cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1594/pangaea.978452
title: Beach profile data for the Elwha River Delta, 2024-03-07

description: Shoreline topography data were collected using GNSS survey methods, with a differential GPS, typically an AshTech ProMark 200 RTK-DGPS system connected to the Washington State Reference Network, operating in Real Time Kinematic (RTK) mode. Survey data are referenced in the horizontal reference frame to NAD83(2011). Elevations, in meters, are referenced to NAVD88(Geoid09) for all survey data collected before December 2012, NAVD88(Geoid12) for all survey data collected between December 2012 and May 2020, and NAVD88(Geoid18) for all survey data collected subsequently. No conversions were applied to these data to account for variations in horizontal or vertical coordinate system adjustments through time, but an error analysis suggests a standard deviation for the elevations of between 0.03 and 0.05 m across the entire sampling period (2011-2024). All survey data were collected with the GNSS system mounted on a 2.05 m rover pole, held level by a surveyor as a transect line was traced in a cross-shore orientation on the beach. The associated text files include the horizontal (HRMS) and vertical (VRMS) root-mean-square errors estimated by the GNSS system, as well as the RTK-DGPS status reported by the GNSS system at the time each point was collected. Times are referenced to local Pacific time (either PST or PDT).
DOI: 10.1594/pangaea.978453
title: Beach profile data for the Elwha River Delta, 2024-05-14

description: Shoreline topography data were collected using GNSS survey methods, with a differential GPS, typically an AshTech ProMark 200 RTK-DGPS system connected to the Washington State Reference Network, operating in Real Time Kinematic (RTK) mode. Survey data are referenced in the horizontal reference frame to NAD83(2011). Elevations, in meters, are referenced to NAVD88(Geoid09) for all survey data collected before December 2012, NAVD88(Geoid12) for all survey data collected between December 2012 and May 2020, and NAVD88(Geoid18) for all survey data collected subsequently. No conversions were applied to these data to account for variations in horizontal or vertical coordinate system adjustments through time, but an error analysis suggests a standard deviation for the elevations of between 0.03 and 0.05 m across the entire sampling period (2011-2024). All survey data were collected with the GNSS system mounted on a 2.05 m rover pole, held level by a surveyor as a transect line was traced in a cross-shore orientation on the beach. The associated text files include the horizontal (HRMS) and vertical (VRMS) root-mean-square errors estimated by the GNSS system, as well as the RTK-DGPS status reported by the GNSS system at the time each point was collected. Times are referenced to local Pacific time (either PST or PDT).
DOI: 10.1594/pangaea.978454
title: Beach profile data for the Elwha River Delta, 2024-05-22

description: Shoreline topography data were collected using GNSS survey methods, with a differential GPS, typically an AshTech ProMark 200 RTK-DGPS system connected to the Washington State Reference Network, operating in Real Time Kinematic (RTK) mode. Survey data are referenced in the horizontal reference frame to NAD83(2011). Elevations, in meters, are referenced to NAVD88(Geoid09) for all survey data collected before December 2012, NAVD88(Geoid12) for all survey data collected between December 2012 and May 2020, and NAVD88(Geoid18) for all survey data collected subsequently. No conversions were applied to these data to account for variations in horizontal or vertical coordinate system adjustments through time, but an error analysis suggests a standard deviation for the elevations of between 0.03 and 0.05 m across the entire sampling period (2011-2024). All survey data were collected with the GNSS system mounted on a 2.05 m rover pole, held level by a surveyor as a transect line was traced in a cross-shore orientation on the beach. The associated text files include the horizontal (HRMS) and vertical (VRMS) root-mean-square errors estimated by the GNSS system, as well as the RTK-DGPS status reported by the GNSS system at the time each point was collected. Times are referenced to local Pacific time (either PST or PDT).
DOI: 10.1594/pangaea.978455
title: Beach profile data for the Elwha River Delta, 2024-05-23

description: Shoreline topography data were collected using GNSS survey methods, with a differential GPS, typically an AshTech ProMark 200 RTK-DGPS system connected to the Washington State Reference Network, operating in Real Time Kinematic (RTK) mode. Survey data are referenced in the horizontal reference frame to NAD83(2011). Elevations, in meters, are referenced to NAVD88(Geoid09) for all survey data collected before December 2012, NAVD88(Geoid12) for all survey data collected between December 2012 and May 2020, and NAVD88(Geoid18) for all survey data collected subsequently. No conversions were applied to these data to account for variations in horizontal or vertical coordinate system adjustments through time, but an error analysis suggests a standard deviation for the elevations of between 0.03 and 0.05 m across the entire sampling period (2011-2024). All survey data were collected with the GNSS system mounted on a 2.05 m rover pole, held level by a surveyor as a transect line was traced in a cross-shore orientation on the beach. The associated text files include the horizontal (HRMS) and vertical (VRMS) root-mean-square errors estimated by the GNSS system, as well as the RTK-DGPS status reported by the GNSS system at the time each point was collected. Times are referenced to local Pacific time (either PST or PDT).
DOI: 10.1594/pangaea.978456
title: Beach profile data for the Elwha River Delta, 2024-05-24

description: Shoreline topography data were collected using GNSS survey methods, with a differential GPS, typically an AshTech ProMark 200 RTK-DGPS system connected to the Washington State Reference Network, operating in Real Time Kinematic (RTK) mode. Survey data are referenced in the horizontal reference frame to NAD83(2011). Elevations, in meters, are referenced to NAVD88(Geoid09) for all survey data collected before December 2012, NAVD88(Geoid12) for all survey data collected between December 2012 and May 2020, and NAVD88(Geoid18) for all survey data collected subsequently. No conversions were applied to these data to account for variations in horizontal or vertical coordinate system adjustments through time, but an error analysis suggests a standard deviation for the elevations of between 0.03 and 0.05 m across the entire sampling period (2011-2024). All survey data were collected with the GNSS system mounted on a 2.05 m rover pole, held level by a surveyor as a transect line was traced in a cross-shore orientation on the beach. The associated text files include the horizontal (HRMS) and vertical (VRMS) root-mean-square errors estimated by the GNSS system, as well as the RTK-DGPS status reported by the GNSS system at the time each point was collected. Times are referenced to local Pacific time (either PST or PDT).
DOI: 10.1594/pangaea.978457
title: Beach profile data for the Elwha River Delta, 2024-08-19

description: Shoreline topography data were collected using GNSS survey methods, with a differential GPS, typically an AshTech ProMark 200 RTK-DGPS system connected to the Washington State Reference Network, operating in Real Time Kinematic (RTK) mode. Survey data are referenced in the horizontal reference frame to NAD83(2011). Elevations, in meters, are referenced to NAVD88(Geoid09) for all survey data collected before December 2012, NAVD88(Geoid12) for all survey data collected between December 2012 and May 2020, and NAVD88(Geoid18) for all survey data collected subsequently. No conversions were applied to these data to account for variations in horizontal or vertical coordinate system adjustments through time, but an error analysis suggests a standard deviation for the elevations of between 0.03 and 0.05 m across the entire sampling period (2011-2024). All survey data were collected with the GNSS system mounted on a 2.05 m rover pole, held level by a surveyor as a transect line was traced in a cross-shore orientation on the beach. The associated text files include the horizontal (HRMS) and vertical (VRMS) root-mean-square errors estimated by the GNSS system, as well as the RTK-DGPS status reported by the GNSS system at the time each point was collected. Times are referenced to local Pacific time (either PST or PDT).
DOI: 10.1594/pangaea.978458
title: Beach profile data for the Elwha River Delta, 2024-08-20

description: Shoreline topography data were collected using GNSS survey methods, with a differential GPS, typically an AshTech ProMark 200 RTK-DGPS system connected to the Washington State Reference Network, operating in Real Time Kinematic (RTK) mode. Survey data are referenced in the horizontal reference frame to NAD83(2011). Elevations, in meters, are referenced to NAVD88(Geoid09) for all survey data collected before December 2012, NAVD88(Geoid12) for all survey data collected between December 2012 and May 2020, and NAVD88(Geoid18) for all survey data collected subsequently. No conversions were applied to these data to account for variations in horizontal or vertical coordinate system adjustments through time, but an error analysis suggests a standard deviation for the elevations of between 0.03 and 0.05 m across the entire sampling period (2011-2024). All survey data were collected with the GNSS system mounted on a 2.05 m rover pole, held level by a surveyor as a transect line was traced in a cross-shore orientation on the beach. The associated text files include the horizontal (HRMS) and vertical (VRMS) root-mean-square errors estimated by the GNSS system, as well as the RTK-DGPS status reported by the GNSS system at the time each point was collected. Times are referenced to local Pacific time (either PST or PDT).
DOI: 10.1594/pangaea.978459
title: Beach profile data for the Elwha River Delta, 2024-11-13

description: Shoreline topography data were collected using GNSS survey methods, with a differential GPS, typically an AshTech ProMark 200 RTK-DGPS system connected to the Washington State Reference Network, operating in Real Time Kinematic (RTK) mode. Survey data are referenced in the horizontal reference frame to NAD83(2011). Elevations, in meters, are referenced to NAVD88(Geoid09) for all survey data collected before December 2012, NAVD88(Geoid12) for all survey data collected between December 2012 and May 2020, and NAVD88(Geoid18) for all survey data collected subsequently. No conversions were applied to these data to account for variations in horizontal or vertical coordinate system adjustments through time, but an error analysis suggests a standard deviation for the elevations of between 0.03 and 0.05 m across the entire sampling period (2011-2024). All survey data were collected with the GNSS system mounted on a 2.05 m rover pole, held level by a surveyor as a transect line was traced in a cross-shore orientation on the beach. The associated text files include the horizontal (HRMS) and vertical (VRMS) root-mean-square errors estimated by the GNSS system, as well as the RTK-DGPS status reported by the GNSS system at the time each point was collected. Times are referenced to local Pacific time (either PST or PDT).
DOI: 10.1594/pangaea.978460
title: Beach profile data for the Elwha River Delta, 2024-11-14

description: Shoreline topography data were collected using GNSS survey methods, with a differential GPS, typically an AshTech ProMark 200 RTK-DGPS system connected to the Washington State Reference Network, operating in Real Time Kinematic (RTK) mode. Survey data are referenced in the horizontal reference frame to NAD83(2011). Elevations, in meters, are referenced to NAVD88(Geoid09) for all survey data collected before December 2012, NAVD88(Geoid12) for all survey data collected between December 2012 and May 2020, and NAVD88(Geoid18) for all survey data collected subsequently. No conversions were applied to these data to account for variations in horizontal or vertical coordinate system adjustments through time, but an error analysis suggests a standard deviation for the elevations of between 0.03 and 0.05 m across the entire sampling period (2011-2024). All survey data were collected with the GNSS system mounted on a 2.05 m rover pole, held level by a surveyor as a transect line was traced in a cross-shore orientation on the beach. The associated text files include the horizontal (HRMS) and vertical (VRMS) root-mean-square errors estimated by the GNSS system, as well as the RTK-DGPS status reported by the GNSS system at the time each point was collected. Times are referenced to local Pacific time (either PST or PDT).
DOI: 10.1594/pangaea.979826
title: Hyperspectral core imaging (HSI) of sediment core LVC18_3G from Lake Victoria, Eastern Africa
DOI: 10.1594/pangaea.979827
title: Hyperspectral core imaging (HSI) of sediment core LVC18_3H from Lake Victoria, Eastern Africa
DOI: 10.1594/pangaea.979829
title: Hyperspectral core imaging (HSI) of sediment core LVC18_3K from Lake Victoria, Eastern Africa
DOI: 10.1594/pangaea.979831
title: Hyperspectral core imaging (HSI) of sediment core LVC18_S1 from Lake Victoria, Eastern Africa
DOI: 10.21227/ahhn-fa69
title: "DNA-Inspired Time Series Encoding: A Glimpse Into The Next 4-Hour Timeframe"

description: "In this work, we introduce a bio-inspired encoding framework for forecasting the direction of financial time series. Motivated by the limitations of linear models and the opacity of many deep learning approaches, we draw an analogy to genetics: observable micro-patterns are encoded into symbolic \"Financial DNA\" sequences. These sequences are then analyzed using a probabilistic state-transition mechanism to estimate the likelihood of subsequent market directions. We evaluate the approach on Bitcoin hourly OHLCV data with a rolling backtest. Among the horizons considered, modeling transitions from current Financial DNA patterns to the 4-hour-ahead price direction yields the strongest results, achieving a win ratio of 0.729. The findings suggest that compact, interpretable symbolic representations can capture salient, recurring structures in noisy, non-stationary markets and support effective directional forecasts."
DOI: 10.15468/dl.kpkzrh
title: Occurrence Download

description: A dataset containing 54479 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Xylocopa violacea",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 54479 records from 798 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003390-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mx4epv
title: Occurrence Download

description: A dataset containing 196 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "TaxonKey is one of (Anthophila [GBIF Backbone Taxonomy], Apis mellifera [GBIF Backbone Taxonomy])",
 "StateProvince is one of (Santander)",
 "GadmGid is one of (COL.28.36_2, COL.28.33_2, COL.28.9_2, COL.28.61_2)"
 ]
 }
 ]
}

 The dataset includes 196 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003405-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30686257
title: Ethics of algorithmic invasiveness in beauty apps: an online experimental survey of public perspectives

description: The rise of beauty apps with artificial intelligence (AI) capabilities has transformed how users perceive, evaluate, and engage with their and others’ appearance. While such apps offer entertainment, concerns persist about their ethical implications and psychological effects. In this study, we focus on the concept of algorithmic invasiveness – defined as the tendency of AI algorithms to increasingly influence and integrate into our daily lives. Using a conjoint survey experiment, we empirically assess public attitudes and social acceptability of beauty app functionalities with varying degrees of invasiveness, from removing blemishes to collecting medical data. Results show that functionalities with high degree of algorithmic invasiveness, such as AI-generated attractiveness rankings, are viewed as significantly less socially acceptable than less invasive functionalities. However, acceptability depends on context: functionalities tied to commercial applications, like personalised product recommendations, are often deemed less problematic despite similar data-use practices. User demographics and attitudes further shape perceptions. Older adults display greater acceptance of highly invasive functionalities compared to younger users. Participants with higher self-objectification or positive views of AI show increased tolerance of invasive functionalities. These findings highlight how both technological design and user predisposition influence both ethical attitudes and preferences in beauty-related technologies. This study showcases how we can elicit moral intuitions about new technologies by exploring how the public form preferences around specific functionalities. Moreover, it provides novel insight about how individuals make normative distinction between degrees of invasiveness, contributing to broader discourse about responsible AI design and digital well-being.
DOI: 10.6084/m9.figshare.30686257.v1
title: Ethics of algorithmic invasiveness in beauty apps: an online experimental survey of public perspectives

description: The rise of beauty apps with artificial intelligence (AI) capabilities has transformed how users perceive, evaluate, and engage with their and others’ appearance. While such apps offer entertainment, concerns persist about their ethical implications and psychological effects. In this study, we focus on the concept of algorithmic invasiveness – defined as the tendency of AI algorithms to increasingly influence and integrate into our daily lives. Using a conjoint survey experiment, we empirically assess public attitudes and social acceptability of beauty app functionalities with varying degrees of invasiveness, from removing blemishes to collecting medical data. Results show that functionalities with high degree of algorithmic invasiveness, such as AI-generated attractiveness rankings, are viewed as significantly less socially acceptable than less invasive functionalities. However, acceptability depends on context: functionalities tied to commercial applications, like personalised product recommendations, are often deemed less problematic despite similar data-use practices. User demographics and attitudes further shape perceptions. Older adults display greater acceptance of highly invasive functionalities compared to younger users. Participants with higher self-objectification or positive views of AI show increased tolerance of invasive functionalities. These findings highlight how both technological design and user predisposition influence both ethical attitudes and preferences in beauty-related technologies. This study showcases how we can elicit moral intuitions about new technologies by exploring how the public form preferences around specific functionalities. Moreover, it provides novel insight about how individuals make normative distinction between degrees of invasiveness, contributing to broader discourse about responsible AI design and digital well-being.
DOI: 10.7910/dvn/d98bn1
title: Replication Data for: The Impact of Household Structure on Fertility: A Study in Vietnam

description: This dataset is used in the research titled "The Impact of Household Structure on Fertility: A Study in Vietnam".
DOI: 10.15468/dl.h7p2st
title: Occurrence Download

description: A dataset containing 1644 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ceratina chalybea",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1644 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003406-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5tf5kk
title: Occurrence Download

description: A dataset containing 194 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Helianthemum songaricum"
 ]
}

 The dataset includes 194 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003407-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4j5sk4
title: Occurrence Download

description: A dataset containing 49 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aloe polyphylla"
 ]
}

 The dataset includes 49 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003408-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dt8t9b
title: Occurrence Download

description: A dataset containing 2694 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Netherlands",
 "Geometry POLYGON((4.90793 53.45006,4.46616 52.91076,4.43344 52.42458,3.66444 51.85219,3.23904 51.45631,3.23904 51.16995,4.08984 51.13916,4.48252 51.36446,5.10427 51.20072,5.67693 51.09808,5.52967 50.83016,5.7751 50.67489,6.10233 50.69562,6.29867 51.24171,6.28231 51.57848,6.08597 51.78139,6.69135 51.76114,7.03494 52.11419,7.14948 52.51428,7.26401 52.96007,7.29673 53.29387,6.8386 53.54738,6.31503 53.59596,4.90793 53.45006))",
 "HasGeospatialIssue is false",
 "TaxonKey is Mustela nivalis",
 "Year 2023-2025"
 ]
}

 The dataset includes 2694 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003409-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30686234
title: Patent data

description: Initial patent information
DOI: 10.6084/m9.figshare.30686234.v1
title: Patent data

description: Initial patent information
DOI: 10.15468/dl.urn899
title: Occurrence Download

description: A dataset containing 142 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Chelostoma foveolatum",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 142 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003410-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17894/ucph.28a99c9f-a2b6-4903-acbb-8a9e609a892a
title: Dataset 1
DOI: 10.15468/dl.ms9y4c
title: Occurrence Download

description: A dataset containing 205 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hoplitis acuticornis",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 205 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003411-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30686285
title: Materials

description: Supplementary materials on the institutional background and the construction of selected indicators