cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.63ws9h
title: Occurrence Download

description: A dataset containing 115 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Juncus articulatus",
 "Year 1970-2000"
 ]
}

 The dataset includes 115 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004153-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dk3phu
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((117.701 4.614158, 117.630511 4.608588, 117.56176 4.592015, 117.496444 4.564849, 117.436173 4.527758, 117.382432 4.481658, 117.336545 4.427686, 117.299641 4.367172, 117.272627 4.301607, 117.256167 4.232607, 117.250663 4.161871, 117.256247 4.091141, 117.272778 4.022159, 117.299849 3.956622, 117.336789 3.896143, 117.382689 3.84221, 117.436417 3.796149, 117.496652 3.759094, 117.561911 3.731955, 117.63059 3.715401, 117.701 3.709837, 117.77141 3.715401, 117.840089 3.731955, 117.905348 3.759094, 117.965583 3.796149, 118.019311 3.84221, 118.065211 3.896143, 118.102151 3.956622, 118.129222 4.022159, 118.145753 4.091141, 118.151337 4.161871, 118.145833 4.232607, 118.129373 4.301607, 118.102359 4.367172, 118.065455 4.427686, 118.019568 4.481658, 117.965827 4.527758, 117.905556 4.564849, 117.84024 4.592015, 117.771489 4.608588, 117.701 4.614158))"},{"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 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004154-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eyvhyu
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.786312 24.224227, 88.709311 24.21865, 88.634227 24.202058, 88.562924 24.174863, 88.49717200000001 24.137742, 88.43859999999999 24.091616, 88.38865300000001 24.03763, 88.348556 23.977122, 88.319289 23.91159, 88.301557 23.842653, 88.295779 23.772014, 88.30208 23.701412, 88.320284 23.632584, 88.349926 23.567222, 88.390263 23.506927, 88.440293 23.453178, 88.49878200000001 23.407289, 88.56429300000001 23.370381, 88.635222 23.343356, 88.709835 23.326872, 88.786312 23.321333, 88.86278900000001 23.326872, 88.93740200000001 23.343356, 89.008331 23.370381, 89.073842 23.407289, 89.13233099999999 23.453178, 89.182361 23.506927, 89.22269799999999 23.567222, 89.25234 23.632584, 89.270544 23.701412, 89.27684499999999 23.772014, 89.271067 23.842653, 89.25333500000001 23.91159, 89.224068 23.977122, 89.183971 24.03763, 89.134024 24.091616, 89.075452 24.137742, 89.0097 24.174863, 88.93839699999999 24.202058, 88.86331300000001 24.21865, 88.786312 24.224227))"},{"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 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004155-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25612/837.e9gpv8kza7oq
title: February 2021 Winter Storms Home Damage Assessment Data from Harris County Office of Homeland Security & Emergency Management - County- and ZCTA-level Aggregated Data

description: Data from a survey assessing the damage to homes in the Houston area after the February 2021 Texas Winter Storms. Data aggregated at the zip code and county levels.
DOI: 10.15468/dl.8ttcbb
title: Occurrence Download

description: A dataset containing 954 records in GBIF matching the query:
SELECT
 '82' AS fish_cell,
 GBIF_MGRSCode(1000, decimalLatitude, decimalLongitude, 0) AS cell, -- NO randomization
 COUNT(DISTINCT speciesKey) AS species_richness
 FROM occurrence
 WHERE hasCoordinate = TRUE
 AND speciesKey IS NOT NULL
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND decimalLatitude &gt;= -20.3845909579754
 AND decimalLatitude &lt;= -16.25916876999866
 AND decimalLongitude &gt;= 40.929903860996625
 AND decimalLongitude &lt;= 46.73072192513675
 AND "year" &gt;= 2015
 AND "year" &lt;= 2026
 AND (coordinateUncertaintyInMeters &lt;= 100 OR coordinateUncertaintyInMeters IS NULL)
 GROUP BY fish_cell, cell

 The dataset's 954 records were derived from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004142-260120142942310/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.28121279
title: Insights on household’s resilience to shocks and poverty: evidence from panel data for two emerging economies in Southeast Asia

description: Given the importance of understanding the resilience of rural households to shocks and poverty in developing countries, we consider resilience as a capacity and estimate a (latent) variable reflecting household’s resilience capacity, and we investigate how better resilience capacity can help rural households mitigate the negative impacts of shocks and improve their welfare. We use panel data of 3367 households from two emerging economies in Southeast Asia collected in three survey waves for empirical analyses. We use a generalized structural equation model to estimate the latent variable of household’s resilience capacity. Then, the results of fixed-effects estimations show that the lagged resilience capacity of rural households is significantly and negatively correlated with losses caused by different types of shocks. Moreover, the results of fixed-effects estimations with a control function approach indicate that an improvement in resilience capacity can prevent rural households from falling into poverty in absolute and multidimensional terms.
DOI: 10.6084/m9.figshare.28121279.v1
title: Insights on household’s resilience to shocks and poverty: evidence from panel data for two emerging economies in Southeast Asia

description: Given the importance of understanding the resilience of rural households to shocks and poverty in developing countries, we consider resilience as a capacity and estimate a (latent) variable reflecting household’s resilience capacity, and we investigate how better resilience capacity can help rural households mitigate the negative impacts of shocks and improve their welfare. We use panel data of 3367 households from two emerging economies in Southeast Asia collected in three survey waves for empirical analyses. We use a generalized structural equation model to estimate the latent variable of household’s resilience capacity. Then, the results of fixed-effects estimations show that the lagged resilience capacity of rural households is significantly and negatively correlated with losses caused by different types of shocks. Moreover, the results of fixed-effects estimations with a control function approach indicate that an improvement in resilience capacity can prevent rural households from falling into poverty in absolute and multidimensional terms.
DOI: 10.6084/m9.figshare.25868702.v1
title: Seasonal deprivation in the Sahel is large, widespread, and can be anticipated

description: Shocks and seasonality may have profound effects on poor households’ wellbeing, especially in contexts such as the Sahel where livelihoods depend on rain-fed agriculture and pastoralism. Understanding how seasonal variation affects Sahelian households is therefore essential for guiding policies that jointly seek to address chronic poverty, seasonality, and unexpected shocks. This paper uses harmonized household survey data from Burkina Faso, Mali, Niger, Senegal, and Chad, collected in two distinct waves in 2018 and 2019, to examine the extent of seasonal deprivation in the Sahel. These data reveal significant seasonal variation in poverty and wellbeing. Mean real monetary consumption is around 10.5 percent lower in the lean season. Moreover, rather than representing a reduction in dietary diversity, this drop is concentrated in staple foods (especially cereals), implying seasonality brings about extreme forms of deprivation. Therefore, while initiatives currently focus on responding to unpredictable shocks, seasonal food insecurity could be better tackled by expanding social protection and providing regular transfers early in the lean season, when prices are lower and fewer households have succumbed to extreme deprivation. Seasonal variation happens every year and more can be done to support Sahelian households if the perennial threat it poses to their wellbeing is recognized.
DOI: 10.6084/m9.figshare.26200212
title: Impact of economic growth, energy consumption, and trade openness on carbon emissions: evidence from the top 20 emitting nations

description: The study focuses on the top 20 carbon emission-increasing nations across continents from 2000 to 2021 and the effects of gross domestic product, energy consumption, and trade openness on carbon emissions. The study uses a panel dataset and multiple linear regression analysis to pinpoint the significant factors influencing each nation's carbon emissions. The findings indicate that China, Kazakhstan, Saudi Arabia, and South Korea in Asia; Algeria, Egypt, Morocco, and the Seychelles in Africa; Antigua and Barbuda, Bolivia, Chile, and Panama in America; Albania, Belarus, Lithuania, and Russia in Europe; and Fiji, Samoa, Tonga, and Vanuatu in Oceania have a highly significant impact on carbon emissions in their respective regions. Energy consumption significantly increases carbon emissions in all countries except Panama and Kazakhstan, where it only significantly impacts GDP-related carbon emissions. These insights lay the groundwork for policymakers to prioritise sustainable development, reduce carbon emissions in their decision-making processes, and establish comprehensive strategies that reconcile ecological concerns with socioeconomic goals by understanding the intricate dynamics between gross domestic product, energy use, trade openness, and carbon emissions.
DOI: 10.6084/m9.figshare.26200212.v1
title: Impact of economic growth, energy consumption, and trade openness on carbon emissions: evidence from the top 20 emitting nations

description: The study focuses on the top 20 carbon emission-increasing nations across continents from 2000 to 2021 and the effects of gross domestic product, energy consumption, and trade openness on carbon emissions. The study uses a panel dataset and multiple linear regression analysis to pinpoint the significant factors influencing each nation's carbon emissions. The findings indicate that China, Kazakhstan, Saudi Arabia, and South Korea in Asia; Algeria, Egypt, Morocco, and the Seychelles in Africa; Antigua and Barbuda, Bolivia, Chile, and Panama in America; Albania, Belarus, Lithuania, and Russia in Europe; and Fiji, Samoa, Tonga, and Vanuatu in Oceania have a highly significant impact on carbon emissions in their respective regions. Energy consumption significantly increases carbon emissions in all countries except Panama and Kazakhstan, where it only significantly impacts GDP-related carbon emissions. These insights lay the groundwork for policymakers to prioritise sustainable development, reduce carbon emissions in their decision-making processes, and establish comprehensive strategies that reconcile ecological concerns with socioeconomic goals by understanding the intricate dynamics between gross domestic product, energy use, trade openness, and carbon emissions.
DOI: 10.6084/m9.figshare.25868702
title: Seasonal deprivation in the Sahel is large, widespread, and can be anticipated

description: Shocks and seasonality may have profound effects on poor households’ wellbeing, especially in contexts such as the Sahel where livelihoods depend on rain-fed agriculture and pastoralism. Understanding how seasonal variation affects Sahelian households is therefore essential for guiding policies that jointly seek to address chronic poverty, seasonality, and unexpected shocks. This paper uses harmonized household survey data from Burkina Faso, Mali, Niger, Senegal, and Chad, collected in two distinct waves in 2018 and 2019, to examine the extent of seasonal deprivation in the Sahel. These data reveal significant seasonal variation in poverty and wellbeing. Mean real monetary consumption is around 10.5 percent lower in the lean season. Moreover, rather than representing a reduction in dietary diversity, this drop is concentrated in staple foods (especially cereals), implying seasonality brings about extreme forms of deprivation. Therefore, while initiatives currently focus on responding to unpredictable shocks, seasonal food insecurity could be better tackled by expanding social protection and providing regular transfers early in the lean season, when prices are lower and fewer households have succumbed to extreme deprivation. Seasonal variation happens every year and more can be done to support Sahelian households if the perennial threat it poses to their wellbeing is recognized.
DOI: 10.6084/m9.figshare.28464245
title: (Mal)adaptive outcomes of farmers’ adaptation strategies to climate change: comparison of three regions from Turkey

description: Despite growing interest in research on climate change adaptation in agriculture, scant attention has been paid to maladaptation, which primarily arises from the unintended adaptation outcomes. This paper seeks to explore the adaptive and maladaptive consequences of adaptation strategies employed by differently situated farmers in three selected regions of Turkey (Mersin, Afyonkarahisar, and Şanlıurfa). It introduces the framework of Cyclical (Mal)adaptive Continuum (CMC), which places adaptation outcomes on a cyclical continuum ranging from successful adaptation to maladaptation, including those in between. The research relies on fieldwork (2021-2023), involving survey interviews with 111 farmers, in-depth interviews with 53 farmers, and 29 expert interviews with agricultural institutions in selected zones. The findings indicate that: (a) farmers’ predominating adaptation behaviours lean towards the maladaptive end of the continuum; (b) modern irrigation systems are the only structured adaptation strategy that approcahes the adaptive end; (c) autonomous adaptation strategies are partially effective, while TARSİM (agricultural insurance pool) serves as a neutral or inadequate measure, tending towards maladaptation; and (d) the root causes of these maladaptive trends stem from path dependency due to years of industrial agriculture reliance, the unintended consequences of the Southeastern Anatolia Project (GAP), and farmers’ short-term behavioural responses.
DOI: 10.6084/m9.figshare.28464245.v1
title: (Mal)adaptive outcomes of farmers’ adaptation strategies to climate change: comparison of three regions from Turkey

description: Despite growing interest in research on climate change adaptation in agriculture, scant attention has been paid to maladaptation, which primarily arises from the unintended adaptation outcomes. This paper seeks to explore the adaptive and maladaptive consequences of adaptation strategies employed by differently situated farmers in three selected regions of Turkey (Mersin, Afyonkarahisar, and Şanlıurfa). It introduces the framework of Cyclical (Mal)adaptive Continuum (CMC), which places adaptation outcomes on a cyclical continuum ranging from successful adaptation to maladaptation, including those in between. The research relies on fieldwork (2021-2023), involving survey interviews with 111 farmers, in-depth interviews with 53 farmers, and 29 expert interviews with agricultural institutions in selected zones. The findings indicate that: (a) farmers’ predominating adaptation behaviours lean towards the maladaptive end of the continuum; (b) modern irrigation systems are the only structured adaptation strategy that approcahes the adaptive end; (c) autonomous adaptation strategies are partially effective, while TARSİM (agricultural insurance pool) serves as a neutral or inadequate measure, tending towards maladaptation; and (d) the root causes of these maladaptive trends stem from path dependency due to years of industrial agriculture reliance, the unintended consequences of the Southeastern Anatolia Project (GAP), and farmers’ short-term behavioural responses.
DOI: 10.6084/m9.figshare.27132121
title: Unveiling Indian farmers’ adoption of climate information services for informed decision-making: a path to agricultural resilience

description: Amidst the intensifying ramifications of climate change on economic conditions, there exists an imperative for farmers to adopt climate forecast information in agricultural decision-making proactively. The research employed an extended theory of planned behaviour to examine the factors influencing farmers’ intentions to incorporate climate information in their decision-making process. Data from 343 male farmers in India were collected using a multi-stage sampling technique and analysed through Partial Least Squares Structural Equation Modelling (PLS-SEM). The results confirm that attitude, subjective norms, perceived behavioural control, perceived benefits and epistemic values significantly and positively affect the farmers’ intention to use climate forecasts in making informed decisions in farming. The findings carry significant importance for advancing adaptation strategies within the agricultural sector, calling for essential measures like fostering the development of farmers’ associations and promoting fruitful collaboration in climate prediction initiatives. Such actions are instrumental in equipping farmers with the necessary tools and knowledge to tackle climate-related challenges and ensure sustainable agricultural practices effectively.
DOI: 10.6084/m9.figshare.27132121.v1
title: Unveiling Indian farmers’ adoption of climate information services for informed decision-making: a path to agricultural resilience

description: Amidst the intensifying ramifications of climate change on economic conditions, there exists an imperative for farmers to adopt climate forecast information in agricultural decision-making proactively. The research employed an extended theory of planned behaviour to examine the factors influencing farmers’ intentions to incorporate climate information in their decision-making process. Data from 343 male farmers in India were collected using a multi-stage sampling technique and analysed through Partial Least Squares Structural Equation Modelling (PLS-SEM). The results confirm that attitude, subjective norms, perceived behavioural control, perceived benefits and epistemic values significantly and positively affect the farmers’ intention to use climate forecasts in making informed decisions in farming. The findings carry significant importance for advancing adaptation strategies within the agricultural sector, calling for essential measures like fostering the development of farmers’ associations and promoting fruitful collaboration in climate prediction initiatives. Such actions are instrumental in equipping farmers with the necessary tools and knowledge to tackle climate-related challenges and ensure sustainable agricultural practices effectively.
DOI: 10.6084/m9.figshare.31120534.v1
title: Jiangsu Elderly Hypertension Medication Adherence Study (JEHMAS) - Dataset

description: This dataset is a pilot collection from the "Jiangsu Elderly Hypertension Medication Adherence Study (JEHMAS)". The project investigates the socio-economic and clinical factors influencing medication adherence among elderly patients (aged 60+) in Jiangsu province, China. The study aims to support better geriatric care and hypertension management strategies.
DOI: 10.15468/dl.xzcccy
title: Occurrence Download

description: A dataset containing 2363 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Hong Kong",
 "TaxonKey is Vespa"
 ]
}

 The dataset includes 2363 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004159-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3zv58u
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((117.443 4.625158, 117.37251 4.619588, 117.303758 4.603015, 117.238441 4.575848, 117.178169 4.538758, 117.124427 4.492658, 117.078539 4.438686, 117.041635 4.378172, 117.014621 4.312607, 116.998161 4.243607, 116.992656 4.172871, 116.99824 4.102141, 117.014773 4.033159, 117.041843 3.967622, 117.078784 3.907143, 117.124684 3.85321, 117.178414 3.807149, 117.238649 3.770094, 117.303909 3.742955, 117.372589 3.726401, 117.443 3.720837, 117.513411 3.726401, 117.582091 3.742955, 117.647351 3.770094, 117.707586 3.807149, 117.761316 3.85321, 117.807216 3.907143, 117.844157 3.967622, 117.871227 4.033159, 117.88776 4.102141, 117.893344 4.172871, 117.887839 4.243607, 117.871379 4.312607, 117.844365 4.378172, 117.807461 4.438686, 117.761573 4.492658, 117.707831 4.538758, 117.647559 4.575848, 117.582242 4.603015, 117.51349 4.619588, 117.443 4.625158))"},{"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/0004161-260120142942310/datasets/export for details.