cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.arhg3t
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Nicandra physalodes",
 "Country is Madagascar",
 "HasCoordinate is true"
 ]
}

 The dataset includes 14 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057665-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q522qx
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Citrus limon",
 "Country is Fiji",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057666-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c99w84
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Parthenocissus quinquefolia",
 "Country is Slovenia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 7 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057669-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8mf358
title: Occurrence Download

description: A dataset containing 189 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Parthenocissus quinquefolia",
 "Country is Russian Federation",
 "HasCoordinate is true"
 ]
}

 The dataset includes 189 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057668-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u6yxnp
title: Occurrence Download

description: A dataset containing 553 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Parthenocissus quinquefolia",
 "Country is Sweden",
 "HasCoordinate is true"
 ]
}

 The dataset includes 553 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057670-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a8k2fu
title: Occurrence Download

description: A dataset containing 122 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Citrus limon",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 122 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057672-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h3adhk
title: Occurrence Download

description: A dataset containing 1573 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Solanum chomatophilum"
 ]
}

 The dataset includes 1573 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009161-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.etbck6
title: Occurrence Download

description: A dataset containing 132 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cryptotis colombiana"
 ]
}

 The dataset includes 132 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022356-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/o7rwm8
title: eco2adapt_Specific Leaf Area SLA_Lithuania LL.tab

description: :unav
DOI: 10.57745/gbdv17
title: eco2adapt_Specific Leaf Area SLA_Lithuania LL

description: This dataset contains calculated values of Specific Leaf Area (SLA) derived from leaf samples collected in the Lithuanian Living Lab (Marcinkonys, May 2024) as part of the eco2adapt project.
DOI: 10.15468/dl.dzuh9z
title: Occurrence Download

description: A dataset containing 3566 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Crocodylia",
 "Country is South Africa",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3566 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022357-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9n58fk
title: Occurrence Download

description: A dataset containing 294 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lecidella stigmatea",
 "Country is one of (Norway)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "CoordinateUncertaintyInMeters is less than or equal to 1000",
 "Year is greater than or equal to 1986",
 "Year is less than or equal to 2025",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 {
 "not" : {
 "BasisOfRecord" : [
 "is Machine Observation"
 ]
 }
 }
 ]
}

 The dataset includes 294 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022358-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k6xm22
title: Occurrence Download

description: A dataset containing 105723 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON((-25.53253 17.11375,-25.34977 16.83129,-24.90117 16.63192,-24.54395 16.54053,-24.44426 16.32454,-24.26149 15.13658,-24.95101 14.98704,-24.80978 14.69628,-24.17011 14.74613,-24.24488 15.06181,-23.88766 15.12827,-23.63844 14.7129,-23.24799 14.80428,-22.924 15.29442,-23.07353 15.50211,-24.12857 15.21965,-24.36949 16.30793,-22.75785 15.85932,-22.56678 16.15008,-22.79939 16.89775,-23.03199 16.89775,-23.05692 16.70668,-22.99046 16.499,-22.74954 16.53223,-22.73293 16.30793,-23.04861 16.22485,-23.08184 16.02547,-24.28642 16.391,-23.91258 16.55715,-24.23657 16.73991,-24.56887 16.85622,-24.54395 16.64853,-25.02578 16.72745,-24.76409 16.85622,-24.89286 17.25082,-25.32069 17.28405,-25.57407 17.19267,-25.53253 17.11375))"
 ]
}

 The dataset includes 105723 records from 301 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022305-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18662442
title: Proglacial wetlands: an overlooked CO2 sink within recently deglaciated landscapes

description: Dataset with publication in SOIL Letters 2026.
DOI: 10.15468/dl.jy8jnz
title: Occurrence Download

description: A dataset containing 28967 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Cosmia pyralina"
 ]
}

 The dataset includes 28967 records from 235 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022359-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2x9qwh
title: Occurrence Download

description: A dataset containing 144067 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bellis perennis",
 "Continent is Europe",
 "HasCoordinate is true",
 "CoordinateUncertaintyInMeters is less than or equal to 100",
 "Year is greater than or equal to 1970",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 {
 "not" : {
 "BasisOfRecord" : [
 "is Material citation"
 ]
 }
 }
 ]
}

 The dataset includes 144067 records from 326 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022225-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57783/dgah-gk58
title: Nielsen Retail Scanner

description: For any questions, please contact:

Eric Vlach, Associate Director of Cloud & Systems Engineering, Arts & Sciences IT, at ev2322@columbia.edu
description: NielsenIQ Retail Scanner Data
description: Through a relationship with NielsenIQ, the Kilts Center at the University of Chicago Booth School of Business provides multiple consumer datasets to academic researchers around the world.

Columbia has an agreement with the Kilts Center. Authorized faculty, graduate students, and research staff can apply to access this dataset.



Annual funding of the dataset is shared between the Program for Economic Research and the Libraries.



Funding for data analysis using the Columbia Data Platform is provided by the Program for Economic Research.


description: The Retail Scanner Data (also referred to as RMS data) consists of weekly pricing, volume, and store merchandising conditions generated by participating retail store point-of-sale systems in all US markets. Depending on the year, data are included from approximately 30,000-50,000 participating grocery, drug, mass merchandiser, and other stores. Products from all NielsenIQ-tracked categories are included in the data, such as food, non-food grocery items, health and beauty aids, and select general merchandise. Currently, the years 2006-2021 are included. We expect to update the data on an annual basis. Updates are expected to be available in the first quarter of each calendar year, and will always lag by 2 years (e.g. 2022 Retail Scanner data is expected to be released in Q1 of 2024).



There are three major types of files associated with the Retail Scanner Data: Stores, Product Description, and Movement (i.e., weekly sales and pricing). The Stores file contains information about each individual store location. The Product Description file contains information about each UPC. The Movement files contain the price and quantity of goods sold at specific stores on a specific week.
DOI: 10.6084/m9.figshare.31347707
title: Rice Bran Oil: A Critical Review on Its Chemistry, Nutrition, Processing Technology, and Application

description: Rice bran oil (RBO) is an underutilized by-product of rice milling with substantial potential for value addition in the global rice industry due to its distinctive compositions and properties. The objective of this review is to provide a comprehensive and critical synthesis of current knowledge on RBO, with emphasis on processing technologies, nutritional functionality, and application development, while identifying key strengths, limitations, and future opportunities across the value chain. The review is based on a systematic survey of literature , focusing on the progress in the last 5–6 years. Comparative evaluation shows that solvent extraction remains the most efficient and widely adopted industrial method for RBO production A few innovative extraction technologies demonstrate clear advantages in preserving bioactive minor lipids, but their broader implementation is constrained by cost, scalability, and process complexity. Nutritionally, RBO is characterized by a unique bioactive fraction (3–5%) that underpins reported antioxidant, cholesterol-lowering, and anti-inflammatory effects. Application studies confirm its strong performance in culinary uses, particularly frying, and its relevance in nutraceutical and pharmaceutical formulations. Emerging insights into rice bran further expands the functional landscape of RBO/rice bran. Future research should prioritize deeper understanding, while industry adoption will benefit from integrated processing and by-product valorization strategies.
DOI: 10.6084/m9.figshare.31347707.v1
title: Rice Bran Oil: A Critical Review on Its Chemistry, Nutrition, Processing Technology, and Application

description: Rice bran oil (RBO) is an underutilized by-product of rice milling with substantial potential for value addition in the global rice industry due to its distinctive compositions and properties. The objective of this review is to provide a comprehensive and critical synthesis of current knowledge on RBO, with emphasis on processing technologies, nutritional functionality, and application development, while identifying key strengths, limitations, and future opportunities across the value chain. The review is based on a systematic survey of literature , focusing on the progress in the last 5–6 years. Comparative evaluation shows that solvent extraction remains the most efficient and widely adopted industrial method for RBO production A few innovative extraction technologies demonstrate clear advantages in preserving bioactive minor lipids, but their broader implementation is constrained by cost, scalability, and process complexity. Nutritionally, RBO is characterized by a unique bioactive fraction (3–5%) that underpins reported antioxidant, cholesterol-lowering, and anti-inflammatory effects. Application studies confirm its strong performance in culinary uses, particularly frying, and its relevance in nutraceutical and pharmaceutical formulations. Emerging insights into rice bran further expands the functional landscape of RBO/rice bran. Future research should prioritize deeper understanding, while industry adoption will benefit from integrated processing and by-product valorization strategies.
DOI: 10.20383/101.0249
title: Data files for "Tree Swallow selection for wetlands in agricultural landscapes predicted by central-place foraging theory"

description: Complete dataset for manuscript entitled "Tree Swallow selection for wetlands in agricultural landscapes predicted by central-place foraging theory". Our study examined Tree Swallow (Tachycineta bicolor) habitat and space use on sites with differing agricultural land use/land cover (cropland-dominated, grassland-dominated, and one “mixed” site with cropland and herbaceous cover). Miniature GPS tags were attached to swallows during the nestling provisioning period, and locational fixes were attempted every 10 minutes from 0500 to 2200 local time, over the course of one day for each swallow. This dataset includes GPS locational fixes from 24 female swallows, the coordinates (lat./long.) for each swallow’s nest box, and a classified land cover raster used to assess habitat use. Also included are insect abundance and biomass data derived from terrestrial sweep-net sampling in different habitats considered “available” to foraging swallows (field “margins,” “pond” margins, and “uplands” (cropland or grassland/herbaceous cover). Finally, we include a set of Tree Swallow body mass data, used to test for impacts of tagging on changes in swallows’ body mass.
DOI: 10.6084/m9.figshare.30943941
title: Effects of sandhi-based predictability in Kansai Japanese depend on markedness: a visual-world eye-tracking study

description: During comprehension, various aspects of upcoming input can be anticipated. However, it is unclear to what extent listeners exploit suprasegmental information to facilitate lexical retrieval. In Japanese, a Kansai-dialect-specific pitch accent system and a sandhi rule create a situation where the final tone of certain modifiers is conditioned by the initial tone of the following noun, thereby making the upcoming tone predictable. Both Tokyo and Kansai speakers showed increased fixations on the target when its initial tone was predictable based on an all-L-tone modifier, suggesting that this “predictability effect” may reflect an increased anticipation of upcoming input that disrupts a marked L-tone sequence, without necessarily invoking the dialect-specific sandhi rule. However, Kansai speakers were quicker to identify the target in this condition, suggesting that they utilised dialect-specific knowledge about the lexically determined pitch accent of the target noun to facilitate processing, particularly when the modifier tone deviated from the default.
DOI: 10.6084/m9.figshare.30943941.v1
title: Effects of sandhi-based predictability in Kansai Japanese depend on markedness: a visual-world eye-tracking study

description: During comprehension, various aspects of upcoming input can be anticipated. However, it is unclear to what extent listeners exploit suprasegmental information to facilitate lexical retrieval. In Japanese, a Kansai-dialect-specific pitch accent system and a sandhi rule create a situation where the final tone of certain modifiers is conditioned by the initial tone of the following noun, thereby making the upcoming tone predictable. Both Tokyo and Kansai speakers showed increased fixations on the target when its initial tone was predictable based on an all-L-tone modifier, suggesting that this “predictability effect” may reflect an increased anticipation of upcoming input that disrupts a marked L-tone sequence, without necessarily invoking the dialect-specific sandhi rule. However, Kansai speakers were quicker to identify the target in this condition, suggesting that they utilised dialect-specific knowledge about the lexically determined pitch accent of the target noun to facilitate processing, particularly when the modifier tone deviated from the default.
DOI: 10.5281/zenodo.18662441
title: Proglacial wetlands: an overlooked CO2 sink within recently deglaciated landscapes

description: Dataset with publication in SOIL Letters 2026.
DOI: 10.6084/m9.figshare.31347717
title: Modalities and algorithms for generalized motor seizure detection and prediction: a scoping review

description: Generalized tonic – clonic seizures (GTCS) are among the most severe seizure types and a major cause of sudden unexpected death in epilepsy (SUDEP). Continuous monitoring is essential, particularly for individuals with drug-resistant epilepsy. While electroencephalography (EEG) remains the diagnostic gold standard, non-EEG wearable systems have emerged as promising, user-friendly alternatives enhancing comfort, mobility, and social acceptance. This scoping review compared sensing modalities and computational methods used for seizure detection and prediction, identified effective sensor combinations, and highlighted current research gaps. Following PRISMA-ScR guidelines, three databases – Scopus, IEEE Xplore, and PubMed – were searched up to 22 April 2025. Twenty-nine studies met inclusion criteria (twenty-six on detection and three on prediction). Combining electrodermal activity (EDA) with motion sensors – accelerometers (ACC), gyroscopes (GYR), and surface electromyography (sEMG) – achieved the highest detection performance (95.2% sensitivity, 98.6% precision, false alarm rate (FAR) 0.64/24 h). As for seizure prediction, HR data showed promise for having sufficient seizure-predictive information with a sensitivity of 62%. Wearable multimodal non-EEG seizure detection and prediction systems offer personalized care but face validation, hardware, and privacy hurdles. Future success depends on efficient AI, IoT integration, patient-centric design, and clear regulations ensuring accessible and trustworthy clinical tools. People with epilepsy experience sudden episodes of abnormal brain activity called seizures, or fits. One serious type, the generalized tonic – clonic seizure (GTCS), causes loss of consciousness, muscle stiffening, and body jerking, which can lead to injury or even sudden unexpected death in epilepsy (SUDEP). Traditionally, seizures are monitored in hospitals using electroencephalography (EEG), which records brain signals. However, EEG systems are uncomfortable for daily use and limit continuous monitoring. Recent innovations in wearable sensor technology – such as motion sensors (accelerometers), skin conductance sensors (electrodermal activity), and heart or blood flow sensors (photoplethysmography) – offer a more practical, noninvasive alternative. These devices can be worn as smartwatches or bracelets, fitting naturally into everyday life. Artificial intelligence (AI) and machine learning play an increasingly important role in these systems. By recognizing complex patterns in motion and physiological signals, AI algorithms can automatically detect seizures and, in early research, may even predict when a seizure is likely to occur. While results so far are promising, more clinical studies and larger datasets are needed to confirm accuracy and reliability across different patient groups. If proven effective, these smart wearable systems could help alert caregivers in real time, support personalized treatment, and greatly reduce risks associated with epilepsy.
DOI: 10.6084/m9.figshare.31347717.v1
title: Modalities and algorithms for generalized motor seizure detection and prediction: a scoping review

description: Generalized tonic – clonic seizures (GTCS) are among the most severe seizure types and a major cause of sudden unexpected death in epilepsy (SUDEP). Continuous monitoring is essential, particularly for individuals with drug-resistant epilepsy. While electroencephalography (EEG) remains the diagnostic gold standard, non-EEG wearable systems have emerged as promising, user-friendly alternatives enhancing comfort, mobility, and social acceptance. This scoping review compared sensing modalities and computational methods used for seizure detection and prediction, identified effective sensor combinations, and highlighted current research gaps. Following PRISMA-ScR guidelines, three databases – Scopus, IEEE Xplore, and PubMed – were searched up to 22 April 2025. Twenty-nine studies met inclusion criteria (twenty-six on detection and three on prediction). Combining electrodermal activity (EDA) with motion sensors – accelerometers (ACC), gyroscopes (GYR), and surface electromyography (sEMG) – achieved the highest detection performance (95.2% sensitivity, 98.6% precision, false alarm rate (FAR) 0.64/24 h). As for seizure prediction, HR data showed promise for having sufficient seizure-predictive information with a sensitivity of 62%. Wearable multimodal non-EEG seizure detection and prediction systems offer personalized care but face validation, hardware, and privacy hurdles. Future success depends on efficient AI, IoT integration, patient-centric design, and clear regulations ensuring accessible and trustworthy clinical tools. People with epilepsy experience sudden episodes of abnormal brain activity called seizures, or fits. One serious type, the generalized tonic – clonic seizure (GTCS), causes loss of consciousness, muscle stiffening, and body jerking, which can lead to injury or even sudden unexpected death in epilepsy (SUDEP). Traditionally, seizures are monitored in hospitals using electroencephalography (EEG), which records brain signals. However, EEG systems are uncomfortable for daily use and limit continuous monitoring. Recent innovations in wearable sensor technology – such as motion sensors (accelerometers), skin conductance sensors (electrodermal activity), and heart or blood flow sensors (photoplethysmography) – offer a more practical, noninvasive alternative. These devices can be worn as smartwatches or bracelets, fitting naturally into everyday life. Artificial intelligence (AI) and machine learning play an increasingly important role in these systems. By recognizing complex patterns in motion and physiological signals, AI algorithms can automatically detect seizures and, in early research, may even predict when a seizure is likely to occur. While results so far are promising, more clinical studies and larger datasets are needed to confirm accuracy and reliability across different patient groups. If proven effective, these smart wearable systems could help alert caregivers in real time, support personalized treatment, and greatly reduce risks associated with epilepsy.