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

description: A dataset containing 2675 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Prosopis laevigata (Humb. & Bonpl. ex Willd.) M.C.Johnst."
 ]
}

 The dataset includes 2675 records from 110 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014569-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vt8wr7
title: Occurrence Download

description: A dataset containing 590 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Month is one of (March, April, May, June, July, August, September, October, November, December)",
 "OccurrenceStatus is Present",
 "TaxonKey is Leucophaeus modestus (Tschudi, 1843)",
 "Year 2010-2010"
 ]
}

 The dataset includes 590 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014571-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hs62vv
title: Occurrence Download

description: A dataset containing 105 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Month is one of (February, January)",
 "OccurrenceStatus is Present",
 "TaxonKey is Leucophaeus modestus (Tschudi, 1843)",
 "Year 2011-2011"
 ]
}

 The dataset includes 105 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014572-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xn5j87
title: Occurrence Download

description: A dataset containing 1033 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Poa alopecurus (Gaudich. ex Mirb.) Kunth"
 ]
}

 The dataset includes 1033 records from 55 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014573-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.53qsxw
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Acropora prolifera (Lamarck, 1816)"
 ]
}

 The dataset includes 21 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014574-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rdhkk5
title: Occurrence Download

description: A dataset containing 1692 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Month is one of (March, April, May, June, July, August, September, October, November, December)",
 "OccurrenceStatus is Present",
 "TaxonKey is Leucophaeus modestus (Tschudi, 1843)",
 "Year 2015-2015"
 ]
}

 The dataset includes 1692 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014575-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w7k43n
title: Occurrence Download

description: A dataset containing 166 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Scutellaria nummulariifolia Hook.f."
 ]
}

 The dataset includes 166 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014576-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wxtqjk
title: Occurrence Download

description: A dataset containing 399 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Month is one of (January, February)",
 "OccurrenceStatus is Present",
 "TaxonKey is Leucophaeus modestus (Tschudi, 1843)",
 "Year 2016-2016"
 ]
}

 The dataset includes 399 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014577-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5d4syh
title: Occurrence Download

description: A dataset listing the 1164 species recorded in GBIF matching the query:
{
 "and" : [
 "GadmGid is MEX.4_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Fungi"
 ]
}

 The dataset's 1164 records were derived from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014552-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26180/25286407
title: The banksia plot: a method for visually comparing point estimates and confidence intervals across datasets

description: Companion data for the creation of a banksia plot:Background:In research evaluating statistical analysis methods, a common aim is to compare point estimates and confidence intervals (CIs) calculated from different analyses. This can be challenging when the outcomes (and their scale ranges) differ across datasets. We therefore developed a plot to facilitate pairwise comparisons of point estimates and confidence intervals from different statistical analyses both within and across datasets.Methods:The plot was developed and refined over the course of an empirical study. To compare results from a variety of different studies, a system of centring and scaling is used. Firstly, the point estimates from reference analyses are centred to zero, followed by scaling confidence intervals to span a range of one. The point estimates and confidence intervals from matching comparator analyses are then adjusted by the same amounts. This enables the relative positions of the point estimates and CI widths to be quickly assessed while maintaining the relative magnitudes of the difference in point estimates and confidence interval widths between the two analyses. Banksia plots can be graphed in a matrix, showing all pairwise comparisons of multiple analyses. In this paper, we show how to create a banksia plot and present two examples: the first relates to an empirical evaluation assessing the difference between various statistical methods across 190 interrupted time series (ITS) data sets with widely varying characteristics, while the second example assesses data extraction accuracy comparing results obtained from analysing original study data (43 ITS studies) with those obtained by four researchers from datasets digitally extracted from graphs from the accompanying manuscripts.Results:In the banksia plot of statistical method comparison, it was clear that there was no difference, on average, in point estimates and it was straightforward to ascertain which methods resulted in smaller, similar or larger confidence intervals than others. In the banksia plot comparing analyses from digitally extracted data to those from the original data it was clear that both the point estimates and confidence intervals were all very similar among data extractors and original data.Conclusions:The banksia plot, a graphical representation of centred and scaled confidence intervals, provides a concise summary of comparisons between multiple point estimates and associated CIs in a single graph. Through this visualisation, patterns and trends in the point estimates and confidence intervals can be easily identified.This collection of files allows the user to create the images used in the companion paper and amend this code to create their own banksia plots using either Stata version 17 or R version 4.3.1
DOI: 10.26180/25286407.v2
title: The banksia plot: a method for visually comparing point estimates and confidence intervals across datasets

description: Companion data for the creation of a banksia plot:Background:In research evaluating statistical analysis methods, a common aim is to compare point estimates and confidence intervals (CIs) calculated from different analyses. This can be challenging when the outcomes (and their scale ranges) differ across datasets. We therefore developed a plot to facilitate pairwise comparisons of point estimates and confidence intervals from different statistical analyses both within and across datasets.Methods:The plot was developed and refined over the course of an empirical study. To compare results from a variety of different studies, a system of centring and scaling is used. Firstly, the point estimates from reference analyses are centred to zero, followed by scaling confidence intervals to span a range of one. The point estimates and confidence intervals from matching comparator analyses are then adjusted by the same amounts. This enables the relative positions of the point estimates and CI widths to be quickly assessed while maintaining the relative magnitudes of the difference in point estimates and confidence interval widths between the two analyses. Banksia plots can be graphed in a matrix, showing all pairwise comparisons of multiple analyses. In this paper, we show how to create a banksia plot and present two examples: the first relates to an empirical evaluation assessing the difference between various statistical methods across 190 interrupted time series (ITS) data sets with widely varying characteristics, while the second example assesses data extraction accuracy comparing results obtained from analysing original study data (43 ITS studies) with those obtained by four researchers from datasets digitally extracted from graphs from the accompanying manuscripts.Results:In the banksia plot of statistical method comparison, it was clear that there was no difference, on average, in point estimates and it was straightforward to ascertain which methods resulted in smaller, similar or larger confidence intervals than others. In the banksia plot comparing analyses from digitally extracted data to those from the original data it was clear that both the point estimates and confidence intervals were all very similar among data extractors and original data.Conclusions:The banksia plot, a graphical representation of centred and scaled confidence intervals, provides a concise summary of comparisons between multiple point estimates and associated CIs in a single graph. Through this visualisation, patterns and trends in the point estimates and confidence intervals can be easily identified.This collection of files allows the user to create the images used in the companion paper and amend this code to create their own banksia plots using either Stata version 17 or R version 4.3.1
DOI: 10.15468/dl.cr2cbe
title: Occurrence Download

description: A dataset containing 65 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pristimantis scolodiscus (Lynch & Burrowes, 1990)"
 ]
}

 The dataset includes 65 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014578-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26180/25286407.v1
title: The banksia plot: a method for visually comparing point estimates and confidence intervals across datasets

description: Companion data for the creation of a banksia plot:Background:In research evaluating statistical analysis methods, a common aim is to compare point estimates and confidence intervals (CIs) calculated from different analyses. This can be challenging when the outcomes (and their scale ranges) differ across datasets. We therefore developed a plot to facilitate pairwise comparisons of point estimates and confidence intervals from different statistical analyses both within and across datasets.Methods:The plot was developed and refined over the course of an empirical study. To compare results from a variety of different studies, a system of centring and scaling is used. Firstly, the point estimates from reference analyses are centred to zero, followed by scaling confidence intervals to span a range of one. The point estimates and confidence intervals from matching comparator analyses are then adjusted by the same amounts. This enables the relative positions of the point estimates and CI widths to be quickly assessed while maintaining the relative magnitudes of the difference in point estimates and confidence interval widths between the two analyses. Banksia plots can be graphed in a matrix, showing all pairwise comparisons of multiple analyses. In this paper, we show how to create a banksia plot and present two examples: the first relates to an empirical evaluation assessing the difference between various statistical methods across 190 interrupted time series (ITS) data sets with widely varying characteristics, while the second example assesses data extraction accuracy comparing results obtained from analysing original study data (43 ITS studies) with those obtained by four researchers from datasets digitally extracted from graphs from the accompanying manuscripts.Results:In the banksia plot of statistical method comparison, it was clear that there was no difference, on average, in point estimates and it was straightforward to ascertain which methods resulted in smaller, similar or larger confidence intervals than others. In the banksia plot comparing analyses from digitally extracted data to those from the original data it was clear that both the point estimates and confidence intervals were all very similar among data extractors and original data.Conclusions:The banksia plot, a graphical representation of centred and scaled confidence intervals, provides a concise summary of comparisons between multiple point estimates and associated CIs in a single graph. Through this visualisation, patterns and trends in the point estimates and confidence intervals can be easily identified.This collection of files allows the user to create the images used in the companion paper and amend this code to create their own banksia plots using either Stata version 17 or R version 4.3.1
DOI: 10.15468/dl.cjf7e9
title: Occurrence Download

description: A dataset containing 172 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Senecio argyreus Phil."
 ]
}

 The dataset includes 172 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014579-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5440/1346198
title: NGEE Arctic Plant Traits: Soil Depth, Kougarok Road Mile Marker 64, Seward Peninsula, Alaska, Beginning 2016

description: Soil depth was measured at the Kougarok site located on the Seward Peninsula using a thaw depth probe inserted into the ground until the point of contact with frozen soil or rock. Measurements were made at vegetation plots, intensive stations, and reference points. Three to four replicate measurements were made during the observation period and the presence of rocks was noted when possible. This dataset includes one pdf file and one csv file.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
DOI: 10.15468/dl.7rujmw
title: Occurrence Download

description: A dataset containing 60 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Senecio laseguei Hombr. & Jacquinot ex DC."
 ]
}

 The dataset includes 60 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014580-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17038/es/1616250
title: Spreadsheets accompanying "Vehicle Technologies and Fuel Cell Technologies Office Research and Development Programs: Prospective Benefits Assessment for Medium- and Heavy-duty Vehicles"
DOI: 10.5440/1618325
title: Iron and Phosphorus Geochemistry in High-Centered and Low-Centered Polygon Soils from the Barrow Environmental Observatory, Utqiagvik, Alaska, 2015

description: This dataset provides chemical characterization of soil cores obtained in triplicate from center and trough positions of a high-centered polygon and center, ridge, and trough positions of a low-centered polygon in the NGEE Arctic research area within the Barrow Environmental Observatory, Utqiagvik, Alaska. Photographs of the 15 collected cores are provided. Cores were collected to thaw depth in early October 2015 and subsequently divided into subsamples for analysis, which included bulk soil horizons (organic or mineral) and finer scale depth increments (minimum 4 cm thickness per increment). Bulk horizons and depth increments were characterized using sequential chemical extractions for Fe and P and with Fe K-edge x-ray absorption spectroscopy. Additional soil parameters such as loss-on-ignition, gravimetric water content, and carbon and nitrogen concentrations were also quantified for bulk horizons. This dataset includes four csv files, one pdf user guide, and one zip folder of *.jpg photos.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
DOI: 10.5440/1728709
title: Geophysical and Environmental Monitoring Data, Lower Watershed, Teller Road Mile Marker 27, Seward Peninsula, Alaska, 2017-2019

description: This data set contains geophysical and environmental monitoring data acquired between September 2017 and 2019 at the lower Teller watershed, Seward Peninsula, Alaska. Geophysical data comprises processed resistivity data, acquired daily between Spring and Fall of 2018 and 2019, and a baseline measurement of Fall 2017. The environmental monitoring data comprises depth resolved, distributed soil moisture and soil temperature data. These measurements were obtained used Decagon 5TE sensors placed at 0.1 m, 0.2 m , 0.3 m, and 0.4 m depth. In addition, temperature data at 0.5 m, 1.0 m, and 1.5 m were acquired using Hobo temperature sensors. Data were collected to improve our understanding of the hydrological response of discontinuous permafrost systems, in particular focusing on multi-annual dynamics and short term disturbances, such as snowmelt or precipitation events. The electrical resistivity tomography (ERT) monitoring data are included as processed resistivity models, with model cells x dimension equal to the distance along the profile, and model z dimension being elevation. The start and end point of the transect are (UTM Zone 3N): E 454881.66 m, N 7178949.45 m, and E 454772.60, N 7178885.81. Locations of the soil moisture and temperature sensors are provided in the data package. This dataset is discussed in detail in the Uhlemann, S. et al 2021 paper listed in the references. This dataset includes one *.pdf user guide and 315 *.csv data files included within four zipped files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).