cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.bakqzx
title: Occurrence Download

description: A dataset containing 3113799 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Falco peregrinus Tunstall, 1771, Circaetus gallicus (Gmelin, 1788), Dendrocygna javanica (Horsfield, 1821), Tadorna ferruginea (Pallas, 1764), Bubulcus ibis (Linnaeus, 1758), Bulbucus ibis, Ardea alba Linnaeus, 1758, Ardea alba Christidis & Boles, 1994, Ardea alba Sacc, 2005, Tyto alba (Scopoli, 1769), Actitis hypoleucos (Linnaeus, 1758), Actites hypoleucos (Linnaeus, 1758), Gallinago gallinago (Linnaeus, 1758), Eudynamys scolopaceus (Linnaeus, 1758), Eudynamys scolopacea (Linnaeus, 1758), Eudynamis scolopaceus (Linnaeus, 1758), Asio otus (Linnaeus, 1758), Toxostoma redivivum (Gambel, 1845), Toxostoma longirostre (Lafresnaye, 1838), Setophaga striata (J.R.Forster, 1772), Scolopax minor J.F.Gmelin, 1789, Scolopax major, Buteo swainsoni Bonaparte, 1838, Cardinalis sinuatus Bonaparte, 1838, Melanerpes aurifrons (Wagler, 1829), Centrocercus urophasianus (Bonaparte, 1827), Toxostoma lecontei Lawrence, 1851, Callipepla squamata (Vigors, 1830), Buteo regalis (G.R.Gray, 1844), Spizella atrogularis (Cabanis, 1851), Ammodramus savannarum (J.F.Gmelin, 1789), Coccyzus erythropthalmus (A.Wilson, 1811), Coccyzus erythopthalmus (Wilson, 1811), Coccyzus erythrophthalmus (Wilson, 1811), Parabuteo unicinctus (Temminck, 1824), Ammodramus henslowii (Audubon, 1829), Rhynchophanes mccownii (Lawrence, 1851), Tympanuchus cupido (Linnaeus, 1758), Bartramia longicauda (Bechstein, 1812), Baratramia longicauda (Bechstein, 1812), Dolichonyx oryzivorus (Linnaeus, 1758), Cistothorus platensis (Latham, 1790), Campylorhynchus brunneicapillus (Lafresnaye, 1835), Haliaeetus leucocephalus (Linnaeus, 1766), Gyps coprotheres (J.R.Forster, 1798), Aquila nipalensis Hodgson, 1833, Clanga pomarina (C.L.Brehm, 1831), Milvus milvus (Linnaeus, 1758), Gypaetus barbatus (Linnaeus, 1758), Pernis ptilorhynchus (Temminck, 1821), Pernis ptilorhyncus (Temminck, 1821), Neophron percnopterus (Linnaeus, 1758), Neophron perenopterus (Linnaeus, 1758), Buteo rufinus (Cretzschmar, 1829), Milvus migrans (Boddaert, 1783), Falco biarmicus Temminck, 1825, Falco subbuteo Linnaeus, 1758, Falco buteo Linnaeus, 1758, Falco columbarius Linnaeus, 1758, Falco palumbarius Linnaeus, 1758, Falco eleonorae Géné, 1839, Asio flammeus (Pontoppidan, 1763), Branta ruficollis (Pallas, 1769), Blatta ruficollis Fabricius, 1787, Calidris tenuirostris (Horsfield, 1821))",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Unknown evidence"
 ]
 }
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "CoordinateUncertaintyInMeters is greater than or equal to 1000",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 3113799 records from 563 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064192-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14097178
title: Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland

description: The probabilistic projections are part of the work: Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, https://doi.org/10.1093/pnasnexus/pgad321

Please cite the article together with the Zenodo link when you use the data.

The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:

Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.Version 2024-2050: Projections for the years 2024-2050, using the latest data of 2023.

The computations were performed at University of Geneva using Baobab HPC service.

This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.
DOI: 10.5281/zenodo.8414846
title: Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland

description: The probabilistic projections are part of the work:Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, https://doi.org/10.1093/pnasnexus/pgad321
Please cite the article together with the Zenodo link when you use the data.
The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:
Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.
This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.
DOI: 10.5281/zenodo.8414845
title: Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland

description: The probabilistic projections are part of the work: Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, https://doi.org/10.1093/pnasnexus/pgad321

Please cite the article together with the Zenodo link when you use the data.

The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:

Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.Version 2024-2050: Projections for the years 2024-2050, using the latest data of 2023.

The computations were performed at University of Geneva using Baobab HPC service.

This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.
DOI: 10.5281/zenodo.8414935
title: Probabilistic projections of granular energy technology diffusion at subnational level - solar photovoltaics, heat pumps, and battery electric vehicles in Switzerland

description: The probabilistic projections are part of the work: Nik Zielonka, Xin Wen, Evelina Trutnevyte, Probabilistic projections of granular energy technology diffusion at subnational level, PNAS Nexus, Volume 2, Issue 10, October 2023, pgad321, https://doi.org/10.1093/pnasnexus/pgad321

Please cite the article together with the Zenodo link when you use the data.

The provided data files contain the estimated probabilistic projections for all Swiss municipalities on the actual diffusion of solar photovoltaics (PV), heat pumps, and battery electric vehicles (BEVs) in Switzerland for the indicated years:

Version 2022-2050: Projections for the years 2022-2050 as presented by Zielonka et. al (2023), PNAS Nexus.Version 2023-2050: Projections for the years 2023-2050, using the latest data of 2022.

The computations were performed at University of Geneva using Baobab HPC service.

This research was carried out with the support of the Swiss Federal Office of Energy SFOE as part of the SWEET project SURE (N.Z., E.T.) and the Swiss National Science Foundation Eccellenza Grant as part of the project "Accuracy of long-range national energy projections" (Grant no. 186834, X.W., E.T.). The authors bear sole responsibility for the conclusions and the results.
DOI: 10.15468/dl.tqseec
title: Occurrence Download

description: A dataset containing 6302 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Arachis hypogaea L.",
 "Year 1970-2000"
 ]
}

 The dataset includes 6302 records from 112 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010299-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.88xtvy
title: Occurrence Download

description: A dataset containing 1247 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eryngium corniculatum Lam."
 ]
}

 The dataset includes 1247 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010300-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10008351
title: Organisation of the nervous system in cysts of Thulinius ruffoi

description: The data collected show the elements of the nervous system in cysts of Thu. ruffoi at specific and unspecified stages of encystment. Text files contain additional technical information.
description: This work was partially supported by the National Science Centre (grant number 2020/37/N/NZ8/00170)
DOI: 10.15468/dl.h7f64n
title: Occurrence Download

description: A dataset containing 109 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Elaeocarpus prunifolioides Hu"
 ]
}

 The dataset includes 109 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010301-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kw2bke
title: Occurrence Download

description: A dataset containing 123 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Eryngium variifolium Coss."
 ]
}

 The dataset includes 123 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010302-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48420/23791680.v3
title: TPI UK ITL3 Scorecards

description: The TPI UK ITL3 scorecards are produced to assess the United Kingdom's subregional productivity performance through a range of productivity indicators and drivers. These scorecards include data for 179 regions defined by the International Territorial Level 3 (ITL3). In addition, data is available for 12 aggregate ITL1 geographies, covering the whole of the United Kingdom. The Data is available for three <i>indicators</i> of productivity, and 12 productivity <i>drivers. </i>The 2024 edition refers to data for the years 2020, 2021 and 2022.The current ITL3 scorecards complement previous versions of the TPI UK ITL3 Productivity Scorecards and the TPI UK ITL1 Productivity Scorecards. The TPI UK ITL3 Productivity Scorecards provide a higher level of geographical granularity. They can be used as a tool by policymakers at the regional level to assess the productivity performance in their region, both relative to their ITL1 parent region and the UK as a whole, including key indicators that influence this performance.<b>This dataset includes:</b>Sources and methods documentation12 PDF files with the ITL3 scorecards for each of the ITL1 regions in the United KingdomCSV data file containing harmonised input data at the ITL3 and ITL1 levelCSV data file containing scorecard indicators and drivers at the ITL3 and ITL1 levelTPI UK ITL3 DashboardsThe <i>TPI Productivity Dashboards</i> are interactive representations of the TPI UK ITL3 Scorecards. These dashboards have been created as a visualisation tool to fulfil the need for relevant data to allow policymakers to compare regional productivity performances across the country and to identify regional drivers that facilitate or hinder productivity in different UK geographic areas. Using the <i>UK TPI Productivity Dashboards</i>, policymakers and stakeholders can drill down to sub-regional levels and productivity measure perspectives. The Dashboards below offer an interactive and easy-to-use way to build a picture of productivity at different points in time.<b>Regional Interactive Dashboards:</b>United Kingdom (ITL1 level)North EastNorth WestYorkshire and The HumberEast MidlandsWest MidlandsEast of EnglandLondonSouth EastSouth WestWalesScotlandNorthern Ireland
DOI: 10.48420/23791680
title: TPI UK ITL3 Scorecards

description: The TPI UK ITL3 scorecards are produced to assess the United Kingdom's subregional productivity performance through a range of productivity indicators and drivers. These scorecards include data for 179 regions defined by the International Territorial Level 3 (ITL3). In addition, data is available for 12 aggregate ITL1 geographies, covering the whole of the United Kingdom. The Data is available for three <i>indicators</i> of productivity, and 12 productivity <i>drivers. </i>The 2024 edition refers to data for the years 2020, 2021 and 2022.The current ITL3 scorecards complement previous versions of the TPI UK ITL3 Productivity Scorecards and the TPI UK ITL1 Productivity Scorecards. The TPI UK ITL3 Productivity Scorecards provide a higher level of geographical granularity. They can be used as a tool by policymakers at the regional level to assess the productivity performance in their region, both relative to their ITL1 parent region and the UK as a whole, including key indicators that influence this performance.<b>This dataset includes:</b>Sources and methods documentation12 PDF files with the ITL3 scorecards for each of the ITL1 regions in the United KingdomCSV data file containing harmonised input data at the ITL3 and ITL1 levelCSV data file containing scorecard indicators and drivers at the ITL3 and ITL1 levelTPI UK ITL3 DashboardsThe <i>TPI Productivity Dashboards</i> are interactive representations of the TPI UK ITL3 Scorecards. These dashboards have been created as a visualisation tool to fulfil the need for relevant data to allow policymakers to compare regional productivity performances across the country and to identify regional drivers that facilitate or hinder productivity in different UK geographic areas. Using the <i>UK TPI Productivity Dashboards</i>, policymakers and stakeholders can drill down to sub-regional levels and productivity measure perspectives. The Dashboards below offer an interactive and easy-to-use way to build a picture of productivity at different points in time.<b>Regional Interactive Dashboards:</b>United Kingdom (ITL1 level)North EastNorth WestYorkshire and The HumberEast MidlandsWest MidlandsEast of EnglandLondonSouth EastSouth WestWalesScotlandNorthern Ireland
DOI: 10.48420/23791680.v2
title: TPI UK ITL3 Scorecards

description: The TPI UK ITL3 scorecards are produced to assess the United Kingdom's subregional productivity performance through a range of productivity indicators and drivers. These scorecards include data for 179 regions, defined by the International Territorial Level 3 (ITL3). In addition data is available for 12 aggregate ITL1 geographies, covering the whole of the United Kingdom. Data is available for three <i>indicators</i> of productivity, and 12 productivity <i>drivers, </i>for the years 2019, 2020 and 2021.The current ITL3 scorecards complement the TPI UK ITL1 Productivity Scorecards, published by the Productivity Lab in January 2023. Providing a higher level of geographical granularity, the ITL3 scorecards can be used as a tool by policymakers at the regional level, to assess the productivity performance in their region, both relative to their ITL1 parent region as well as the UK as a whole, including key indicators that influence this performance.<b>This dataset includes:</b>Sources and methods documentation12 PDF files with the ITL3 scorecards for each of the ITL1 regions in the United KingdomCSV data file containing: harmonised input data, at the ITL3 and ITL1 levelCSV data file containing: with scorecard indicators and drivers at the ITL3 and ITL1 levelTPI UK ITL3 DashboardsThe <i>TPI Productivity Dashboards</i> are interactive representations of the TPI UK ITL3 Scorecards. These dashboards have been created as a visualisation tool to fulfil the need for relevant data to allow policymakers to compare regional productivity performances across the country and to identify regional drivers that facilitate or hinder productivity in different UK geographic areas. Using the <i>UK TPI Productivity Dashboards</i>, policymakers and stakeholders can drill down to sub-regional levels and productivity measure perspectives. The Dashboards below offer an interactive and easy-to-use way to build a picture of productivity at different points in time.<b>Regional Interactive Dashboards:</b>United Kingdom (ITL1 level)North EastNorth WestYorkshire and The HumberEast MidlandsWest MidlandsEast of EnglandLondonSouth EastSouth WestWalesScotlandNorthern Ireland
DOI: 10.15468/dl.pr7hu6
title: Occurrence Download

description: A dataset containing 5040 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Biodiversidad asociada al proyecto Interconexión Cocorná - La Sierra 110 kV"
 ]
}

 The dataset includes 5040 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010305-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qcu6r3
title: Occurrence Download

description: A dataset containing 5040 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Biodiversidad asociada al proyecto Interconexión Cocorná - La Sierra 110 kV"
 ]
}

 The dataset includes 5040 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010304-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15129/a63f3a52-1753-46bf-9bf8-449bb4b9c16d
title: Data for: "Exploring the impact of commonly used ionizable and pegylated lipids on mRNA-LNPs: A combined in vitro and preclinical perspective"

description: Ionizable lipids are widely recognized as the crucial component of lipid nanoparticles (LNPs). They enable mRNA encapsulation, shield it from enzymatic degradation, facilitate cellular uptake, and foster its cytosolic release for subsequent translation into proteins. In addition, PEGylated lipids are added to stabilize the particles in storage and in vivo. In this study, we investigate the potency of LNPs prepared using commonly adopted ionizable and pegylated lipids in vitro (using HEK293 cells) and in vivo (mouse studies) to consider the impact of structure on potency. LNPs were prepared using a fixed molar ratio of DSPC: Cholesterol: ionizable/cationic lipid: PEG lipid (10:38.5:50:1.5 mol%). All LNP formulations exhibited similar critical quality attributes (CQAs) with sizes under 100 nm, low PDI (&lt;0.2), near-neutral zeta potentials, and high encapsulation efficiencies (&gt;90%). However, the potency of these LNPs, as measured by in vitro mRNA expression and in vivo expression after intramuscular injection in mice varied significantly. LNPs formulated with SM-102 showed the highest expression in vitro, whilst in vivo SM-102 and ALC-0315 LNPs showed significantly higher mRNA expression than DLin-MC3-DMA, DODAP and DOTAP LNPs. We also investigated the effect of PEG lipid choice (ALC-0159, DMG-PEG2k, and DSPE-PEG2k). The choice of PEG lipid did not impact CQAs or LNP clearance from the injection site but significantly influenced mRNA expression with the incorporation of DSPE-PEG2k within the LNPs reduced expression both in vitro and in vivo. This work contributes valuable insights to the evolving landscape of mRNA research, emphasizing that CQAs are a marker of the quality of the LNP production process but not discriminatory regarding LNP potency. Similarly, standard in vitro studies do not provide insights into in vivo potency. These results further emphasize the intricacies of formulation design and the importance of bridging gaps between experimental outcomes in different settings.
DOI: 10.15468/dl.h58s98
title: Occurrence Download

description: A dataset containing 34258 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Animal Sound Archive"
 ]
}

 The dataset includes 34258 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010303-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.52szw7
title: Occurrence Download

description: A dataset containing 2505451 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Rana temporaria Linnaeus, 1758, Bufo bufo (Linnaeus, 1758), Ichthyosaura alpestris (Laurenti, 1768), Pelophylax esculentus (Linnaeus, 1758), Pelophylax lessonae x Pelophylax ridibundus, Hyla arborea (Linnaeus, 1758), Bombina variegata (Linnaeus, 1758), Rana arvalis Nilsson, 1842, Triturus cristatus (Laurenti, 1768), Lissotriton vulgaris (Linnaeus, 1758), Pelobates fuscus (Laurenti, 1768), Pelophylax ridibundus (Pallas, 1771), Pelophylax lessonae (Camerano, 1882), Epidalea calamita (Cope, 1864), Lissotriton helveticus (Razoumovsky, 1789), Alytes obstetricans (Laurenti, 1768), Salamandra salamandra (Linnaeus, 1758))"
 ]
}

 The dataset includes 2505451 records from 5148 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010298-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hmpa23
title: Occurrence Download

description: A dataset listing the 7548 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-6.36656 43.10407,-5.24267 43.10407,-5.24267 43.65071,-6.36656 43.65071,-6.36656 43.10407))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2000-2024"
 ]
}

 The dataset's 7548 records were derived from 133 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047748-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.