</b> <br><br>These data were provided upon a request to the authors of this study, all of whom were part of the research team at a time. <br><br>We are grateful to the MUST study team and, in particular to the principal investigator of the MUST study, Richard Arum, for allowing us to use the dataset. <br><b></b><br>The agreement we signed with the principal investigator and the consent forms provided to our participants prevent us from sharing these data, due to these data belonging to a pilot study that was not originally intended to be published as an openly available data set. <br><br>To make (partially) up for this limitation in sharing the data openly, we have opted to making all outputs openly available to give other researchers the opportunity to check our analyses and results. The Mplus outputs can be found here: https://osf.io/s29a8/<br><br>It is planned to make other datasets collected by this research project openly available at a later time point. <br><br>For further information on this research project, please visit the project website https://education.uci.edu/next-gen-ug-success-project.html
description: Experience Sampling Method in combination with online pre- and post-test surveys.
DOI: 10.5281/zenodo.5719431
title: IPBES regions and sub-regions

description: <strong>This dataset describes the IPBES regions and sub-regions and their corresponding countries or areas.</strong> ipbes_regions_subregions.csv GID_0: Corresponds to the GID from GADM (https://gadm.org/) and offered to facilitates the preparation of maps ISO_3166_alpha_3: Corresponds to the International Standard for country codes and codes for their subdivisions (https://www.iso.org/iso-3166-country-codes.html) Region: Corresponds to IPBES regions Sub-region: Corresponds to IPBES subregions ipbes_regions.png: map of the IPBES regions (Robinson projection https://epsg.io/54030) ipbes_sub_regions.png: map of the IPBES sub-regions (Robinson projection https://epsg.io/54030) ipbes_regions_shapefile.R: The R code used to link the "ipbes_regions_subregions.csv" to GADM (https://gadm.org/) boundaries ipbes_region_mapping.R The R code used to map the IPBES regions and sub-regions.<br> <br> <em>Inquiries: IPBES technical support unit on knowledge and data - tsu.data@ipbes.net</em>
description: The assignment of countries or areas to specific groupings is for statistical convenience and does not imply the expression of any opinion whatsoever on the part of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services concerning the legal status of any country, territory, city or area of its authorities, or concerning the delimitation of its frontiers or boundaries.
DOI: 10.5281/zenodo.3923633
title: IPBES regions and sub-regions

description: <strong>This dataset describes the IPBES regions and sub-regions and their corresponding countries or areas.</strong> ipbes_regions_subregions.csv GID_0: Corresponds to the GID from GADM (https://gadm.org/) and offered to facilitates the preparation of maps ISO_3166_alpha_3: Corresponds to the International Standard for country codes and codes for their subdivisions (https://www.iso.org/iso-3166-country-codes.html) Region: Corresponds to IPBES regions Sub-region: Corresponds to IPBES subregions ipbes_regions.png: map of the IPBES regions (Robinson projection https://epsg.io/54030) ipbes_sub_regions.png: map of the IPBES sub-regions (Robinson projection https://epsg.io/54030) ipbes_regions_shapefile.R: The R code used to link the "ipbes_regions_subregions.csv" to GADM (https://gadm.org/) boundaries ipbes_region_mapping.R The R code used to map the IPBES regions and sub-regions.<br> <br> <em>Inquiries: IPBES technical support unit on knowledge and data - tsu.data@ipbes.net</em>
description: The assignment of countries or areas to specific groupings is for statistical convenience and does not imply the expression of any opinion whatsoever on the part of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services concerning the legal status of any country, territory, city or area of its authorities, or concerning the delimitation of its frontiers or boundaries.
DOI: 10.6084/m9.figshare.17075660.v3
title: Global heat wave records from 1971 to 2100

description: We employed three historical hourly climate data of CRU-JAR, ERA5, and GLDAS, as well as future daily data of GFDL-ESM4 under 3 SSPs (Shared Socioeconomic Pathways) to produce a longtime global heat wave record dataset and investigate the spatiotemporal variation of global heat waves.
DOI: 10.5281/zenodo.5724218
title: Falco cenchroides SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5724217
title: Falco cenchroides SPAdes preassembly

description: .
DOI: 10.5281/zenodo.5708224
title: Random Forest model for PPI predictions

description: The uploaded model contains the trained random forest model to predict protein-protein Interactions based on their amino acid sequence. The model is described in our preprint "ProteinPrompt: a webserver for predicting protein-protein interactions" which can be found on bioRxiv.org: https://doi.org/10.1101/2021.09.03.458859 The model was trained with the scikit-learn package in version 0.20.3 under Python 3.7.12
DOI: 10.5281/zenodo.5708223
title: Random Forest model for PPI predictions

description: The uploaded model contains the trained random forest model to predict protein-protein Interactions based on their amino acid sequence. The model is described in our preprint "ProteinPrompt: a webserver for predicting protein-protein interactions" which can be found on bioRxiv.org: https://doi.org/10.1101/2021.09.03.458859 The model was trained with the scikit-learn package in version 0.20.3 under Python 3.7.12
DOI: 10.6084/m9.figshare.17075717
title: vacancy_skills_data

description: 3 datasets representing processed skill-sets for job advertisements obtained from HeadHunter online hiring platform (collected with open API https://dev.hh.ru/) for specialists in Information Technologies (in accordance with classifier https://github.com/hhru/api/blob/master/docs_eng/specializations.md). Description of main fields for vacancies is available via link https://github.com/hhru/api/blob/master/docs_eng/vacancies.md.<br><b>Datasets</b>:<br>1. "<i>vacancy_skill.csv</i>" - two column dataset, representing vacancy ID ("vacid") and processed skill name [in-demand skill] ("lv");<br>2. "<i>sh_soft_clusters.csv</i>" - three column dataset, representing initial formulations (translated) of "soft" skills ("V1"), the frequency of occurrence in the sample ("V2"), the etalon name [generalized categories of "soft" skills] ("ETALON");<br>3. "<i>jaccard_matrix.csv</i>" - dissimilarity square matrix between processed skill names (1,730 X 1,730) with Jaccard distances (computed by comparison of vacancy ID sets for each skill pair) [the first row and the first column contain skill names].
DOI: 10.5519/qd.nx3iqo9l
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.6084/m9.figshare.17075660.v4
title: Global heat wave records from 1971 to 2100

description: We employed three historical hourly climate data of CRU-JAR, ERA5, and GLDAS, as well as future daily data of GFDL-ESM4 under 3 SSPs (Shared Socioeconomic Pathways) to produce a longtime global heat wave record dataset and investigate the spatiotemporal variation of global heat waves.
DOI: 10.6084/m9.figshare.14120129
title: Features computed for each mitochondrial trajectory

description: This data was obtained by applying the package trajpy (github.com/ocbe-uio/trajpy) to neuronal mitochondrial trajectories.<br><b>Acronyms: </b>dop - 6-hydroxydopaminerot - rotenonectrl - control <br><b>Data dictionary </b>variable -- description -- possible values<br><b>condition </b>--<b> </b>treatment -- dop, rot or ctrl<b>concentration</b> -- 0 (control), 6.25, 12.5, 25, 50 (dop), 31.25, 62.5, 125, 250 (rot)<b>hour </b>-- treatment duration -- 24h, 96h<b>lifetime </b>-- trajectory duration -- discrete (seconds)<b>net </b>-- net displacement -- continuous (micrometers)<b>velocity </b>-- mean velocity -- continuous (micrometers/s)<b>anisotropy </b>--<b> </b>degree of asymmetry and randomness -- continuous<b>kurtosis </b>-- quartic moment along the first main component -- continuous<b>straightness </b>-- similarity to a straight trajectory -- continuous<b>efficiency </b>-- degree of irregularity -- continuous<b>file </b>-- experimental video file from which the trajectory was obtained<b><br></b>
DOI: 10.6084/m9.figshare.14120129.v2
title: Features computed for each mitochondrial trajectory

description: This data was obtained by applying the package trajpy (github.com/ocbe-uio/trajpy) to neuronal mitochondrial trajectories.<br><b>Acronyms: </b>dop - 6-hydroxydopaminerot - rotenonectrl - control <br><b>Data dictionary </b>variable -- description -- possible values<br><b>condition </b>--<b> </b>treatment -- dop, rot or ctrl<b>concentration</b> -- 0 (control), 6.25, 12.5, 25, 50 (dop), 31.25, 62.5, 125, 250 (rot)<b>hour </b>-- treatment duration -- 24h, 96h<b>lifetime </b>-- trajectory duration -- discrete (seconds)<b>net </b>-- net displacement -- continuous (micrometers)<b>velocity </b>-- mean velocity -- continuous (micrometers/s)<b>anisotropy </b>--<b> </b>degree of asymmetry and randomness -- continuous<b>kurtosis </b>-- quartic moment along the first main component -- continuous<b>straightness </b>-- similarity to a straight trajectory -- continuous<b>efficiency </b>-- degree of irregularity -- continuous<b>file </b>-- experimental video file from which the trajectory was obtained<b><br></b>