cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48597/5bmj-hkx6
title: Measurement of Calcium at Løken

description: Measurement of Calcium at Løken
DOI: 10.48597/74sg-z5vk
title: Measurement of pH at Løken

description: Measurement of pH at Løken
DOI: 10.48597/evb2-3m98
title: Measurement of Chloride at Løken

description: Measurement of Chloride at Løken
DOI: 10.48597/ksps-hk8z
title: Measurement of Conductivity at Løken

description: Measurement of Conductivity at Løken
DOI: 10.48597/se63-q2f9
title: Measurement of Inorganics in precipitation at Løken

description: Measurement of Inorganics in precipitation at Løken
DOI: 10.48597/ct7c-d6sa
title: Measurement of Sulphate_corrected at Løken

description: Measurement of Sulphate_corrected at Løken
DOI: 10.48597/pkxf-83xp
title: Measurement of Inorganics in precipitation at Løken

description: Measurement of Inorganics in precipitation at Løken
DOI: 10.48597/f5dg-ypeq
title: Measurement of Inorganics in precipitation at Løken

description: Measurement of Inorganics in precipitation at Løken
DOI: 10.48597/f8gd-2mku
title: Measurement of Inorganics in precipitation at Løken

description: Measurement of Inorganics in precipitation at Løken
DOI: 10.48597/gm56-mru8
title: Measurement of Inorganics in precipitation at Løken

description: Measurement of Inorganics in precipitation at Løken
DOI: 10.48597/jckx-p795
title: Measurement of Inorganics in precipitation at Løken

description: Measurement of Inorganics in precipitation at Løken
DOI: 10.48597/sthw-k2vk
title: Measurement of Inorganics in precipitation at Løken

description: Measurement of Inorganics in precipitation at Løken
DOI: 10.48597/6z6d-crc2
title: Measurement of Sulphate_corrected at Løken

description: Measurement of Sulphate_corrected at Løken
DOI: 10.48597/n74z-qkn2
title: Measurement of Sulphate_corrected at Løken

description: Measurement of Sulphate_corrected at Løken
DOI: 10.48597/rspy-qdfk
title: Measurement of Sulphate_corrected at Løken

description: Measurement of Sulphate_corrected at Løken
DOI: 10.48597/2n7e-9q3g
title: Measurement of Sulphate_corrected at Løken

description: Measurement of Sulphate_corrected at Løken
DOI: 10.48597/cptn-dtxg
title: Measurement of Sulphate_corrected at Løken

description: Measurement of Sulphate_corrected at Løken
DOI: 10.48597/fbyc-s7hx
title: Measurement of Sulphate_corrected at Løken

description: Measurement of Sulphate_corrected at Løken
DOI: 10.48597/t2xp-vmv3
title: Measurement of Sulphate_corrected at Løken

description: Measurement of Sulphate_corrected at Løken
DOI: 10.48597/w2h2-djv5
title: Measurement of Sulphate_corrected at Løken

description: Measurement of Sulphate_corrected at Løken
DOI: 10.5281/zenodo.8421485
title: Information Trapping by Topologically Protected Edge States: Scrambling and the Butterfly Velocity

description: Data files, in the form of Mathematica mx files, for "Information Trapping by Topologically Protected Edge States: Scrambling and the Butterfly Velocity". Included are data for the OTOCs for two models. Parameters and models are given in the file names. For the Kitaev model the form is {System size, location of perturbation, {J_0, Delta_0, mu_}, {J_1, Delta_1, mu_1}, Invariant, Invariant} (The invariant is written twice at the end due to bookkeeping). For the SSH model 'pert' refers to the location of the unitary perturbation, 'edge' refers to OTOCs only calculated at teh system boundary, and 'longt' are data for longer times. See the paper for further details.

https://doi.org/10.1103/PhysRevB.108.184303

https://doi.org/10.48550/arXiv.2306.00527
description: This work was supported by the National Science Centre (NCN, Poland) under the grant 2019/35/B/ST3/03625.
DOI: 10.5281/zenodo.8421484
title: Information Trapping by Topologically Protected Edge States: Scrambling and the Butterfly Velocity

description: Data files, in the form of Mathematica mx files, for "Information Trapping by Topologically Protected Edge States: Scrambling and the Butterfly Velocity". Included are data for the OTOCs for two models. Parameters and models are given in the file names. For the Kitaev model the form is {System size, location of perturbation, {J_0, Delta_0, mu_}, {J_1, Delta_1, mu_1}, Invariant, Invariant} (The invariant is written twice at the end due to bookkeeping). For the SSH model 'pert' refers to the location of the unitary perturbation, 'edge' refers to OTOCs only calculated at teh system boundary, and 'longt' are data for longer times. See the paper for further details.

https://doi.org/10.1103/PhysRevB.108.184303

https://doi.org/10.48550/arXiv.2306.00527
description: This work was supported by the National Science Centre (NCN, Poland) under the grant 2019/35/B/ST3/03625.
DOI: 10.15468/dl.hzr5wy
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leptopelis xenodactylus Poynton, 1963"
 ]
}

 The dataset includes 14 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010189-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18758/71021051
title: Simulations of the magnetosphere - ionosphere coupling to estimate the Physical Properties of Magnetospheric Generators from Observations of Quiet Discrete Auroral Arcs

description: We designed a method to estimate the properties of the auroral generator from low-altitude observations of aurora. 

The method is based on a magnetosphere – ionosphere quasi-stationary coupling model, a parametric description of a magnetospheric interface generator and a minimization procedure that finds the generator model whose auroral effects best fit in-situ observations. 

The database includes the results of the magnetosphere-ionosphere coupling model used to test the methodology. The method can be generalized to other types of generators and observables.
DOI: 10.15468/dl.2h9f6z
title: Occurrence Download

description: A dataset listing the 8 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pinus strobus L."
 ]
}

 The dataset's 8 records were derived from 609 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010187-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z7nze7
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leptopelis yaldeni Largen, 1977"
 ]
}

 The dataset includes 42 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010191-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qd4gec
title: Occurrence Download

description: A dataset containing 141 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Batrachyla antartandica Barrio, 1967"
 ]
}

 The dataset includes 141 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010195-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g3emc5
title: Occurrence Download

description: A dataset containing 174 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Country is Australia",
 "GadmGid is AUS.5_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Pherosphaera fitzgeraldii (F.Muell.) Hook.f."
 ]
}

 The dataset includes 174 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010194-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.myq5db
title: Occurrence Download

description: A dataset containing 186 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-45.19779187520658 -19.994284232624757,-42.56669687544268 -19.994284232624757,-42.56669687544268 -17.363189232860854,-45.19779187520658 -17.363189232860854,-45.19779187520658 -19.994284232624757))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Acritopappus longifolius (Gardner) R.M.King & H.Rob."
 ]
}

 The dataset includes 186 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010198-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.14459/2024mp1748915
title: Cycle aging data of automotive-grade lithium ion battery cells under realistic and accelerated load conditions

description: This data set contains cycle aging data from three automotive-grade NMC|Gr pouch cells (used in the VW ID.3) subjected to three different usage patterns over a duration of 3+ years. Please refer to the linked article for cell and measurement specifications. All measurements are provided as .txt files (raw files exported from the battery test system).
DOI: 10.3886/e207770v3
title: ECIN Replication Package for "Pricing in Response to New Information: The Case of Betting Markets"

description: Markets are information aggregators. But how do they incorporate new data into their pricing? We examine the response of prediction markets to a novel information shock in a quasi-natural experiment: How did the absence announcements of elite soccer players influence the betting odds of affected matches? Analyzing the first four statistical moments of 117,174 odds from 32 bookmakers, we identify initial inertia followed by a lagged reaction that we cannot reason with learning. Our findings raise questions about how bettors and bookmakers incorporate new information into their beliefs. It has broader implications regarding information processing in markets.<br><br>The present dataset contains 117,174 closing odds for 1,372 soccer matches in the German Bundesliga 1 and the Italian Serie A for the seasons 2019/2020 and 202/2021. It combines this information with match outcomes and COVID-19 infection information for the clubs participating in the two leagues. All infection data is based on publicly available information, e.g., announcements by the press, players, or their clubs.<br><br>
description: Betting odds of bookmakersSoccer clubs in the German Bundesliga 1 or the Italian Serie A in either one of the seasons 2019/2020 or 2020/2021 or both<br><br>.
DOI: 10.3886/e207770v1
title: ECIN Replication Package for "Pricing in Response to New Information: The Case of Betting Markets"

description: Markets are information aggregators. But how do they incorporate new data into their pricing? We examine the response of prediction markets to a novel information shock in a quasi-natural experiment: How did the absence announcements of elite soccer players influence the betting odds of affected matches? Analyzing the first four statistical moments of 117,174 odds from 32 bookmakers, we identify initial inertia followed by a lagged reaction that we cannot reason with learning. Our findings raise questions about how bettors and bookmakers incorporate new information into their beliefs. It has broader implications regarding information processing in markets.<br><br>The present dataset contains 117,174 closing odds for 1,372 soccer matches in the German Bundesliga 1 and the Italian Serie A for the seasons 2019/2020 and 202/2021. It combines this information with match outcomes and COVID-19 infection information for the clubs participating in the two leagues. All infection data is based on publicly available information, e.g., announcements by the press, players, or their clubs.<br><br>
description: Betting odds of bookmakersSoccer clubs in the German Bundesliga 1 or the Italian Serie A in either one of the seasons 2019/2020 or 2020/2021 or both<br><br>.
DOI: 10.3886/e207770v2
title: ECIN Replication Package for "Pricing in Response to New Information: The Case of Betting Markets"

description: Markets are information aggregators. But how do they incorporate new data into their pricing? We examine the response of prediction markets to a novel information shock in a quasi-natural experiment: How did the absence announcements of elite soccer players influence the betting odds of affected matches? Analyzing the first four statistical moments of 117,174 odds from 32 bookmakers, we identify initial inertia followed by a lagged reaction that we cannot reason with learning. Our findings raise questions about how bettors and bookmakers incorporate new information into their beliefs. It has broader implications regarding information processing in markets.<br><br>The present dataset contains 117,174 closing odds for 1,372 soccer matches in the German Bundesliga 1 and the Italian Serie A for the seasons 2019/2020 and 202/2021. It combines this information with match outcomes and COVID-19 infection information for the clubs participating in the two leagues. All infection data is based on publicly available information, e.g., announcements by the press, players, or their clubs.<br><br>
description: Betting odds of bookmakersSoccer clubs in the German Bundesliga 1 or the Italian Serie A in either one of the seasons 2019/2020 or 2020/2021 or both<br><br>.
DOI: 10.3886/e207770
title: ECIN Replication Package for "Pricing in Response to New Information: The Case of Betting Markets"

description: Markets are information aggregators. But how do they incorporate new data into their pricing? We examine the response of prediction markets to a novel information shock in a quasi-natural experiment: How did the absence announcements of elite soccer players influence the betting odds of affected matches? Analyzing the first four statistical moments of 117,174 odds from 32 bookmakers, we identify initial inertia followed by a lagged reaction that we cannot reason with learning. Our findings raise questions about how bettors and bookmakers incorporate new information into their beliefs. It has broader implications regarding information processing in markets.<br><br>The present dataset contains 117,174 closing odds for 1,372 soccer matches in the German Bundesliga 1 and the Italian Serie A for the seasons 2019/2020 and 202/2021. It combines this information with match outcomes and COVID-19 infection information for the clubs participating in the two leagues. All infection data is based on publicly available information, e.g., announcements by the press, players, or their clubs.<br><br>
description: Betting odds of bookmakersSoccer clubs in the German Bundesliga 1 or the Italian Serie A in either one of the seasons 2019/2020 or 2020/2021 or both<br><br>.
DOI: 10.15468/dl.rqeuz4
title: Occurrence Download

description: A dataset containing 140 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-23.36389 -22.83452,93.36389 -22.83452,93.36389 23.83452,-23.36389 23.83452,-23.36389 -22.83452))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Zanthoxylum chalybeum Engl."
 ]
}

 The dataset includes 140 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010199-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pce6nw
title: Occurrence Download

description: A dataset containing 939 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Batrachyla leptopus Bell, 1843"
 ]
}

 The dataset includes 939 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010200-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.keezwr
title: Occurrence Download

description: A dataset containing 27804 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Calystegia silvatica (Kit.) Griseb."
 ]
}

 The dataset includes 27804 records from 339 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010197-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mgeu46
title: Occurrence Download

description: A dataset containing 163 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Zostera noltei seagrass meadows spatial distribution at LTER site Ria de Aveiro lagoon in 2014"
 ]
}

 The dataset includes 163 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010201-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e207770v4
title: ECIN Replication Package for "Pricing in Response to New Information: The Case of Betting Markets"

description: Markets are information aggregators. But how do they incorporate new data into their pricing? We examine the response of prediction markets to a novel information shock in a quasi-natural experiment: How did the absence announcements of elite soccer players influence the betting odds of affected matches? Analyzing the first four statistical moments of 117,174 odds from 32 bookmakers, we identify initial inertia followed by a lagged reaction that we cannot reason with learning. Our findings raise questions about how bettors and bookmakers incorporate new information into their beliefs. It has broader implications regarding information processing in markets.<br><br>The present dataset contains 117,174 closing odds for 1,372 soccer matches in the German Bundesliga 1 and the Italian Serie A for the seasons 2019/2020 and 202/2021. It combines this information with match outcomes and COVID-19 infection information for the clubs participating in the two leagues. All infection data is based on publicly available information, e.g., announcements by the press, players, or their clubs.<br><br>
description: Betting odds of bookmakersSoccer clubs in the German Bundesliga 1 or the Italian Serie A in either one of the seasons 2019/2020 or 2020/2021 or both<br><br>.
DOI: 10.15468/dl.ttjzux
title: Occurrence Download

description: A dataset containing 770089 species occurrences available in GBIF matching the query:
{
 "Country" : [
 "is Gabon"
 ]
}

 The dataset includes 770089 records from 731 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010192-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.539fr4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Penaeus japonicus",
 "Country is Morocco",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033075-250827131500795/datasets/export for details.
DOI: 10.15468/dl.f8xnyb
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Gilchristella aestuaria, Hilsa kelee, Laeviscutella dekimpei, Limnothrissa miodon, Limnothrissa stappersii, Poecilothrissa centralis, Microthrissa congica, Poecilothrissa congica, Microthrissa minuta, Microthrissa moeruensis, Poecilothrissa moeruensis, Microthrissa royauxi, Microthrissa whiteheadi, Nannothrissa parva, Nannothrissa stewarti, Odaxothrissa ansorgii, Odaxothrissa losera, Odaxothrissa mento, Odaxothrissa vittata, Pellonula leonensis, Pellonula vorax, Potamothrissa acutirostris, Potamothrissa obtusirostris, Potamothrissa whiteheadi, Sauvagella madagascariensis, Sauvagella robusta, Sierrathrissa leonensis, Spratellomorpha bianalis, Stolothrissa tanganicae, Thrattidion noctivagus, Denticeps clupeoides, Pellona ditchela, Cobitis maroccana, Acapoeta tanganicae, Alburnus alburnus, Barboides gracilis, Barbopsis devecchii, Caecobarbus geertsii, Carasobarbus fritschii, Carasobarbus harterti, Carasobarbus moulouyensis, Carassius auratus, Chelaethiops bibie, Chelaethiops congicus, Chelaethiops elongatus, Chelaethiops minutus, Chelaethiops rukwaensis, Cirrhinus cirrhosus, Clypeobarbus bellcrossi, Clypeobarbus bomokandi, Clypeobarbus congicus, Clypeobarbus hypsolepis, Clypeobarbus matthesi, Enteromius matthesi, Clypeobarbus pleuropholis, Clypeobarbus pseudognathodon, Clypeobarbus schoutedeni, Coptostomabarbus bellcrossi, Coptostomabarbus wittei, Ctenopharyngodon idella, Engraulicypris bredoi, Engraulicypris brevianalis, Engraulicypris gariepinus, Engraulicypris moeruensis, Neobola moeruensis, Engraulicypris ngalala, Engraulicypris sardella, Engraulicypris spinifer, Enteromius ablabes, Enteromius aboinensis, Enteromius afrohamiltoni, Enteromius afrovernayi, Enteromius aliciae, Enteromius aloyi, Enteromius amanpoae, Enteromius amatolicus, Enteromius amboseli, Enteromius apleurogramma, Enteromius anema, Enteromius annectens, Enteromius anniae, Enteromius anoplus, Enteromius ansorgii, Enteromius arambourgi, Labeobarbus arambourgi, Enteromius arcislongae, Enteromius argenteus, Enteromius aspilus, Enteromius atakorensis, Enteromius atkinsoni, Enteromius atromaculatus, Enteromius bagbwensis, Enteromius barnardi, Enteromius barotseensis, Enteromius baudoni, Enteromius bawkuensis, Enteromius bifrenatus, Enteromius bigornei, Enteromius boboi)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((-16.8319 22.8052,-16.8618 22.7792,-16.8618 20.0599,-7.8618 20.0599,-7.8618 29.0599,-11.3716 29.0599,-11.7891 28.737,-13.2712 28.3165,-13.6289 27.8814,-13.9674 27.0555,-14.8908 26.4573,-15.3388 24.986,-16.3247 24.0887,-16.8319 22.8052))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033076-250827131500795/datasets/export for details.
DOI: 10.5063/f1cc0z5g
title: Dangermond Preserve Cojo Terrace July 2022 RGB UAS Orthomosaic

description: Photomosaic derived from UAS imagery (1.6 cm x 1.6 cm) obtained on July 22nd and 23rd, 2022 by Madison Heffentrager and Ian Walker, UC Santa Barbara, Department of Geography. Post processing conducted in Agisoft Metashape Professional v.1.8.4. by Madison Heffentrager. Data collection funded by The Nature Conservancy at the Jack and Laura Dangermond Preserve.