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

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:AAD4143"
 ]
}

 The dataset includes 16 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035293-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5eu6tc
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:AAD4142"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035294-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bd76p2
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:AAY8760"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035295-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29877632
title: <b>Absence of Artificial Intelligence (AI) policy and its impact on academic integrity in higher education in Africa: A systematic review</b>

description: Globally, AI is reshaping numerous sectors, but its widespread integration is increasingly sparking public integrity concerns. Africa confronts substantial hurdles, especially in its education sector. A key issue is the revelation that most African countries lack the national policy and legal frameworks essential for guiding AI's implementation in higher education. Moreover, higher education institutions themselves are often without specific policies governing AI usage. Despite AI's broad adoption by students and likely staff, many academic personnel remain insufficiently experienced with it. Consequently, this often results in serious ethical misconduct within academic settings.
DOI: 10.6084/m9.figshare.29877632.v1
title: <b>Absence of Artificial Intelligence (AI) policy and its impact on academic integrity in higher education in Africa: A systematic review</b>

description: Globally, AI is reshaping numerous sectors, but its widespread integration is increasingly sparking public integrity concerns. Africa confronts substantial hurdles, especially in its education sector. A key issue is the revelation that most African countries lack the national policy and legal frameworks essential for guiding AI's implementation in higher education. Moreover, higher education institutions themselves are often without specific policies governing AI usage. Despite AI's broad adoption by students and likely staff, many academic personnel remain insufficiently experienced with it. Consequently, this often results in serious ethical misconduct within academic settings.
DOI: 10.5281/zenodo.15641875
title: State of Wildfires 2024-25: ConFLAME NRT Attribution Results

description: ConFLAME attribution outputs are split by region, using the same regions as in previous years, with each directory name ending in -2425 to denote the 2024/25 report version:





Amazon-2425 – Northeast Amazonia




Pantanal-2425 – Pantanal–Chiquitano




LA-2425 – Southern California




Congo-2425 – Congo Basin



Each of these directories contains the files necessary for rerunning the model without redoing the optimisation.While you may not need to view these directly, they are useful for setting up your own ConFLAME model experiments (see the ConFLAME configuration settings).

Core files:





model_graph.png – Graphical representation of the trained model




scalers-*.csv – Scaling parameters used for model input features




trace-*.nc – Posterior traces from the Bayesian optimisation




none_trace-params-*.txt – Parameter values without the trace




variables_info-*.txt – List of variables used in the model and their definitions




namelist_*.txt – Model configuration files



Core directories:





figs – Automatically generated evaluation figures and selected output plots




time_series – Burned area (BA) time series for attribution experiments.Structure:



 

<<model_id>>/<<experiment>>/<<range>>/<<members or percentiles>>/<<metric>>/<<variable>>.csv







Model ID: currently only _19-frac_points_0.5




Experiments:





factual – Simulations using observed climate conditions




counterfactual – Simulations with climate change signal removed




counterfactual-metmean – Counterfactuals using mean meteorological differences (HadGEM3 ALL–NAT) as in Barbosa et al. (submitted)






Range:





mean – Regional mean burned area




pc-0.95 – Burned area for grid cells in the top 5% of BA values






Members or Percentiles:





members – Time series for individual ensemble members from the sampled posterior




percentiles – Precomputed percentile ranges (e.g., 5th–95th) over all members






Metric:





absolute – Burned area in km² or equivalent units




climatology – Long-term monthly mean burned area




anomaly – Deviation from climatology for that month




ratio – Burned area divided by climatology






Variables:





Evaluate.csv – BA simulated with stochasticity (used in the State of Wildfires report)




Control.csv – BA simulated without stochasticity








samples – Contains full spatial samples from the posterior for each experiment:







 



<<model_id>>/<<experiment>>/<<variable>>/sample-predXXX.nc







Variables align with those in the time_series directory.




Pairing: Samples are paired across experiments within the same optimisation, meaning sample-predX.nc in the factual experiment matches sample-predX.nc in the counterfactual experiment.
DOI: 10.5281/zenodo.15641876
title: State of Wildfires 2024-25: ConFLAME NRT Attribution Results

description: ConFLAME attribution outputs are split by region, using the same regions as in previous years, with each directory name ending in -2425 to denote the 2024/25 report version:





Amazon-2425 – Northeast Amazonia




Pantanal-2425 – Pantanal–Chiquitano




LA-2425 – Southern California




Congo-2425 – Congo Basin



Each of these directories contains the files necessary for rerunning the model without redoing the optimisation.While you may not need to view these directly, they are useful for setting up your own ConFLAME model experiments (see the ConFLAME configuration settings).

Core files:





model_graph.png – Graphical representation of the trained model




scalers-*.csv – Scaling parameters used for model input features




trace-*.nc – Posterior traces from the Bayesian optimisation




none_trace-params-*.txt – Parameter values without the trace




variables_info-*.txt – List of variables used in the model and their definitions




namelist_*.txt – Model configuration files



Core directories:





figs – Automatically generated evaluation figures and selected output plots




time_series – Burned area (BA) time series for attribution experiments.Structure:



 

<<model_id>>/<<experiment>>/<<range>>/<<members or percentiles>>/<<metric>>/<<variable>>.csv







Model ID: currently only _19-frac_points_0.5




Experiments:





factual – Simulations using observed climate conditions




counterfactual – Simulations with climate change signal removed




counterfactual-metmean – Counterfactuals using mean meteorological differences (HadGEM3 ALL–NAT) as in Barbosa et al. (submitted)






Range:





mean – Regional mean burned area




pc-0.95 – Burned area for grid cells in the top 5% of BA values






Members or Percentiles:





members – Time series for individual ensemble members from the sampled posterior




percentiles – Precomputed percentile ranges (e.g., 5th–95th) over all members






Metric:





absolute – Burned area in km² or equivalent units




climatology – Long-term monthly mean burned area




anomaly – Deviation from climatology for that month




ratio – Burned area divided by climatology






Variables:





Evaluate.csv – BA simulated with stochasticity (used in the State of Wildfires report)




Control.csv – BA simulated without stochasticity








samples – Contains full spatial samples from the posterior for each experiment:







 



<<model_id>>/<<experiment>>/<<variable>>/sample-predXXX.nc







Variables align with those in the time_series directory.




Pairing: Samples are paired across experiments within the same optimisation, meaning sample-predX.nc in the factual experiment matches sample-predX.nc in the counterfactual experiment.
DOI: 10.15468/dl.pwgen4
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:AEC9410"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035296-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5sgts9
title: Occurrence Download

description: A dataset containing 428 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Searsia koefoedi"
 ]
}

 The dataset includes 428 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035297-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wstfs9
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:AAD1434"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035298-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hb9sdx
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:AAC8017"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035299-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3rvxuy
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Searsia koefoedi primicrops"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035300-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4myfv9
title: Occurrence Download

description: A dataset containing 1861983410 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2015-2025"
 ]
}

 The dataset includes 1861983410 records from 38352 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035268-250802193616735/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/jrbm-c91-656
title: Kankai ibun, vol. 6, 1807

description: Kankai ibun, [1807], volume 6 = Description and travel of circumnavigation of seas, [1807], volume 6 = 環海 異聞 六 文化 4. Volume six of a manuscript by Gentaku Ōtsuki and Kōkyō Shimura who drew from four Japanese fishermen the story of their voyage around the world during repeated interviews. The Kankai Ibun is an account of their journey when they were sent off-course en route to the capital, Edo. The fishermen were rescued by a Russian ship and taken to late 18th / early 19th century Europe and, most especially, Russia.; The cargo ship, Wakamiyamaru, left from the port of Ishimaki in Sendai-han, today’s Miyagi Prefecture, in Japan carrying rice and lumber with 16 crew members for Edo, today’s Tokyo, on November 27th, 1793. Shipwrecked and drifted in the Pacific Ocean for half a year, they finally landed in one of the islands of the Andreanof Islands, a part of the Aleutian Islands on May 10th, 1794. They were transferred to Irkutsk by Russians where they stayed until the spring of 1803. They were then called in by Alexander I of Russia to St. Petersburg where they stayed from April to June in 1803, and from where they started their journey back home. They were included in the Rezanov mission on the first Russian global circumnavigation by a ship Nadezhda. It took them 16 months to reach the port of Nagasaki, Japan in September, 1804 since they left in June, 1803. Of the sixteen, five crewmen got on board. Four returned home in Sendai; one, who had become an interpreter, returned to Russia with Rezanov.
DOI: 10.25549/inscriptifact-ouc110591385
title: Persepolis Fortification Aramaic Text 253, PFAT 253

description: [Text Description:] Administrative text with seals PFS 0578s; PFATS 0268; PFATS 0004s ❧ [Text or Publication Number Note:] PFAT 253, Bowman 253 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Aramaic ❧ [Physical Object Description:] Clay tablet with incised inscription and ink traces. Seal impressions PFS 0578s on upper edge; PFATS 0268 on reverse; PFATS 0004s on flat edge.
DOI: 10.25549/inscriptifact-ouc110594913
title: Persepolis Fortification Anepigraphic Tablet 0855.105, PFAnep 0855.105

description: [Text Description:] Administrative text with seal PFUTS 0011. ❧ [Text or Publication Number Note:] PFAnep 0855.105, PFUT 0855-105 ❧ [Excavation Description:] Found during Oriental Institute's excavations in Spring, 1933, near the northeastern corner of the stone platform of Persepolis, in two small rooms of a bastion in the fortification wall ❧ [Medium:] Clay ❧ [Script Note:] Non-Text ❧ [Physical Object Description:] Clay tablet with seal impression PFUTS 0011 on left edge.
DOI: 10.5255/ukda-sn-7573-4
title: OECD Economic Outlook: Statistics and Projections, 1960-2022
title: Economic Outlook

description: &lt;p&gt;The OECD Economic Outlook analyses the major economic trends over the coming 2 years. It provides in-depth coverage of the main economic issues and the policy measures required to foster growth in each member country. Forthcoming developments in selected non-OECD economies are also evaluated in detail. Each edition of the Outlook provides a unique resource to keep abreast of world economic developments. The OECD Economic Outlook database is a comprehensive and consistent macroeconomic database of the OECD economies, covering expenditures, foreign trade, output, labour markets, interest and exchange rates, balance of payments, and government debt. For the non-OECD regions, foreign trade and current account series are available.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;The database contains annual data (for all variables) and quarterly figures (for a subset of variables). Variables are defined in such a way that they are as homogenous as possible for the countries covered. Breaks in underlying series are corrected as far as possible. Sources for the historical data are publications of national statistical agencies and OECD databases such as Quarterly National Accounts, Annual National Accounts, Labour Force Statistics and Main Economic Indicators.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;Concerning the aggregation of world trade, a new composition has been introduced, since projections are now made for selected non-OECD economies. Thus, besides OECD and the OECD Euro Area, the following new regions are available: Dynamic Asian Economies (Chinese Taipei, Hong Kong, Malaysia, the Philippines, Singapore, Thailand, Vietnam); Oil Producers (Azerbaijan, Kazakhstan, Turkmenistan, Brunei, Timor-Leste, Bahrain, Iran, Iraq, Kuwait, Libya, Oman, Qatar, Saudi Arabia, United Arab Emirates, Yemen, Ecuador, Trinidad and Tobago, Venezuela, Algeria, Angola, Chad, Rep. of Congo, Equatorial Guinea, Gabon, Nigeria, Sudan); with the remaining countries in a residual 'Rest of the World' group.&lt;/p&gt;
DOI: 10.5255/ukda-sn-8206-4
title: Low Carbon and Renewable Energy Economy Survey, 2014-2018: Secure Access
title: LCRES; UK LCRE; LCREE Survey; LCREES

description: The &lt;em&gt;Low Carbon and Renewable Energy Economy Survey&lt;/em&gt; (LCRES) is an annual survey designed to collect information from businesses working within the green economy, including low carbon and renewable energy activities. UK government departments and devolved administrations will use this information to assess and develop policies relating to green job creation, potential growth and investment opportunities both nationally and regionally. The LCRES was conducted for the first time in 2015, for the reporting year 2014. Continuity between the first and second year of the survey was ensured with minimal changes made to the questionnaire in the second year.&lt;br&gt; &lt;br&gt; The survey provides a number of high-level indicators of Low Carbon and Renewable Energy Economy (LCREE) activity such as turnover, number of businesses, imports, exports, employees and capital assets. Results from the LCRES can be used to show business activity in 17 specific sectors which can be aggregated into 6 LCREE groups.&lt;br&gt; &lt;br&gt; All businesses with an employment of 250 or above are selected, together with a random sample of businesses from each of the other strata, defined by 2-digit SIC 2007 industry classification, country and employment size-band. Businesses that are randomly sampled are generally expected to remain in the sample for 2 years.&lt;br&gt; &lt;br&gt; Further information about the LCRES is available on the &lt;a href="https://www.ons.gov.uk/economy/environmentalaccounts/qmis/lowcarbonandrenewableenergyeconomylcreesurvey"&gt;ONS&lt;/a&gt; web pages.&lt;br&gt; &lt;br&gt; &lt;strong&gt;Latest Edition Information&lt;/strong&gt;&lt;br&gt; For the fifth edition (March 2021), the 2014-2017 data files have been updated, and a new data file for 2018 has been added. The documentation has also been updated with a variable catalogue and a user guide covering 2014-2018.&lt;br&gt;
DOI: 10.15139/s3/uyxdkc
title: Data Management and Sharing Plan for: Hypoxia Signaling: A Therapeutic Target for Cystic Fibrosis Lung Disease

description: The Data Management and Sharing Plan describes the scientific data to be generated and/or used in the research and outlines a strategy for managing and sharing project data.
description: Data Management and Sharing Plan
DOI: 10.25549/jrbm-c91-1318
title: Kankai ibun, vol. 7, 1807

description: Kankai ibun, [1807], volume 7 = Description and travel of circumnavigation of seas, [1807], volume 7 = 環海 異聞 七 文化 4. Volume seven of a manuscript by Gentaku Ōtsuki and Kōkyō Shimura who drew from four Japanese fishermen the story of their voyage around the world during repeated interviews. The Kankai Ibun is an account of their journey when they were sent off-course en route to the capital, Edo. The fishermen were rescued by a Russian ship and taken to late 18th / early 19th century Europe and, most especially, Russia.; The cargo ship, Wakamiyamaru, left from the port of Ishimaki in Sendai-han, today’s Miyagi Prefecture, in Japan carrying rice and lumber with 16 crew members for Edo, today’s Tokyo, on November 27th, 1793. Shipwrecked and drifted in the Pacific Ocean for half a year, they finally landed in one of the islands of the Andreanof Islands, a part of the Aleutian Islands on May 10th, 1794. They were transferred to Irkutsk by Russians where they stayed until the spring of 1803. They were then called in by Alexander I of Russia to St. Petersburg where they stayed from April to June in 1803, and from where they started their journey back home. They were included in the Rezanov mission on the first Russian global circumnavigation by a ship Nadezhda. It took them 16 months to reach the port of Nagasaki, Japan in September, 1804 since they left in June, 1803. Of the sixteen, five crewmen got on board. Four returned home in Sendai; one, who had become an interpreter, returned to Russia with Rezanov.
DOI: 10.5255/ukda-sn-6533-16
title: National Diet and Nutrition Survey Years 1-11, 2008-2019
title: National Diet and Nutrition Surveys
title: NDNS RP

description: &lt;p&gt;The National Diet and Nutrition Survey (NDNS) Rolling Programme (RP) began in 2008 and is designed to assess the diet, nutrient intake and nutritional status of the general population aged 1.5 years and over living in private households in the UK. (For details of the previous NDNS series, which began in 1992, see the documentation for studies 3481, 4036, 4243 and 5140.)&lt;/p&gt;&lt;p&gt;The NDNS RP is funded by Public Health England (PHE), an executive agency of the Department of Health, and the UK Food Standards Agency (FSA) and is currently carried out by a consortium comprising NatCen Social Research (NatCen) and the National Institute of Health Research Cambridge Biomedical Research Centre (NIHR BRC). Scientific leadership for the RP from Year 11 (2018/19) transferred to NIHR BRC from the Medical Research Council Elsie Widdowson Laboratory (MRC EWL).&lt;/p&gt;&lt;p&gt;The NDNS RP provides the only source of high quality, nationally representative UK data on the types and quantities of foods consumed by individuals, from which estimates of nutrient intake for the population are derived. Results are used by Government to develop policy and monitor progress toward diet and nutrition objectives of UK Health Departments, for example work to tackle obesity and monitor progress towards a healthy, balanced diet as visually depicted in the &lt;a title="Eatwell Guide" href="https://www.gov.uk/government/publications/the-eatwell-guide"&gt;Eatwell Guide&lt;/a&gt;. The NDNS RP provides an important source of evidence underpinning the &lt;a title="Scientific Advisory Committee on Nutrition" href="https://www.gov.uk/government/groups/scientific-advisory-committee-on-nutrition"&gt;Scientific Advisory Committee on Nutrition&lt;/a&gt; (SACN) work relating to national nutrition policy. The food consumption data are also used by the FSA to assess exposure to chemicals in food, as part of the risk assessment and communication process in response to a food emergency or to inform negotiations on setting regulatory limits for contaminants.&lt;/p&gt;&lt;p&gt;Further information is available from the gov.uk &lt;a title="National Diet and Nutrition Survey" href="https://www.gov.uk/government/collections/national-diet-and-nutrition-survey"&gt;National Diet and Nutrition Survey&lt;/a&gt; webpage.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt; &lt;p&gt;The study currently includes data and documentation covering Years 1-11 of the NDNS RP.&lt;br&gt;&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;For the 16th edition (March 2021), Years 10 and 11 data and documentation were added to the study. The Year 9 standalone datasets have been replaced by combined Years 9-11 individual-level, household-level and person-level dietary datasets; Years 9, 10 and 11 food level dietary datasets are presented as separate files due to their size.&lt;/p&gt;