cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18739/a27940v7j
title: Bering Strait Conductivity, Temperature, and Depth (CTD) Data 2015

description: This Arctic Observing Network (AON) project will support a comprehensive observationally-based measurement scheme for the Bering Strait, which will combine in situ observations with traditional knowledge from the Bering Strait communities and results from a 4 kilometer (km) resolution ice-ocean model, ECCO2 (Estimating the Circulation and Climate of the Ocean phase II). Temperature, salinity and velocity data from the Bering Strait and data products of oceanic fluxes will be measured to meet the needs of numerous scientific, climate and industrial (e.g., oil/gas exploration, shipping) studies in the region.
 This is an archive of Conductivity, Temperature, and Depth (CTD) data from the 2015 Bering Strait mooring cruise.
 The cruise was funded by the National Science Foundation (NSF)-Arctic Observing Network grant:
 - Division of Polar Programs (PLR)-1304052 (Principal Investigators (PIs): Woodgate, Heimbach and Nguyen)
 The data are from a ~ 9 day cruise
 - on the US vessel Norseman II
 - from 1st-9th July 2015
 - sailing from and to Nome, Alaska, United States of America (USA)
 - to the Bering Strait and southern Chukchi Sea.
 The cruise was also to service oceanographic moorings in the strait and those data are archived separately.
 A total of 258 casts were taken and are included in this archive.
 No water samples were taken during this cruise.
 Stations were taken in US waters near mooring locations and on sections in the Bering Strait and southern Chukchi Sea. See cruise report for map, station locations, calibration, and processing details.
 The data are taken using an SBE911p system, recording
 - pressure
 - temperature (dual system)
 - conductivity (dual system)
 - oxygen (dual system)
 - fluorescence
 - turbidity.
 For an overview of previous and on-going Bering Strait mooring work, please see http://psc.apl.washington.edu/BeringStrait.html.
 For details of the measurements and their interpretation, please see: Woodgate, R.A., K.M.Stafford and F.G.Prahl (2015) A synthesis of year-round interdisciplinary mooring measurements in the Bering Strait (1990-2014) and the Russian-American Long-term Census of the Arctic (RUSALCA) years (2004-2011), Oceanography 28(3):46-67, doi:10.5670/oceanog.2015.57
 Please cite that reference for the data.
 For queries, please contact:
 Rebecca Woodgate woodgate@uw.edu (206) 221-3268
 Polar Science Center, Applied Physics Lab, University of Washington
 1013 NE 40th, Seattle, Washington (WA) 98105-6698 USA
DOI: 10.18739/a23j3923t
title: Bering Strait Conductivity, Temperature, and Depth (CTD) Data 2016

description: This Arctic Observing Network (AON) project will support a comprehensive observationally-based measurement scheme for the Bering Strait, which will combine in situ observations with traditional knowledge from the Bering Strait communities and results from a 4 kilometer (km) resolution ice-ocean model, ECCO2 (Estimating the Circulation and Climate of the Ocean phase II). Temperature, salinity and velocity data from the Bering Strait and data products of oceanic fluxes will be measured to meet the needs of numerous scientific, climate and industrial (e.g., oil/gas exploration, shipping) studies in the region.
 This is an archive of Conductivity, Temperature, and Depth (CTD) data from the 2016 Bering Strait mooring cruise.
 The cruise was funded by the National Science Foundation (NSF)-Arctic Observing Network grants:
 - Division of Polar Programs (PLR)-1304052 (Principal Investigators (PIs): Woodgate, Heimbach and Nguyen)
 The data are from a ~ 9 day cruise
 - on the US vessel Norseman II
 - from 7th-15th July 2016
 - sailing from and to Nome, Alaska, United States of America (USA)
 - to the Bering Strait and southern Chukchi Sea.
 The cruise was also to service oceanographic moorings in the strait and those data are archived separately.
 A total of 277 casts were taken and are included in this archive.
 No water samples were taken during this cruise.
 Stations were taken in United States (US) waters near mooring locations and on sections in the Bering Strait and southern Chukchi Sea. See cruise report for map, station locations, calibration, and processing details.
 The data are taken using an SBE911p system, recording
 - pressure
 - temperature (dual system)
 - conductivity (dual system)
 - oxygen (dual system)
 - fluorescence
 - turbidity.
 For an overview of previous and on-going Bering Strait mooring work, please see http://psc.apl.washington.edu/BeringStrait.html.
 For details of the measurements and their interpretation, please see: Woodgate, R.A., K.M.Stafford and F.G.Prahl (2015) A synthesis of year-round interdisciplinary mooring measurements in the Bering Strait (1990-2014) and the Russian-American Long-term Census of the Arctic (RUSALCA) years (2004-2011), Oceanography 28(3):46-67, doi:10.5670/oceanog.2015.57
 Please cite that reference for the data.
 For queries, please contact:
 Rebecca Woodgate woodgate@uw.edu (206) 221-3268
 Polar Science Center, Applied Physics Lab, University of Washington
 1013 NE 40th, Seattle, Washington (WA) 98105-6698 USA
DOI: 10.18739/a2zs2kd8t
title: Bering Strait Conductivity, Temperature, and Depth (CTD) Data 2017

description: This Arctic Observing Network (AON) project will support a comprehensive observationally-based measurement scheme for the Bering Strait, which will combine in situ observations with traditional knowledge from the Bering Strait communities and results from a 4 kilometer (km) resolution ice-ocean model, ECCO2 (Estimating the Circulation and Climate of the Ocean phase II). Temperature, salinity and velocity data from the Bering Strait and data products of oceanic fluxes will be measured to meet the needs of numerous scientific, climate and industrial (e.g., oil/gas exploration, shipping) studies in the region.
 This is an archive of Conductivity, Temperature, and Depth (CTD) data from the 2017 Bering Strait mooring cruise.
 The cruise was funded by the National Science Foundation (NSF)-Arctic Observing Network grants:
 - Division of Polar Programs (PLR)-1304052 (Principal Investigators (PIs): Woodgate, Heimbach and Nguyen)
 The data are from a ~ 9 day cruise
 - on the US vessel Norseman II
 - from 7th-15th July 2017
 - sailing from and to Nome, Alaska, United States of America (USA)
 - to the Bering Strait and southern Chukchi Sea.
 The cruise was also to service oceanographic moorings in the strait and those
 data are archived separately.
 A total of 342 casts were taken and are included in this archive.
 No water samples were taken during this cruise.
 Stations were taken in United States (US) waters near mooring locations and on sections
 in the Bering Strait and southern Chukchi Sea. See cruise report for
 map, station locations, calibration, and processing details.
 The data are taken using an SBE911p system, recording
 - pressure
 - temperature (dual system)
 - conductivity (dual system)
 - oxygen (dual system)
 - fluorescence
 - turbidity.
 For an overview of previous and on-going Bering Strait mooring work,
 please see http://psc.apl.washington.edu/BeringStrait.html.
 For details of the measurements and their interpretation, please see:
 Woodgate, R.A., K.M.Stafford and F.G.Prahl (2015) A synthesis of
 year-round interdisciplinary mooring measurements in the Bering
 Strait (1990-2014) and the Russian-American Long-term Census of the Arctic (RUSALCA) years (2004-2011), Oceanography
 28(3):46-67, doi:10.5670/oceanog.2015.57
 Please cite that reference for the data.
 For queries, please contact:
 Rebecca Woodgate woodgate@uw.edu (206) 221-3268
 Polar Science Center, Applied Physics Lab, University of Washington
 1013 NE 40th, Seattle, Washington (WA) 98105-6698 USA
DOI: 10.18739/a22v2cb3n
title: Ecosystem scale implication of soil carbon dioxide (CO2) concentration dynamics during soil freezing in Alaskan Arctic tundra ecosystems 2005-2007

description: The rates, processes, and controls on Arctic cold period soil carbon loss are 
still poorly understood. To understand one component of winter CO2 loss to the atmosphere, continuous 
measurements of soil [CO2] (concentration of CO2) were made and compared to ecosystem scale CO2 fluxes. Measurements of soil [CO2] 
were made near Utqiaġvik, Alaska from the beginning of soil thaw in summer 2005 until spring 2007. In the 
summer, soil [CO2] rose with increased soil temperature, reaching values orders of magnitude higher than 
atmospheric [CO2]. Soil [CO2] initially decreased at the end of summer and beginning of fall but then 
increased subsequent to soil freezing. Due to complex changes in biological activity, storage, and 
transport processes, soil [CO2] was then approximately double that observed in the summer. After reaching 
peak concentrations in November, soil [CO2] steeply decreased over a couple of weeks, suggesting a substantial 
release of CO2 into the atmosphere and movement within the soil column. Eddy covariance measurements showed 
variable but continued emissions of CO2 to the atmosphere during freeze-up. The disconnect between soil 
[CO2] and landscape level fluxes may be attributed to the spatio-temporal heterogeneity in releases of high 
concentrations of soil [CO2] to the atmosphere during the fall; and when integrated over the area of the eddy 
covariance tower footprint, do not frequently result in detectable emission events. Continued monitoring of 
fall and winter soil [CO2] and ecosystem fluxes will be vital to further understanding the variability of 
inter-annual Arctic CO2 emissions.
DOI: 10.5281/zenodo.14051520
title: Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process - Datasets

description: This dataset contains supporting data for a research project aimed at analysing herbarium samples from the New England area at a large scale with deep learning techniques. Details on the methodology are shared in the acompanying paper (to be published).

Content:



dataset600k_withAI.csv : A dataset of over 600.000 herbarium samples with its record metadata and a corresponding AI phenological annotations with matching confidence scores. The entirety of the record headers are provided, extracted directly from the NEVP portal. In addition, the AI labels are defined by the following headers. These 8 columns represent 4 binary classifiers with the Presence/Absence of each 4 traits and corresponding confidence (as a percentage - presence/absence percentages sum to 1).








Flowering
Not Flowering
Budding
Not Budding
Fruiting
Not Fruiting
Reproductive
Not Reproductive










data_species_with_statuses.csv: A processed dataset summarizing flowering period shift at a species level. Two types of headers are provided.



First metadata concerning the flowering shift and the data used to compute that value: 








genus
genus_species
slope
nb_specimens
p_value_significance
trend_category


Genus of the species
Binomial name of the species
Regression slope defining the flowering shift as a slope
Number of herbarium specimens used to compute the shift
P-value significance of the slope being non-zero. ('Non Significant'/'Significant')
Summary of the shift as a binary characteristic ('Earlier'/'Later')







Second, metadata summarizing various traits associated to each species:








lifeform_status
native_introduced_status
wetland_status
seasonality_average
seasonality_spread


Growth form from the USDA PLANTS Database. 'Forb_Herb', 'Shrub_Tree' or 'Vine'
'Native'/'Introduced' status from the USDA PLANTS Database.


National Wetland Plant List (NWPL) Wetland Indicator Status within the Northcentral and Northeast Region

'OBL'/'FACW'/'FAC'/'FACU'/'UPL'

A characteristic of the flowering season of the species based on the mean Day of Year of the analysed specimens: if <=180: 'Early', else 'Late'
A characteristic of the flowering season of the species based on the spread of the flowering season. Less than 28 days: 'Narrow', larger: 'Large'.




 






phylogenetic_tree.tre: The raw data used to generate the visualization of the flowering seasonality character and the detected flowering shift foreach species on a phylogenetic tree.

phylogenetic_processed_dataset.csv: The processed dataset resuting from the phylogenetic signal analysis. For each trait, an associated significance binary value is provided.
DOI: 10.5281/zenodo.14051521
title: Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process - Datasets

description: This dataset contains supporting data for a research project aimed at analysing herbarium samples from the New England area at a large scale with deep learning techniques. Details on the methodology are shared in the acompanying paper (to be published).

Content:



dataset600k_withAI.csv : A dataset of over 600.000 herbarium samples with its record metadata and a corresponding AI phenological annotations with matching confidence scores. The entirety of the record headers are provided, extracted directly from the NEVP portal. In addition, the AI labels are defined by the following headers. These 8 columns represent 4 binary classifiers with the Presence/Absence of each 4 traits and corresponding confidence (as a percentage - presence/absence percentages sum to 1).








Flowering
Not Flowering
Budding
Not Budding
Fruiting
Not Fruiting
Reproductive
Not Reproductive










data_species_with_statuses.csv: A processed dataset summarizing flowering period shift at a species level. Two types of headers are provided.



First metadata concerning the flowering shift and the data used to compute that value: 








genus
genus_species
slope
nb_specimens
p_value_significance
trend_category


Genus of the species
Binomial name of the species
Regression slope defining the flowering shift as a slope
Number of herbarium specimens used to compute the shift
P-value significance of the slope being non-zero. ('Non Significant'/'Significant')
Summary of the shift as a binary characteristic ('Earlier'/'Later')







Second, metadata summarizing various traits associated to each species:








lifeform_status
native_introduced_status
wetland_status
seasonality_average
seasonality_spread


Growth form from the USDA PLANTS Database. 'Forb_Herb', 'Shrub_Tree' or 'Vine'
'Native'/'Introduced' status from the USDA PLANTS Database.


National Wetland Plant List (NWPL) Wetland Indicator Status within the Northcentral and Northeast Region

'OBL'/'FACW'/'FAC'/'FACU'/'UPL'

A characteristic of the flowering season of the species based on the mean Day of Year of the analysed specimens: if <=180: 'Early', else 'Late'
A characteristic of the flowering season of the species based on the spread of the flowering season. Less than 28 days: 'Narrow', larger: 'Large'.
DOI: 10.5281/zenodo.14056635
title: Improving the accuracy of automated labeling of specimen images datasets via a confidence-based process - Datasets

description: This dataset contains supporting data for a research project aimed at analysing herbarium samples from the New England area at a large scale with deep learning techniques. Details on the methodology are shared in the acompanying paper (to be published).

Content:



dataset600k_withAI.csv : A dataset of over 600.000 herbarium samples with its record metadata and a corresponding AI phenological annotations with matching confidence scores. The entirety of the record headers are provided, extracted directly from the NEVP portal. In addition, the AI labels are defined by the following headers. These 8 columns represent 4 binary classifiers with the Presence/Absence of each 4 traits and corresponding confidence (as a percentage - presence/absence percentages sum to 1).








Flowering
Not Flowering
Budding
Not Budding
Fruiting
Not Fruiting
Reproductive
Not Reproductive










data_species_with_statuses.csv: A processed dataset summarizing flowering period shift at a species level. Two types of headers are provided.



First metadata concerning the flowering shift and the data used to compute that value: 








genus
genus_species
slope
nb_specimens
p_value_significance
trend_category


Genus of the species
Binomial name of the species
Regression slope defining the flowering shift as a slope
Number of herbarium specimens used to compute the shift
P-value significance of the slope being non-zero. ('Non Significant'/'Significant')
Summary of the shift as a binary characteristic ('Earlier'/'Later')







Second, metadata summarizing various traits associated to each species:








lifeform_status
native_introduced_status
wetland_status
seasonality_average
seasonality_spread


Growth form from the USDA PLANTS Database. 'Forb_Herb', 'Shrub_Tree' or 'Vine'
'Native'/'Introduced' status from the USDA PLANTS Database.


National Wetland Plant List (NWPL) Wetland Indicator Status within the Northcentral and Northeast Region

'OBL'/'FACW'/'FAC'/'FACU'/'UPL'

A characteristic of the flowering season of the species based on the mean Day of Year of the analysed specimens: if <=180: 'Early', else 'Late'
A characteristic of the flowering season of the species based on the spread of the flowering season. Less than 28 days: 'Narrow', larger: 'Large'.




 






phylogenetic_tree.tre: The raw data used to generate the visualization of the flowering seasonality character and the detected flowering shift foreach species on a phylogenetic tree.

phylogenetic_processed_dataset.csv: The processed dataset resuting from the phylogenetic signal analysis. For each trait, an associated significance binary value is provided.
DOI: 10.15468/dl.4vqncj
title: Occurrence Download

description: A dataset containing 590 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "TaxonKey is Synsepalum dulcificum (Schumach. & Thonn.) Daniell"
 ]
}

 The dataset includes 590 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027848-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18739/a2v11vm5g
title: Holocene landscape change at the northern Manitoba tundra-forest border 2003-2009

description: The purpose of this study was to understand how location within a landscape influences the response of aquatic and terrestrial ecosystems to climate change (with particular focus on how lake ecosystems respond to peatland development) at the tundra-forest border of northern Manitoba. In 2008-2009 we sampled water and surface sediment from 44 lakes. We collected sediment cores (3-4 meters in length) from 8 of these lakes. These cores represent 4000 - 8000 years of continuous sediment deposition. The data suggest a strong link between lake water chemistry and peatland in the modern landscape, a relatively stable mix of forest and tundra over the past 7500 years and a heterogeneous expansion of peatland beginning ~4500 years Before Present (BP).
 This dataset includes modern lake chemistry, diatom assemblages, and morphometry and land cover data for 44 lakes sampled primarily in 2009. Proxy data are also provided for sediment cores collected from 8 lakes. Data for the cores include Lead 210 and 14-Carbon dates, loss-on-ignition, grain size, carbon and nitrogen (concentrations and isotopes), extractable elements (Calcium, Magnesium, Iron, Potassium, Phosphorous), biogenic silica, X- Ray Fluorescence (XRF), magnetics (susceptibility, Anhysteretic Remanent Magnetization (ARM), Isothermal Remanent Magnetization (IRM)), charcoal, pollen and spores, and diatoms.
DOI: 10.18739/a2kk94d2q
title: Beaufort Gyre Observing System (BGOS) Underway pCO2 data and air-sea CO2 fluxes, Arctic Ocean, 2011-2019

description: These data were collected on the CCGS (Canadian Coast Guard) Louis St. Laurent during BGOS (Beaufort Gyre Observing System) research cruises in 2012, 2013, 2014, 2016, 2017, 2019 in the Beaufort Sea area. They are underway pCO2
 (Partial pressure of CO2) data collected using an equilibrator-infrared method (SUPER CO2 system from Sunburst Sensors). Ancillary data for calculation of air-sea CO
 fluxes include temperature, salinity, atmospheric CO2, wind speed, and gas transfer velocity (calculated from Wanninkhof et al. (2009). Fluxes are not corrected for fractional ice-coverage.
 The specific goal of the study is to continue to operate an Arctic Observing Network (AON) for the measurement of the partial pressure of CO2 (pCO2), pH, and dissolved O2 (DO) focused on the surface waters of the Arctic Ocean (specifically, the Canada Basin). These data were collected on the CCGS (Canadian Coast Guard) Louis St. Laurent during a BGOS (Beaufort Gyre Observing System) research cruise in 2019 in the Beaufort Sea area. It is underway pCO2 (partial pressure of carbon dioxide) data collected using an equilibrator-infrared method (SUPER CO2 system from Sunburst Sensors). Ancillary data for calculation of air-sea CO2 fluxes include temperature, salinity, atmospheric CO2.
DOI: 10.18739/a2ft8dk7g
title: Beaufort Gyre Observing System (BGOS) Underway pCO2 data and air-sea CO2 fluxes, Arctic Ocean, 2011-2019

description: These data were collected on the CCGS (Canadian Coast Guard) Louis St. Laurent during BGOS (Beaufort Gyre Observing System) research cruises in 2012, 2013, 2014, 2016, 2017, 2019 in the Beaufort Sea area. They are underway pCO2
 (Partial pressure of CO2) data collected using an equilibrator-infrared method (SUPER CO2 system from Sunburst Sensors). Ancillary data for calculation of air-sea CO
 fluxes include temperature, salinity, atmospheric CO2, wind speed, and gas transfer velocity (calculated from Wanninkhof et al. (2009). Fluxes are not corrected for fractional ice-coverage.
 The specific goal of the study is to continue to operate an Arctic Observing Network (AON) for the measurement of the partial pressure of CO2 (pCO2), pH, and dissolved O2 (DO) focused on the surface waters of the Arctic Ocean (specifically, the Canada Basin). These data were collected on the CCGS (Canadian Coast Guard) Louis St. Laurent during a BGOS (Beaufort Gyre Observing System) research cruise in 2019 in the Beaufort Sea area. It is underway pCO2 (partial pressure of carbon dioxide) data collected using an equilibrator-infrared method (SUPER CO2 system from Sunburst Sensors). Ancillary data for calculation of air-sea CO2 fluxes include temperature, salinity, atmospheric CO2.
DOI: 10.18739/a2b27ps25
title: Beaufort Gyre Observing System (BGOS) Underway Partial pressure of carbon dioxide (pCO2) data and air-sea CO2 fluxes, Arctic Ocean, 2011-2019

description: These data were collected on the CCGS (Canadian Coast Guard) Louis St. Laurent during BGOS (Beaufort Gyre Observing System) research cruises in 2012, 2013, 2014, 2016, 2017, 2019 in the Beaufort Sea area. They are underway pCO2 (Partial pressure of carbon dioxide) data collected using an equilibrator-infrared method (SUPER CO2 system from Sunburst Sensors). Ancillary data for calculation of air-sea CO fluxes include temperature, salinity, atmospheric CO2, wind speed, and gas transfer velocity (calculated from Wanninkhof et al. (2009). Fluxes are not corrected for fractional ice-coverage.
 The specific goal of the study is to continue to operate an Arctic Observing Network (AON) for the measurement of the partial pressure of CO2 (pCO2), pH, and dissolved O2 (DO) focused on the surface waters of the Arctic Ocean (specifically, the Canada Basin). These data were collected on the CCGS (Canadian Coast Guard) Louis St. Laurent during a BGOS (Beaufort Gyre Observing System) research cruise in 2019 in the Beaufort Sea area. It is underway pCO2 (partial pressure of carbon dioxide) data collected using an equilibrator-infrared method (SUPER CO2 system from Sunburst Sensors). Ancillary data for calculation of air-sea CO2 fluxes include temperature, salinity, atmospheric CO2.