cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.dn5ggb
title: Occurrence Download

description: A dataset containing 17694 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is SP-Algae - Herbário do Estado \"Maria Eneyda P. Kaufmann Fidalgo - Coleção de Algas"
 ]
}

 The dataset includes 17694 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021735-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7dt7xb
title: Occurrence Download

description: A dataset containing 9619 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "IucnRedListCategory is one of (CR, EN, VU)",
 "TaxonKey is Plantae",
 "Year 1981-2024"
 ]
}

 The dataset includes 9619 records from 108 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021737-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13943076
title: SCSES test data

description: This dataset includes BAM files for three cell lines, each containing five cells. The data is utilized for testing the SCSES package, and the raw data is from the GEO database.
DOI: 10.15468/dl.8nujqr
title: Occurrence Download

description: A dataset containing 15146754 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bryophyta"
 ]
}

 The dataset includes 15146754 records from 2457 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021707-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p97ku2yd
title: Southern Beaufort Sea Polar Bear Diet and Gut Microbiota Data, 2015-2019

description: This dataset is one table with data collected and derived from polar bears sampled in Alaska's southern Beaufort Sea during 2015-2019. Data include demographic and morphometric information from each sampled polar bear, gut microbiome diversity indices derived from fecal DNA metabarcoding, and the proportion of prey species detected in individual bear diets derived from quantitative fatty acid analysis (QFASA). The presence of prey items in the diet of polar bears was also assessed using fecal DNA metabarcoding. Note that cetacean data obtained via DNA metabarcoding was not analyzed and is not presented in this data release. Only southern Beaufort Sea population data are included in this USGS data release. All DNA sequence data have been submitted to the NCBI Short Read Archive (accession bioproject ID: PRJNA773176).
DOI: 10.15468/dl.qd447v
title: Occurrence Download

description: A dataset containing 522510 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "TaxonKey is Animalia",
 "Year 1985-2000"
 ]
}

 The dataset includes 522510 records from 512 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062890-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14275645
title: 2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics

description:  

This dataset is the first release of data for the 2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics

The competition is open and welcomes everyone who wishes to participate and to anyone who can benefit from these data.

Competition Outline

Forecasting of electric energy consumption can be a very difficult tasks when handling building-level data. However, an accurate forecast is needed to boost the potential of energy management systems. The need to forecast energy consumption grows as our reliance on renewable energy sources, such as solar and wind power, grows. This means that to meet consumer demand with renewable energy generation, energy management systems must operate based on accurate energy forecasting models for both short and long-term periods. Energy consumption forecasting techniques that can manage a variety of scenarios, including varying prediction timeframes, accessible data, data frequency, and even data quality, have been the subject of intense research. There is no one-size-fits-all approach, where certain situations call for different approaches. The goal of this competition is to compile and evaluate the most recent advances in energy consumption forecasting techniques.

 

Releases Details



v1.0: one year of data from a smart building with readings taken every 5 minutes.

v2.0: 40 days of data from a smart building with readings taken every 5 minutes.

v3.x: a single day of data from a smart building with readings taken every hour. These releases will become available during the first competition period (from 06/01/2025 to 10/01/2025).

v4.x (to be confirmed): a single day of data from a smart building with readings taken every hour. These releases will become available during the second competition period.


 

Dataset Description

All releases are composed of the following data:



Time: in hours and minutes

Power: in Watts

Voltage: in Volts

Current: in Ampers

Generation power: in Watts

Temperature: in ºC
description: The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020), DOI:10.54499/UIDB/00760/2020.
DOI: 10.5281/zenodo.14275818
title: 2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics

description:  

This dataset is the second release of data for the 2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics

The competition is open and welcomes everyone who wishes to participate and to anyone who can benefit from these data.

Competition Outline

Forecasting of electric energy consumption can be a very difficult tasks when handling building-level data. However, an accurate forecast is needed to boost the potential of energy management systems. The need to forecast energy consumption grows as our reliance on renewable energy sources, such as solar and wind power, grows. This means that to meet consumer demand with renewable energy generation, energy management systems must operate based on accurate energy forecasting models for both short and long-term periods. Energy consumption forecasting techniques that can manage a variety of scenarios, including varying prediction timeframes, accessible data, data frequency, and even data quality, have been the subject of intense research. There is no one-size-fits-all approach, where certain situations call for different approaches. The goal of this competition is to compile and evaluate the most recent advances in energy consumption forecasting techniques.

 

Releases Details



v1.0: one year of data from a smart building with readings taken every 5 minutes.

v2.0: 40 days of data from a smart building with readings taken every 5 minutes.

v3.x: a single day of data from a smart building with readings taken every hour. These releases will become available during the first competition period (from 06/01/2025 to 10/01/2025).

v4.x (to be confirmed): a single day of data from a smart building with readings taken every hour. These releases will become available during the second competition period.


 

Dataset Description

All releases are composed of the following data:



Time: in hours and minutes

Power: in Watts

Voltage: in Volts

Current: in Ampers

Generation power: in Watts

Temperature: in ºC
description: The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020), DOI:10.54499/UIDB/00760/2020.
DOI: 10.5281/zenodo.14592706
title: 2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics

description:  

This dataset is the second release of data for the 2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics

The competition is open and welcomes everyone who wishes to participate and to anyone who can benefit from these data.

Competition Outline

Forecasting of electric energy consumption can be a very difficult tasks when handling building-level data. However, an accurate forecast is needed to boost the potential of energy management systems. The need to forecast energy consumption grows as our reliance on renewable energy sources, such as solar and wind power, grows. This means that to meet consumer demand with renewable energy generation, energy management systems must operate based on accurate energy forecasting models for both short and long-term periods. Energy consumption forecasting techniques that can manage a variety of scenarios, including varying prediction timeframes, accessible data, data frequency, and even data quality, have been the subject of intense research. There is no one-size-fits-all approach, where certain situations call for different approaches. The goal of this competition is to compile and evaluate the most recent advances in energy consumption forecasting techniques.

 

Releases Details



v1.0: one year of data from a smart building with readings taken every 5 minutes.

v2.0: 40 days of data from a smart building with readings taken every 5 minutes.

v3.x: a single day of data from a smart building with readings taken every hour. These releases will become available during the first competition period (from 06/01/2025 to 10/01/2025).

v4.x (to be confirmed): a single day of data from a smart building with readings taken every hour. These releases will become available during the second competition period.


 

Dataset Description

All releases are composed of the following data:



Time: in hours and minutes

Power: in Watts

Voltage: in Volts

Current: in Ampers

Generation power: in Watts

Temperature: in ºC
description: The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020), DOI:10.54499/UIDB/00760/2020.
DOI: 10.5281/zenodo.14606127
title: 2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics

description:  

This dataset is the second release of data for the 2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics

The competition is open and welcomes everyone who wishes to participate and to anyone who can benefit from these data.

Competition Outline

Forecasting of electric energy consumption can be a very difficult tasks when handling building-level data. However, an accurate forecast is needed to boost the potential of energy management systems. The need to forecast energy consumption grows as our reliance on renewable energy sources, such as solar and wind power, grows. This means that to meet consumer demand with renewable energy generation, energy management systems must operate based on accurate energy forecasting models for both short and long-term periods. Energy consumption forecasting techniques that can manage a variety of scenarios, including varying prediction timeframes, accessible data, data frequency, and even data quality, have been the subject of intense research. There is no one-size-fits-all approach, where certain situations call for different approaches. The goal of this competition is to compile and evaluate the most recent advances in energy consumption forecasting techniques.

 

Releases Details



v1.0: one year of data from a smart building with readings taken every 5 minutes.

v2.0: 40 days of data from a smart building with readings taken every 5 minutes.

v3.x: a single day of data from a smart building with readings taken every hour. These releases will become available during the first competition period (from 06/01/2025 to 10/01/2025).

v4.x (to be confirmed): a single day of data from a smart building with readings taken every hour. These releases will become available during the second competition period.


 

Dataset Description

All releases are composed of the following data:



Time: in hours and minutes

Power: in Watts

Voltage: in Volts

Current: in Ampers

Generation power: in Watts

Temperature: in ºC
description: The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020), DOI:10.54499/UIDB/00760/2020.
DOI: 10.5281/zenodo.14611255
title: 2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics

description:  

This dataset is the second release of data for the 2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics

The competition is open and welcomes everyone who wishes to participate and to anyone who can benefit from these data.

Competition Outline

Forecasting of electric energy consumption can be a very difficult tasks when handling building-level data. However, an accurate forecast is needed to boost the potential of energy management systems. The need to forecast energy consumption grows as our reliance on renewable energy sources, such as solar and wind power, grows. This means that to meet consumer demand with renewable energy generation, energy management systems must operate based on accurate energy forecasting models for both short and long-term periods. Energy consumption forecasting techniques that can manage a variety of scenarios, including varying prediction timeframes, accessible data, data frequency, and even data quality, have been the subject of intense research. There is no one-size-fits-all approach, where certain situations call for different approaches. The goal of this competition is to compile and evaluate the most recent advances in energy consumption forecasting techniques.

 

Releases Details



v1.0: one year of data from a smart building with readings taken every 5 minutes.

v2.0: 40 days of data from a smart building with readings taken every 5 minutes.

v3.x: a single day of data from a smart building with readings taken every hour. These releases will become available during the first competition period (from 06/01/2025 to 10/01/2025).

v4.x (to be confirmed): a single day of data from a smart building with readings taken every hour. These releases will become available during the second competition period.


 

Dataset Description

All releases are composed of the following data:



Time: in hours and minutes

Power: in Watts

Voltage: in Volts

Current: in Ampers

Generation power: in Watts

Temperature: in ºC
description: The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020), DOI:10.54499/UIDB/00760/2020.
DOI: 10.5281/zenodo.14617924
title: 2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics

description:  

This dataset is the second release of data for the 2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics

The competition is open and welcomes everyone who wishes to participate and to anyone who can benefit from these data.

Competition Outline

Forecasting of electric energy consumption can be a very difficult tasks when handling building-level data. However, an accurate forecast is needed to boost the potential of energy management systems. The need to forecast energy consumption grows as our reliance on renewable energy sources, such as solar and wind power, grows. This means that to meet consumer demand with renewable energy generation, energy management systems must operate based on accurate energy forecasting models for both short and long-term periods. Energy consumption forecasting techniques that can manage a variety of scenarios, including varying prediction timeframes, accessible data, data frequency, and even data quality, have been the subject of intense research. There is no one-size-fits-all approach, where certain situations call for different approaches. The goal of this competition is to compile and evaluate the most recent advances in energy consumption forecasting techniques.

 

Releases Details



v1.0: one year of data from a smart building with readings taken every 5 minutes.

v2.0: 40 days of data from a smart building with readings taken every 5 minutes.

v3.x: a single day of data from a smart building with readings taken every hour. These releases will become available during the first competition period (from 06/01/2025 to 10/01/2025).

v4.x (to be confirmed): a single day of data from a smart building with readings taken every hour. These releases will become available during the second competition period.


 

Dataset Description

All releases are composed of the following data:



Time: in hours and minutes

Power: in Watts

Voltage: in Volts

Current: in Ampers

Generation power: in Watts

Temperature: in ºC
description: The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020), DOI:10.54499/UIDB/00760/2020.
DOI: 10.5281/zenodo.14625060
title: 2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics

description:  

This dataset is the second release of data for the 2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics

The competition is open and welcomes everyone who wishes to participate and to anyone who can benefit from these data.

Competition Outline

Forecasting of electric energy consumption can be a very difficult tasks when handling building-level data. However, an accurate forecast is needed to boost the potential of energy management systems. The need to forecast energy consumption grows as our reliance on renewable energy sources, such as solar and wind power, grows. This means that to meet consumer demand with renewable energy generation, energy management systems must operate based on accurate energy forecasting models for both short and long-term periods. Energy consumption forecasting techniques that can manage a variety of scenarios, including varying prediction timeframes, accessible data, data frequency, and even data quality, have been the subject of intense research. There is no one-size-fits-all approach, where certain situations call for different approaches. The goal of this competition is to compile and evaluate the most recent advances in energy consumption forecasting techniques.

 

Releases Details



v1.0: one year of data from a smart building with readings taken every 5 minutes.

v2.0: 40 days of data from a smart building with readings taken every 5 minutes.

v3.x: a single day of data from a smart building with readings taken every hour. These releases will become available during the first competition period (from 06/01/2025 to 10/01/2025).

v4.x (to be confirmed): a single day of data from a smart building with readings taken every hour. These releases will become available during the second competition period.


 

Dataset Description

All releases are composed of the following data:



Time: in hours and minutes

Power: in Watts

Voltage: in Volts

Current: in Ampers

Generation power: in Watts

Temperature: in ºC
description: The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020), DOI:10.54499/UIDB/00760/2020.
DOI: 10.5281/zenodo.14607036
title: Ground forest fuels data for Scots pine forests in Boiarka Forest Research Station, Kyiv oblast, Ukraine

description: This data collection was carried out in Scots pine (Pinus sylverstris L.) forests of Boiarka Forest Research Station, Kyiv oblast, Ukraine. This station is a unit of National University of Life and Environmental Sciences (Kyiv, Ukraine).

The data collection was fully supported by Formas, Swedish government research council for sustainable development (https://formas.se/en/). This project, 'Emergency action to collect data in war-affected Scots pine forests in Ukraine', aimed to collect a comprehensive data set on ground forest fuels in rich pine forests close to Kyiv City. This data set is used for fire behavior modeling.

Use file ReadMe.xlsx to examine the description of other files in this data set.

Primary file, SAMPLE_DATA.xlsx contains information about samples on transects and sub-plots within sample plots (litter and duff, dead and live fuels, also snags).

Main file, PLOT_DATA.xlsx contains summary on ground fuels calculated by FIREMON methodology.

This data set also contains PDF files with raw data materials (which may contain errors on plot ID, cleaned in final data) and ZIP-archives with photos from each plot. Every person who occasionally appeared in these research photos, provided their consent for sharing these materials.
DOI: 10.5281/zenodo.14607037
title: Ground forest fuels data for Scots pine forests in Boiarka Forest Research Station, Kyiv oblast, Ukraine

description: This data collection was carried out in Scots pine (Pinus sylverstris L.) forests of Boiarka Forest Research Station, Kyiv oblast, Ukraine. This station is a unit of National University of Life and Environmental Sciences (Kyiv, Ukraine).

The data collection was fully supported by Formas, Swedish government research council for sustainable development (https://formas.se/en/). This project, 'Emergency action to collect data in war-affected Scots pine forests in Ukraine', aimed to collect a comprehensive data set on ground forest fuels in rich pine forests close to Kyiv City. This data set is used for fire behavior modeling.

Use file ReadMe.xlsx to examine the description of other files in this data set.

Primary file, SAMPLE_DATA.xlsx contains information about samples on transects and sub-plots within sample plots (litter and duff, dead and live fuels, also snags).

Main file, PLOT_DATA.xlsx contains summary on ground fuels calculated by FIREMON methodology.

This data set also contains PDF files with raw data materials (which may contain errors on plot ID, cleaned in final data) and ZIP-archives with photos from each plot. Every person who occasionally appeared in these research photos, provided their consent for sharing these materials.
DOI: 10.15468/dl.fx4bbn
title: Occurrence Download

description: A dataset containing 8835 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Juniperus scopulorum Sarg.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 8835 records from 137 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062909-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hk9yhj
title: Occurrence Download

description: A dataset containing 43511 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Juniperus virginiana L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 43511 records from 320 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062910-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ba3a6s
title: Occurrence Download

description: A dataset containing 1272146 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Denmark",
 "OccurrenceStatus is Present",
 "CoordinateUncertaintyInMeters is less than 50000",
 "Year is 2017",
 "DecimalLongitude is greater than or equal to 7.0",
 "DecimalLongitude is less than or equal to 11.0",
 "DecimalLatitude is greater than or equal to 54.5",
 "DecimalLatitude is less than or equal to 58.0"
 ]
}

 The dataset includes 1272146 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0062894-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.