cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.22008/fk2/3tsbf0/vf0upy
title: get_l0tx.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/nu8rzv
title: payload_types.csv

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/2wjtog
title: get_msg.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/yhvtbf
title: __init__.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/3ezwba
title: .gitignore

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/qr2v4r
title: setup.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/sratam
title: CITATION.cff

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/h53wxz
title: LICENSE.txt

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/zyhoal
title: README.md

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/cqusf3
title: environment.yml

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/alwmc9
title: MANIFEST.in

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/g9trqx
title: .readthedocs.yaml

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/bopdvk
title: paper.md

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.15468/dl.8px28b
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((89.02503400000001 23.960925, 88.94819099999999 23.955348, 88.873259 23.938756, 88.80210099999999 23.911562, 88.736482 23.87444, 88.678028 23.828314, 88.628179 23.774328, 88.588161 23.71382, 88.55895 23.648286, 88.541251 23.579348, 88.535482 23.508707, 88.541766 23.438102, 88.55993100000001 23.369271, 88.589511 23.303905, 88.629766 23.243608, 88.67969600000001 23.189855, 88.738069 23.143963, 88.80345 23.107052, 88.874239 23.080025, 88.948706 23.06354, 89.02503400000001 23.058001, 89.10136199999999 23.06354, 89.17582899999999 23.080025, 89.246618 23.107052, 89.311999 23.143963, 89.370372 23.189855, 89.42030200000001 23.243608, 89.46055699999999 23.303905, 89.490137 23.369271, 89.508302 23.438102, 89.51458599999999 23.508707, 89.50881699999999 23.579348, 89.491118 23.648286, 89.461907 23.71382, 89.42188899999999 23.774328, 89.37204 23.828314, 89.313586 23.87444, 89.247967 23.911562, 89.17680900000001 23.938756, 89.101877 23.955348, 89.02503400000001 23.960925))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007352-260120142942310/datasets/export for details.
DOI: 10.22008/fk2/3tsbf0/i4y6mg
title: requirements.txt

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/suphik
title: paper.bib

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.15468/dl.3ranvn
title: Occurrence Download

description: A dataset containing 71 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Mapping of subtidal Laminaria hyperborea and Saccorhiza polyschides in Northern Portugal",
 "Geometry POLYGON((-27.14723 34.74421,9.14723 34.74421,9.14723 49.25579,-27.14723 49.25579,-27.14723 34.74421))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Phaeophyceae"
 ]
}

 The dataset includes 71 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007351-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22008/fk2/3tsbf0/gx7jtw
title: modules.rst

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/hz8aam
title: technical_process.rst

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/ilwiv2
title: guide_developer.rst

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/dfphsz
title: guide_user.rst

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/ett4na
title: make.bat

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/fzfytt
title: conf.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/f6f8d6
title: index.rst

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/mrkly2
title: technical_data.rst

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/betpus
title: install.rst

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/a8arwz
title: utilities.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/apktxf
title: Makefile

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/meb63j
title: __init__.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/hyclif
title: test_pressure_transducer_depth.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/wsiwbn
title: test_wind.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/ytcrst
title: test_humidity.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/ekj6e6
title: test_station_pose.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/y5ozhs
title: test_radiation.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/lnew5r
title: test_air_temperature.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/8ts5xi
title: test_gps.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/wcdt3i
title: test_station_boom_height.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/tczk0s
title: __init__.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.6084/m9.figshare.31136239
title: Dataset accompanying manuscript <i>s</i>ubmitted to Communications Earth & Environment titled "Higher but more variable annual CO<sub>2</sub> emissions from lakes in drier Arctic landscapes" by Hazuková et al.

description: Database Compilation: Lakes in this dataset are located across Alaska, Siberian Lowlands, Northwest Territories and Nunavut in Canada, West Greenland, and Scandinavia—spanning gradients of precipitation, temperature, and ecotypes (Fig. 1A). We used the Cryosphere Inland Water Greenhouse Gases Database (Song et al., 2024) and added data from more recent publications and data releases (Karlsson et al., 2024; Saros et al., 2024; Verheijen et al., 2022). When available, we also collated dissolved organic C (DOC) concentrations, specific ultraviolet absorption coefficient at 254 nm (SUVA254), total phosphorus (TP) concentrations, and pH from publications and/or authors. Climate Data: We used the European Centre for Medium Range Weather Forecasts’ ERA5 reanalysis monthly resolution products on a 0.25° grid. Lake watersheds are smaller than grid cell areas; hence, we extracted the closest gridded value of monthly accumulated total precipitation, and potential evapotranspiration until 2023 for each lake center coordinate. We subtracted potential evapotranspiration from total precipitation (P-PET) and used it as a metric of aridity and water balance in the landscape (Woo, 2012). Here, we classify lakes with long-term (1994-2023) negative summer (June – August) P-PET as ‘drier’ and lakes with positive summer P-PET as ‘wetter’; we use a time span that covers collection of all measurements included in the dataset. We used seasonal temperature data (1994-2023) to calculate Conrad’s Continentality Index (CCI) for each catchment (Conrad, 1946). Catchment Properties: For each catchment we extracted surface soil organic carbon (C) content (Northern Circumpolar Soil Carbon Database, 0-100 cm Hugelius et al., 2013) and land cover (Global Land Cover, 2019, 100 m resolution Buchhorn et al., 2020). To distinguish between wetland and upland catchments, we classified lake catchments conservatively as wetlands if wetland cover &gt; 0%. We calculated the watershed:lake area ratio (WA:LA) to scale the effect of the size of the catchment in proportion to the lake. Average slope and elevation of each catchment were derived from the processed ArcticDEM layers. To estimate seasonal variability in water surface coverage (hereafter called ‘Seasonal water (%)), we extracted seasonality data layers from Global Surface Water as available and determined the proportion of each catchment that does not represent permanent water during unfrozen periods (Pekel et al., 2016). We also used products from Brown et al., 2002 to identify permafrost condition (continuous, discontinuous, sporadic, and isolated) and ground ice content (visible ice within the first 10-20 m of the soil profile, high &gt; 20%, medium, and low &lt; 10%) within each catchment, respectively, and estimated average catchment active layer thickness using 2021 outputs from the Climate Change Initiative permafrost model (Westermann et al., 2024).
DOI: 10.22008/fk2/3tsbf0/cfmi1e
title: test_precipitation.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.22008/fk2/3tsbf0/nxo1x1
title: test_get_l2.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.6084/m9.figshare.31136239.v1
title: Dataset accompanying manuscript <i>s</i>ubmitted to Communications Earth & Environment titled "Higher but more variable annual CO<sub>2</sub> emissions from lakes in drier Arctic landscapes" by Hazuková et al.

description: Database Compilation: Lakes in this dataset are located across Alaska, Siberian Lowlands, Northwest Territories and Nunavut in Canada, West Greenland, and Scandinavia—spanning gradients of precipitation, temperature, and ecotypes (Fig. 1A). We used the Cryosphere Inland Water Greenhouse Gases Database (Song et al., 2024) and added data from more recent publications and data releases (Karlsson et al., 2024; Saros et al., 2024; Verheijen et al., 2022). When available, we also collated dissolved organic C (DOC) concentrations, specific ultraviolet absorption coefficient at 254 nm (SUVA254), total phosphorus (TP) concentrations, and pH from publications and/or authors. Climate Data: We used the European Centre for Medium Range Weather Forecasts’ ERA5 reanalysis monthly resolution products on a 0.25° grid. Lake watersheds are smaller than grid cell areas; hence, we extracted the closest gridded value of monthly accumulated total precipitation, and potential evapotranspiration until 2023 for each lake center coordinate. We subtracted potential evapotranspiration from total precipitation (P-PET) and used it as a metric of aridity and water balance in the landscape (Woo, 2012). Here, we classify lakes with long-term (1994-2023) negative summer (June – August) P-PET as ‘drier’ and lakes with positive summer P-PET as ‘wetter’; we use a time span that covers collection of all measurements included in the dataset. We used seasonal temperature data (1994-2023) to calculate Conrad’s Continentality Index (CCI) for each catchment (Conrad, 1946). Catchment Properties: For each catchment we extracted surface soil organic carbon (C) content (Northern Circumpolar Soil Carbon Database, 0-100 cm Hugelius et al., 2013) and land cover (Global Land Cover, 2019, 100 m resolution Buchhorn et al., 2020). To distinguish between wetland and upland catchments, we classified lake catchments conservatively as wetlands if wetland cover &gt; 0%. We calculated the watershed:lake area ratio (WA:LA) to scale the effect of the size of the catchment in proportion to the lake. Average slope and elevation of each catchment were derived from the processed ArcticDEM layers. To estimate seasonal variability in water surface coverage (hereafter called ‘Seasonal water (%)), we extracted seasonality data layers from Global Surface Water as available and determined the proportion of each catchment that does not represent permanent water during unfrozen periods (Pekel et al., 2016). We also used products from Brown et al., 2002 to identify permafrost condition (continuous, discontinuous, sporadic, and isolated) and ground ice content (visible ice within the first 10-20 m of the soil profile, high &gt; 20%, medium, and low &lt; 10%) within each catchment, respectively, and estimated average catchment active layer thickness using 2021 outputs from the Climate Change Initiative permafrost model (Westermann et al., 2024).
DOI: 10.22008/fk2/3tsbf0/wvftwt
title: test_process.py

description: Uploaded with GitHub Action from GEUS-Glaciology-and-Climate/pypromice.
DOI: 10.6073/pasta/06abc8ab4e230f2dc74ff3c1858cabdb
title: Phytoplankton, benthic algae, and associated environmental data from Lake Okeechobee, Florida, USA, August 2023 - November 2023

description: This data package contains phytoplankton, periphytometer, and environmental data collected from the South Florida Water Management District’s (SFWMD) Aquifer Storage and Recovery (ASR) monitoring sites and the Indian Prairie marsh during the 2023 rainy season. We collected phytoplankton from a surface water grab and benthic algae from artificial substrates (periphytometers) that were placed outside for three weeks. We also collected associated nutrient measurements and environmental data. Collections occurred twice from August to November 2023. Data were collected to better understand how phytoplankton and benthic algae differed in their responses to TN:TP ratios in a hypereutrophic lake known to have spatial differences in limiting nutrients. Data collection for this data package is complete.
DOI: 10.15468/dl.bnjvfa
title: Occurrence Download

description: A dataset containing 634 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Adenocarpus telonensis",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 634 records from 49 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002212-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5061/dryad.547d7wm4s
title: Migrants and residents of a long-lived seabird differ in their behavioural response to a stressor

description: Inter-individual variation in behaviour has been recognised as a major
 driver of population ecology, but its relationship to migratory strategy
 has been ill-explored. Here, we investigated whether male migrant and
 resident Cory’s Shearwaters (Calonectris borealis), a long-lived partially
 migratory seabird, are distinguishable by their temperament at the colony.
 We tracked a large number of individuals over two winters using GLS
 devices and assessed whether exploratory behaviour and reaction to
 extraction from the nest corresponded to migratory strategy over this
 period. While exploratory behaviour was unrelated to migratory strategy,
 birds that were resident were more likely to be unreactive towards
 extraction from the nest. This difference in behavioural stress response,
 together with previous findings that migrants display higher physiological
 stress over winter, suggests that migrants and residents may be
 distinguishable by their stress threshold.
DOI: 10.5061/dryad.jdfn2z37n
title: Early sexual dimorphism in the developing gut microbiome of northern elephant seals

description: The gut microbiome is an integral part of a species’ ecology, but we know
 little about how host characteristics impact its development in wild
 populations. Here, we explored the role of such intrinsic factors in
 shaping the gut microbiome of northern elephant seals during a critical
 developmental window of six weeks after weaning, when the pups stay ashore
 without feeding. We found substantial sex-differences in the early-life
 gut microbiome, even though males and females could not yet be
 distinguished morphologically. Sex and age both explained around 15% of
 the variation in gut microbial beta diversity, while microbial communities
 sampled from the same individual showed high levels of similarity across
 time, explaining another 40% of the variation. Only a small proportion of
 the variation in beta diversity was explained by health status, assessed
 by full blood counts, but clinically healthy individuals had a greater
 microbial alpha diversity than their clinically abnormal peers. Across the
 post-weaning period, the northern elephant seal gut microbiome was highly
 dynamic. We found evidence for several colonisation and extinction events
 as well as a decline in Bacteroides and an increase in Prevotella, a
 pattern that has previously been associated with the transition from
 nursing to solid food. Lastly, we show that genetic relatedness was
 correlated with gut microbiome similarity in males but not females, again
 reflecting early sex-differences. Our study represents a naturally
 diet-controlled and longitudinal investigation of how intrinsic factors
 shape the early gut microbiome in a species with extreme sex differences
 in morphology and life history.
description: Here, we studied the gut microbiome of northern elephant seals
 across the post-weaning period. We sampled rectal swabs to quantify the
 microbiome communities using 16S rRNA sequencing (please have a look at
 our Molecular Ecology paper for details). The processed 16S raw reads were
 assembled into amplicon sequence variants (ASVs). In this repository,
 we store these ASV tables alongside other sampling data (Sex, age,
 clinical health status) in phyloseq objects (ps0 and ps3). These are used
 in the R-scripts and make it possible for the interested scientist to
 reproduce our analyses and figures. The raw reads are stored in the
 European Nucleotide Archive (ENA) under study accession PRJEB36555 and we
 also provide the relevant scripts used for bioinformatics processing. More
 information can be found in README.txt . 
description: The full repository can also be found at https://github.com/mastoffel/nes_microbiome . The README file and comments in the R-scripts contain all relevant information to reproduce the analyses in the paper.
DOI: 10.5061/dryad.1rn8pk0q8
title: Elder barn owl nestlings flexibly redistribute parental food according to siblings’ need or in return for allopreening

description: Kin selection and reciprocation of biological services are distinct
 theories invoked to explain the origin and evolutionary maintenance of
 altruistic and cooperative behaviours. Although these behaviours are
 considered as non-mutually exclusive, the cost-to-benefit balance to
 behave altruistically or to reciprocally cooperate, and the conditions
 promoting a switch between such different strategies have rarely been
 tested. Here we examined the association between allofeeding, allopreening
 and vocal solicitations in wild barn owl (Tyto alba) broods under
 different food abundance conditions: natural food provisioning, and after
 an experimental food supplementation. Allofeeding was mainly performed by
 elder nestlings (hatching is asynchronous) in prime condition, especially
 when the cost of forgoing a prey was small (when parents allocated more
 prey to the food donor and after food supplementation). Nestlings
 preferentially shared food with siblings that emitted very intense calls,
 thus potentially increasing indirect fitness benefits, or the ones that
 provided extensive allopreening to the donor, thus possibly promoting
 direct benefits from reciprocation. Finally, allopreening was mainly
 directed towards older siblings, perhaps to maximize the probability of
 being fed in return. Helping behaviour among relatives can therefore be
 driven by both kin selection and direct cooperation, although it is
 dependent on the contingent environmental conditions.
description: We recorded the allofeeding, allopreening and vocal
 negotiation of entire barn owl broods in the natural condition using
 cameras and individual microphones. We recorded during two consecutive
 nights under different conditions of food abundance: one night under the
 natural food provisioning regimes provided by parents and a second one
 after an experimental food supplementation. The sex of each nestling was
 determined using molecular markers and age by measuring the left-wing from
 the bird’s wrist to the tip of the longest primary.
DOI: 10.82294/dvnhcj3bf
title: Observaciones semiestructuradas. Territorio: Cali. Eje medular: Agenciamiento. Sector: Educación. Año: 2025

description: La capacidad de agencia o agenciamiento se entiende como las libertades y oportunidades que tiene un individuo para tomar decisiones y actuar en un contexto, transformando situaciones que afectan a su comunidad a nivel social y ambiental. Implica la capacidad de razonar críticamente y llevar a cabo acciones intencionales. En el ámbito de la educación artística y ambiental, esto se manifiesta en la capacidad de realizar el potencial creativo y de contribuir a la construcción de una realidad institucional o social (Maras, 2018; Katz-Buonicontro, 2018; García-Calvo y Garzón-Barragán, 2023).