cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57707/fmi-b2share.4aeh2-mwb52
title: Investigating the effect of snow-ice formation on snow depth and density over Arctic sea ice

description: We investigated the effect of snow-ice formation on snow depth and density over Arctic sea ice. We coupled SnowModel-LG, a snow modeling system adapted for snow depth and density reconstruction over sea ice, with HIGHTSI, a 1-D sea ice thermodynamic model, to simulate snow-ice growth. Pan-Arctic model simulations were performed over the period 1 August 1980 through 31 July 2022. The model outputs were gridded to the 25x25 km Equal-Area Scalable Earth Grid (EASE-Grid), provided by the National Snow and Ice Data Center (NSIDC) (361 x 361 pixels). We compared snow depth and density from the coupled product (SnowModel-LG_HS) to outputs from the SnowModel-LG. 

The data set includes daily, pan-Arctic information of snow depth, snow density, snow-ice thickness and thermodynamically grown sea ice thickness from 1.8.1980 until 31.7.2022, based on SnowModel-LG_HS. Specifically, it includes the following (4) netcdf files:

1. snod_hs.nc: Pan-Arctic snow depth based on SnowModel-LG_HS
2. sden_hs.nc: Pan-Arctic snow density based on SnowModel-LG_HS
3. sice.nc: Pan-Arctic snow-ice thickness based on SnowModel-LG_HS
4. tice.nc: Pan-Arctic thermodynamically grown sea ice thickness based on SnowModel-LG_HS
DOI: 10.57707/fmi-b2share.yw62c-6nb17
title: Investigating the effect of snow-ice formation on snow depth and density over Arctic sea ice

description: We investigated the effect of snow-ice formation on snow depth and density over Arctic sea ice. We coupled SnowModel-LG, a snow modeling system adapted for snow depth and density reconstruction over sea ice, with HIGHTSI, a 1-D sea ice thermodynamic model, to simulate snow-ice growth. Pan-Arctic model simulations were performed over the period 1 August 1980 through 31 July 2022. The model outputs were gridded to the 25x25 km Equal-Area Scalable Earth Grid (EASE-Grid), provided by the National Snow and Ice Data Center (NSIDC) (361 x 361 pixels). We compared snow depth and density from the coupled product (SnowModel-LG_HS) to outputs from the SnowModel-LG. 

The data set includes daily, pan-Arctic information of snow depth, snow density, snow-ice thickness and thermodynamically grown sea ice thickness from 1.8.1980 until 31.7.2022, based on SnowModel-LG_HS. Specifically, it includes the following (4) netcdf files:

1. snod_hs.nc: Pan-Arctic snow depth based on SnowModel-LG_HS
2. sden_hs.nc: Pan-Arctic snow density based on SnowModel-LG_HS
3. sice.nc: Pan-Arctic snow-ice thickness based on SnowModel-LG_HS
4. tice.nc: Pan-Arctic thermodynamically grown sea ice thickness based on SnowModel-LG_HS
DOI: 10.15468/dl.bcyffn
title: Occurrence Download

description: A dataset containing 1298318 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "DatasetKey is iNaturalist Research-grade Observations"
 ]
}

 The dataset includes 1298318 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046769-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57707/fmi-b2share.667h8-k2m32
title: Quantifying pan-Arctic snow depth and density trends caused by snow-ice formation [dataset]

description: We quantified the regional variations and trends of pan-Arctic snow depth and density, associated with snow-ice formation. We coupled SnowModel-LG, a snow modeling system adapted for snow depth and density reconstruction over sea ice, with HIGHTSI, a 1-D sea ice thermodynamic model, to simulate snow-ice growth. Pan-Arctic model simulations were performed over the period 1 August 1980 through 31 July 2020. The model outputs were gridded to the 25x25 km Equal-Area Scalable Earth Grid (EASE-Grid), provided by the National Snow and Ice Data Center (NSIDC) (361 x 361 pixels). We compared snow depth and density from the coupled product (SnowModel-LG_HS) to outputs from the SnowModel-LG. 

The data set includes Pan-Arctic information of snow depth and snow density from both SnowModel-LG and SnowModel-LG_HS together with snow-ice thickness on the day of maximum snow-on-sea-ice volume. It also includes the trends of snow depth, snow density and snow-ice based on SnowModel-LG_HS. Specifically, it includes the following (8) csv files:

1. snod_sm.csv: Annual pan-Arctic snow depth on the day of maximum snow-on-sea-ice volume based on SnowModel-LG
2. snod.csv: Annual pan-Arctic snow depth on the day of maximum snow-on-sea-ice volume based on SnowModel-LG_HS
3. sden_sm.csv: Annual pan-Arctic snow density on the day of maximum snow-on-sea-ice volume based on SnowModel-LG
4. sden.csv: Annual pan-Arctic snow density on the day of maximum snow-on-sea-ice volume based on SnowModel-LG_HS
5. sice.csv: Annual pan-Arctic snow-ice thickness on the day of maximum snow-on-sea-ice volume based on SnowModel-LG_HS
6. s_snod.csv: Long-term trends of snow depth from 1980 through 2020
7. s_sden.csv: Long-term trends of snow density from 1980 through 2020
8. s_sice.csv: Long-term trends of snow-ice from 1980 through 2020

The dimensions of the csv files 1-5 are (361 pixels x 361 pixels x 40 years).
The dimensions of the csv files 6-8 are (361 pixels x 361 pixels).
DOI: 10.57707/fmi-b2share.961jh-yjr46
title: Investigating the effect of snow-ice formation on snow depth and density over Arctic sea ice

description: We investigated the effect of snow-ice formation on snow depth and density over Arctic sea ice. We coupled SnowModel-LG, a snow modeling system adapted for snow depth and density reconstruction over sea ice, with HIGHTSI, a 1-D sea ice thermodynamic model, to simulate snow-ice growth. Pan-Arctic model simulations were performed over the period 1 August 1980 through 31 July 2022. The model outputs were gridded to the 25x25 km Equal-Area Scalable Earth Grid (EASE-Grid), provided by the National Snow and Ice Data Center (NSIDC) (361 x 361 pixels). We compared snow depth and density from the coupled product (SnowModel-LG_HS) to outputs from the SnowModel-LG. 

The data set includes daily, pan-Arctic information of snow depth, snow density, snow-ice thickness and thermodynamically grown sea ice thickness from 1.8.1980 until 31.7.2022, based on SnowModel-LG_HS. Specifically, it includes the following (4) netcdf files:

1. snod_hs.nc: Pan-Arctic snow depth based on SnowModel-LG_HS
2. sden_hs.nc: Pan-Arctic snow density based on SnowModel-LG_HS
3. sice.nc: Pan-Arctic snow-ice thickness based on SnowModel-LG_HS
4. tice.nc: Pan-Arctic thermodynamically grown sea ice thickness based on SnowModel-LG_HS
DOI: 10.57707/fmi-b2share.a6yhd-w7q80
title: Investigating the effect of snow-ice formation on snow depth and density over Arctic sea ice

description: We investigated the effect of snow-ice formation on snow depth and density over Arctic sea ice. We coupled SnowModel-LG, a snow modeling system adapted for snow depth and density reconstruction over sea ice, with HIGHTSI, a 1-D sea ice thermodynamic model, to simulate snow-ice growth. Pan-Arctic model simulations were performed over the period 1 August 1980 through 31 July 2022. The model outputs were gridded to the 25x25 km Equal-Area Scalable Earth Grid (EASE-Grid), provided by the National Snow and Ice Data Center (NSIDC) (361 x 361 pixels). We compared snow depth and density from the coupled product (SnowModel-LG_HS) to outputs from the SnowModel-LG. 

The data set includes daily, pan-Arctic information of snow depth, snow density, snow-ice thickness and thermodynamically grown sea ice thickness from 1.8.1980 until 31.7.2022, based on SnowModel-LG_HS. Specifically, it includes the following (4) netcdf files:

1. snod_hs.nc: Pan-Arctic snow depth based on SnowModel-LG_HS
2. sden_hs.nc: Pan-Arctic snow density based on SnowModel-LG_HS
3. sice.nc: Pan-Arctic snow-ice thickness based on SnowModel-LG_HS
4. tice.nc: Pan-Arctic thermodynamically grown sea ice thickness based on SnowModel-LG_HS
DOI: 10.15468/dl.r5xxrp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((111.614 -1.853821, 111.5437 -1.859387, 111.47513 -1.875946, 111.409977 -1.903092, 111.349843 -1.940157, 111.29621 -1.986228, 111.250396 -2.040171, 111.213531 -2.100661, 111.186523 -2.166206, 111.170037 -2.235195, 111.164481 -2.305929, 111.169993 -2.376666, 111.186439 -2.445664, 111.213416 -2.511226, 111.250261 -2.571734, 111.296068 -2.6257, 111.349708 -2.671792, 111.409862 -2.708876, 111.475047 -2.736038, 111.543656 -2.752607, 111.614 -2.758176, 111.684344 -2.752607, 111.752953 -2.736038, 111.818138 -2.708876, 111.878292 -2.671792, 111.931932 -2.6257, 111.977739 -2.571734, 112.014584 -2.511226, 112.041561 -2.445664, 112.058007 -2.376666, 112.063519 -2.305929, 112.057963 -2.235195, 112.041477 -2.166206, 112.014469 -2.100661, 111.977604 -2.040171, 111.93179 -1.986228, 111.878157 -1.940157, 111.818023 -1.903092, 111.75287 -1.875946, 111.6843 -1.859387, 111.614 -1.853821))"},{"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/0046781-251120083545085/datasets/export for details.
DOI: 10.17617/3.iwildo
title: d2Smell - Marker

description: Data collected as part of the D2Smell project. The data includes the following fields&lt;br&gt;Collection method: capture a gas from the object&lt;br&gt;Collection volume: 1000 ml&lt;br&gt;Sampling method: dynamic_headspace&lt;br&gt;TDU type: Markes TD100-xr&lt;br&gt;Notes: Marker, Edding3000, red color
DOI: 10.57707/fmi-b2share.xakhc-29497
title: RoadSurf library evaluation study dataset

description: Data related to manuscript: RoadSurf: Open-source Road Weather Model library
Data contains:
-Road weather station observations for Finnish road weather stations from 1 October 2022 to 1 April 2023
-Atmospheric forecast from Finnish Meteorological institute. The data is combination of forecast edited by meteorologist and automatically produced nowcast.
-Simulated road surface temperature and amounts of water, snow, ice and black ice on the road
-RMSE and bias calucated for the simulations for each month and forecast start time. Also, RMSE and bias for "same as yesterday" and persistence forecasts are included
-ReadMe files that explain the data in more detail
DOI: 10.57707/fmi-b2share.jp08v-7wp14
title: RoadSurf library evaluation study dataset

description: Data related to manuscript: RoadSurf: Open-source Road Weather Model library
Data contains:
-Road weather station observations for Finnish road weather stations from 1 October 2022 to 1 April 2023
-Atmospheric forecast from Finnish Meteorological institute. The data is combination of forecast edited by meteorologist and automatically produced nowcast.
-Simulated road surface temperature and amounts of water, snow, ice and black ice on the road
-RMSE and bias calucated for the simulations for each month and forecast start time. Also, RMSE and bias for "same as yesterday" and persistence forecasts are included
-ReadMe files that explain the data in more detail

Changes made to the original version:
Simulations were rerun because of a bug related to ice and snow melting. This caused changes to road weather forecast data and verification results. Atmospheric data used by the simulation was also updated to make sure it corresponds the data used by the simulations
DOI: 10.57707/fmi-b2share.5ey25-32n05
title: RoadSurf library evaluation study dataset

description: Data related to manuscript: RoadSurf: Open-source Road Weather Model library
Data contains:
-Road weather station observations for Finnish road weather stations from 1 October 2022 to 1 April 2023
-Atmospheric forecast from Finnish Meteorological institute. The data is combination of forecast edited by meteorologist and automatically produced nowcast.
-Simulated road surface temperature and amounts of water, snow, ice and black ice on the road
-RMSE and bias calucated for the simulations for each month and forecast start time. Also, RMSE and bias for "same as yesterday" and persistence forecasts are included
-ReadMe files that explain the data in more detail

Changes made to the original version:
Simulations were rerun because of a bug related to ice and snow melting. This caused changes to road weather forecast data and verification results. Atmospheric data used by the simulation was also updated to make sure it corresponds the data used by the simulations

Changes made on 4 March 2024:
-RMSE and bias were recalculated for December after removing forecasts cases with several missing atmospheric forecast files
DOI: 10.57707/fmi-b2share.jhyct-era43
title: RoadSurf library evaluation study dataset

description: Data related to manuscript: RoadSurf: Open-source Road Weather Model library
Data contains:
-Road weather station observations for Finnish road weather stations from 1 October 2022 to 1 April 2023
-Atmospheric forecast from Finnish Meteorological institute. The data is combination of forecast edited by meteorologist and automatically produced nowcast.
-Simulated road surface temperature and amounts of water, snow, ice and black ice on the road
-RMSE and bias calucated for the simulations for each month and forecast start time. Also, RMSE and bias for "same as yesterday" and persistence forecasts are included
-ReadMe files that explain the data in more detail

Changes made to the original version:
Simulations were rerun because of a bug related to ice and snow melting. This caused changes to road weather forecast data and verification results. Atmospheric data used by the simulation was also updated to make sure it corresponds the data used by the simulations

Changes made on 4 March 2024:
-RMSE and bias were recalculated for December after removing forecasts cases with several missing atmospheric forecast files
DOI: 10.5281/zenodo.17897072
title: Data Acquisition & Processing Project
DOI: 10.5281/zenodo.17897073
title: Data Acquisition & Processing Project
DOI: 10.6084/m9.figshare.29046359
title: Data and Data Dictionary from Language Models in Nutrition and Dietetics Scoping Review

description: Language models are a form of generative artificial intelligence that excel at natural language understanding and generation, with possible applications for providing evidence-based nutrition information in an interactive and personalized manner. However, they also pose risks to public health related to inaccuracy, bias, and data protection. It is unclear what quantity or type of research has been conducted on these models applied within nutrition and dietetics. This data arose from a scoping review that aimed to identify the ways in which language models are being applied to nutrition and dietetics and the associated opportunities and challenges. The review was conducted in accordance with a pre-defined protocol, and included original studies from peer-reviewed journals. The databases Medline, EMBASE, PsycINFO, CINAHL, PubMed, Global Health, Web of Science, Scopus, DBLP, and IEEE Xplore were searched, and forward and backward citation chasing conducted for included articles. This data is the information that was extracted and charted from the studies included in the review. The journal article describing the scoping review, including methods and results, is available at: https://doi.org/10.1016/j.ajcnut.2025.101127.
DOI: 10.6084/m9.figshare.29046359.v1
title: Data and Data Dictionary from Language Models in Nutrition and Dietetics Scoping Review

description: Language models are a form of generative artificial intelligence that excel at natural language understanding and generation, with possible applications for providing evidence-based nutrition information in an interactive and personalized manner. However, they also pose risks to public health related to inaccuracy, bias, and data protection. It is unclear what quantity or type of research has been conducted on these models applied within nutrition and dietetics. This data arose from a scoping review that aimed to identify the ways in which language models are being applied to nutrition and dietetics and the associated opportunities and challenges. The review was conducted in accordance with a pre-defined protocol, and included original studies from peer-reviewed journals. The databases Medline, EMBASE, PsycINFO, CINAHL, PubMed, Global Health, Web of Science, Scopus, DBLP, and IEEE Xplore were searched, and forward and backward citation chasing conducted for included articles. This data is the information that was extracted and charted from the studies included in the review. The journal article describing the scoping review, including methods and results, is available at: https://doi.org/10.1016/j.ajcnut.2025.101127.
DOI: 10.15468/dl.a3eq3g
title: Occurrence Download

description: A dataset containing 755 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Canada",
 "DatasetKey is one of (iNaturalist Research-grade Observations, Arctic Species Trend Index (ASTI) : Marine, USGS Alaska Science Center Polar Bear Maternal Dens, Happywhale - Polar bear in Arctic Ocean, Observation.org, Nature data from around the World, Blue Futures Pathways Expeditions Biodiversity Observations, NatureServe Network Species Occurrence Data)",
 "OccurrenceStatus is Present",
 "TaxonKey is Ursus maritimus",
 "Year 2010-2024"
 ]
}

 The dataset includes 755 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046783-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9f3r2r
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((111.837 -1.848821, 111.7667 -1.854387, 111.69813 -1.870946, 111.632977 -1.898092, 111.572844 -1.935157, 111.519211 -1.981228, 111.473397 -2.035171, 111.436532 -2.095661, 111.409524 -2.161206, 111.393038 -2.230195, 111.387482 -2.300929, 111.392995 -2.371666, 111.409441 -2.440665, 111.436418 -2.506226, 111.473263 -2.566734, 111.519069 -2.6207, 111.572709 -2.666792, 111.632863 -2.703876, 111.698047 -2.731038, 111.766657 -2.747607, 111.837 -2.753176, 111.907343 -2.747607, 111.975953 -2.731038, 112.041137 -2.703876, 112.101291 -2.666792, 112.154931 -2.6207, 112.200737 -2.566734, 112.237582 -2.506226, 112.264559 -2.440665, 112.281005 -2.371666, 112.286518 -2.300929, 112.280962 -2.230195, 112.264476 -2.161206, 112.237468 -2.095661, 112.200603 -2.035171, 112.154789 -1.981228, 112.101156 -1.935157, 112.041023 -1.898092, 111.97587 -1.870946, 111.9073 -1.854387, 111.837 -1.848821))"},{"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/0046784-251120083545085/datasets/export for details.
DOI: 10.5286/isis.e.rb2510633-1
title: Tracking Li-ion Diffusion Changes in Electrochemically Aged Ni-rich Li-ion Battery Cathodes

description: Ni-rich Li-ion battery cathodes like LiNi0.8Mn0.1Co0.1O2 (NMC811) are the most industry-ready candidates for next-generation high-energy-density batteries. The biggest roadblock preventing their commercialisation is their electrochemical performance degradation over long-term (&gt;100s of cycles) cycling. Thus, it is important to study the material properties of such electrochemically aged cathodes if we are to devise strategies to mitigate their performance fade. A key material property, which is hard to probe via conventional methods, is the Li-ion diffusion coefficient. In this study, we want to use positive muon spin relaxation experiments to track how the Li-ion diffusion properties change in aged NMC811 cathodes. To ensure that most representative results are obtained, measurements will be made directly on industry-like single-layer pouch cells without any modifications, which, to the best of our knowledge, will be a first of its kind experiment. Two sets of experiments will be performed in this study. Firstly, two cells—fresh and after 100 cycles—will be studied in their open-circuit-voltage, charged and discharged states. Secondly, on the discharged cells, we will perform variable temperature experiments on the discharged cells. An Xpress access proposal has already been submitted to optimise the signal and experimental setup for the pouch cells.
DOI: 10.5286/isis.e.rb2510633
title: Tracking Li-ion Diffusion Changes in Electrochemically Aged Ni-rich Li-ion Battery Cathodes

description: Ni-rich Li-ion battery cathodes like LiNi0.8Mn0.1Co0.1O2 (NMC811) are the most industry-ready candidates for next-generation high-energy-density batteries. The biggest roadblock preventing their commercialisation is their electrochemical performance degradation over long-term (&gt;100s of cycles) cycling. Thus, it is important to study the material properties of such electrochemically aged cathodes if we are to devise strategies to mitigate their performance fade. A key material property, which is hard to probe via conventional methods, is the Li-ion diffusion coefficient. In this study, we want to use positive muon spin relaxation experiments to track how the Li-ion diffusion properties change in aged NMC811 cathodes. To ensure that most representative results are obtained, measurements will be made directly on industry-like single-layer pouch cells without any modifications, which, to the best of our knowledge, will be a first of its kind experiment. Two sets of experiments will be performed in this study. Firstly, two cells—fresh and after 100 cycles—will be studied in their open-circuit-voltage, charged and discharged states. Secondly, on the discharged cells, we will perform variable temperature experiments on the discharged cells. An Xpress access proposal has already been submitted to optimise the signal and experimental setup for the pouch cells.