cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.rdqw6p
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.89901999999999 24.191159, 88.822039 24.185582, 88.74697399999999 24.16899, 88.67568900000001 24.141795, 88.609955 24.104674, 88.55139699999999 24.058548, 88.501462 24.004562, 88.461376 23.944054, 88.432115 23.878522, 88.414388 23.809585, 88.40861099999999 23.738945, 88.41491000000001 23.668343, 88.433109 23.599515, 88.462743 23.534152, 88.50306999999999 23.473857, 88.553087 23.420107, 88.61156200000001 23.374218, 88.67705599999999 23.33731, 88.747967 23.310284, 88.822562 23.2938, 88.89901999999999 23.288261, 88.975478 23.2938, 89.050073 23.310284, 89.12098400000001 23.33731, 89.18647799999999 23.374218, 89.244953 23.420107, 89.29497000000001 23.473857, 89.335297 23.534152, 89.364931 23.599515, 89.38312999999999 23.668343, 89.38942900000001 23.738945, 89.383652 23.809585, 89.365925 23.878522, 89.336664 23.944054, 89.296578 24.004562, 89.24664300000001 24.058548, 89.188085 24.104674, 89.12235099999999 24.141795, 89.05106600000001 24.16899, 88.976001 24.185582, 88.89901999999999 24.191159))"},{"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/0003914-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/alk-prd-mbv
title: Chordata

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/q7b-ler-zah
title: Cervidae Goldfuss, 1820

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/216-jf7-yps
title: Proboscidea Illiger, 1811

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/03y-ajw-273
title: Echinoidea Schumacher, 1817

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/4hj-7z8-ztn
title: Elephantidae Gray, 1821

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.15468/dl.abxxmc
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((98.39700000000001 3.946166, 98.326572 3.940596, 98.25788 3.924024, 98.19261899999999 3.89686, 98.132397 3.859771, 98.078699 3.813674, 98.032847 3.759704, 97.995968 3.699192, 97.968971 3.633628, 97.952518 3.564628, 97.947012 3.493892, 97.952585 3.42316, 97.969098 3.354176, 97.996143 3.288635, 98.033051 3.228153, 98.078914 3.174216, 98.13260200000001 3.128151, 98.19279299999999 3.091092, 98.25800700000001 3.063951, 98.326638 3.047394, 98.39700000000001 3.04183, 98.46736199999999 3.047394, 98.535993 3.063951, 98.601207 3.091092, 98.66139800000001 3.128151, 98.715086 3.174216, 98.760949 3.228153, 98.79785699999999 3.288635, 98.82490199999999 3.354176, 98.841415 3.42316, 98.846988 3.493892, 98.841482 3.564628, 98.825029 3.633628, 98.79803200000001 3.699192, 98.76115299999999 3.759704, 98.715301 3.813674, 98.661603 3.859771, 98.601381 3.89686, 98.53612 3.924024, 98.467428 3.940596, 98.39700000000001 3.946166))"},{"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 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003915-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18332491
title: National scale sub-meter mangrove mapping using an augmented border training sample method

description: This study proposes a novel Semi-automatic Sub-meter Mapping Method (SSMM), which incorporates an innovative automated sample collection strategy to obtain sufficient and spatially representative training samples. The method integrates Sentinel-2 and Google Earth imagery and selects nine key features to enhance the spectral separability between mangroves and other land-cover types. Based on this framework, the first Large-scale Sub-meter Mangrove Map (LSMM) was produced. Based on this map, the total mangrove area in China is estimated at 28,253 ha, and 40,035 mangrove patches are identified. The LSMM achieved an overall accuracy of 97.56% and an F1 score of 0.98, demonstrating significant advantages in boundary delineation and in detecting fragmented and small patches. The LSMM provides reliable fine-scale spatial information to support national-scale mangrove monitoring and coastal ecosystem management.
DOI: 10.5281/zenodo.18332492
title: National scale sub-meter mangrove mapping using an augmented border training sample method

description: This study proposes a novel Semi-automatic Sub-meter Mapping Method (SSMM), which incorporates an innovative automated sample collection strategy to obtain sufficient and spatially representative training samples. The method integrates Sentinel-2 and Google Earth imagery and selects nine key features to enhance the spectral separability between mangroves and other land-cover types. Based on this framework, the first Large-scale Sub-meter Mangrove Map (LSMM) was produced. Based on this map, the total mangrove area in China is estimated at 28,253 ha, and 40,035 mangrove patches are identified. The LSMM achieved an overall accuracy of 97.56% and an F1 score of 0.98, demonstrating significant advantages in boundary delineation and in detecting fragmented and small patches. The LSMM provides reliable fine-scale spatial information to support national-scale mangrove monitoring and coastal ecosystem management.
DOI: 10.6084/m9.figshare.30823995
title: Electric power: a pillar for agricultural transformation in Togo

description: This study aims to analyze the effect of access to electricity on agricultural structural transformation in Togo. Autoregressive distributed Lag Error Correction Model (ARDL-ECM) was used for empirical estimations. The data used come from the World Development Indicators database and cover the period of 1980–2020. The results indicate that, in the short-term, a 1% increase of access to electricity translates into 1.128% reduction of the share of agriculture in the total GDP, a transfer of 0.018% of agricultural employment to service and industrial sectors while in the long-term, a 1% increase of access to electricity leads to 0.591% decrease of the share of agriculture in the total GDP and 0.25% transfer of agricultural labor to the service and industrial sectors. These results confirm the pivotal role of rural population access to electricity in the agricultural transformation process. Therefore, policies aiming to boost agricultural transformation in Togo should prioritize electrification in high-potential agricultural regions, ensuring not only access but also reliability, affordability, and quality of supply.
DOI: 10.6084/m9.figshare.30823995.v1
title: Electric power: a pillar for agricultural transformation in Togo

description: This study aims to analyze the effect of access to electricity on agricultural structural transformation in Togo. Autoregressive distributed Lag Error Correction Model (ARDL-ECM) was used for empirical estimations. The data used come from the World Development Indicators database and cover the period of 1980–2020. The results indicate that, in the short-term, a 1% increase of access to electricity translates into 1.128% reduction of the share of agriculture in the total GDP, a transfer of 0.018% of agricultural employment to service and industrial sectors while in the long-term, a 1% increase of access to electricity leads to 0.591% decrease of the share of agriculture in the total GDP and 0.25% transfer of agricultural labor to the service and industrial sectors. These results confirm the pivotal role of rural population access to electricity in the agricultural transformation process. Therefore, policies aiming to boost agricultural transformation in Togo should prioritize electrification in high-potential agricultural regions, ensuring not only access but also reliability, affordability, and quality of supply.
DOI: 10.6084/m9.figshare.30692475
title: Information and communication technology (ICT) and agricultural sector productivity: empirical evidence from Somalia

description: Agriculture is the backbone of Somalia’s economy, contributing more than 75% of GDP and 93% of total revenue. Using an Autoregressive Distributed Lag (ARDL) model covering the period from 1990 through 2019, this study examines the relationship between ICT adoption and agricultural productivity in Somalia. The results show that ICT has a positive and significant relationship with agricultural productivity, indicating that ICT adoption contributes to higher production efficiency, greater land productivity, and improved labour productivity. Conversely, environmental degradation and population growth adversely affect agricultural productivity, suggesting that increased emissions and growing populations negatively impact the sector. The short-term results of the error correction model (ECM) indicate that ICT and population growth show a statistically significant positive correlation with agricultural productivity in the short term. In contrast, environmental degradation shows a positive and statistically significant relationship with agricultural productivity. This might be due to short-term adaptation strategies or increased input use. The study suggests important policy implications for stimulating agricultural development in Somalia. The government should promote investment in ICT-related infrastructure in Somalia, as this has the potential to enhance the performance of the country’s agricultural sector.
DOI: 10.6084/m9.figshare.30692475.v1
title: Information and communication technology (ICT) and agricultural sector productivity: empirical evidence from Somalia

description: Agriculture is the backbone of Somalia’s economy, contributing more than 75% of GDP and 93% of total revenue. Using an Autoregressive Distributed Lag (ARDL) model covering the period from 1990 through 2019, this study examines the relationship between ICT adoption and agricultural productivity in Somalia. The results show that ICT has a positive and significant relationship with agricultural productivity, indicating that ICT adoption contributes to higher production efficiency, greater land productivity, and improved labour productivity. Conversely, environmental degradation and population growth adversely affect agricultural productivity, suggesting that increased emissions and growing populations negatively impact the sector. The short-term results of the error correction model (ECM) indicate that ICT and population growth show a statistically significant positive correlation with agricultural productivity in the short term. In contrast, environmental degradation shows a positive and statistically significant relationship with agricultural productivity. This might be due to short-term adaptation strategies or increased input use. The study suggests important policy implications for stimulating agricultural development in Somalia. The government should promote investment in ICT-related infrastructure in Somalia, as this has the potential to enhance the performance of the country’s agricultural sector.
DOI: 10.6084/m9.figshare.28931063
title: Impacts and implications of agronomic efficiency on rice and maize productivity among small-scale farmers in Chiang Mai, Thailand

description: Agricultural productivity (APL) and efficiency are crucial for Thailand’s development, yet local small-scale farmers face challenges due to lower production. This study compares the efficiency of agronomic practices between rice and maize cultivation. Utilizing survey data from 297 small-scale farmers across three districts in Chiang Mai, we employed the Frontier Production Function and integrated kernel density analysis to assess agronomic practices. Results indicate maize cultivation is slightly more efficiency than rice. Technological progress, particularly advanced labor, significantly impacts productivity in both crops. Kernel density analysis reveals clustering of efficient values around intervals associated with advanced labor practices, emphasizing the importance of promoting technological progress. Meanwhile, the concentration of KDE implies a higher density of efficiency values within these intervals, representing typical levels of agronomic practices’ efficiency or reflecting common technological and managerial practices. Tobit regression analysis shows farm size, family size, market distance and agricultural revenue are important determinants for rice productivity; by contrast, farm size and income are important factors for maize output. The study emphasizes how the efficiency of maize and rice is impacted differently by inputs and social factors. These understandings can help policymakers maximize agricultural inputs and solve social issues to improve sustainability and production.
DOI: 10.6084/m9.figshare.28931063.v1
title: Impacts and implications of agronomic efficiency on rice and maize productivity among small-scale farmers in Chiang Mai, Thailand

description: Agricultural productivity (APL) and efficiency are crucial for Thailand’s development, yet local small-scale farmers face challenges due to lower production. This study compares the efficiency of agronomic practices between rice and maize cultivation. Utilizing survey data from 297 small-scale farmers across three districts in Chiang Mai, we employed the Frontier Production Function and integrated kernel density analysis to assess agronomic practices. Results indicate maize cultivation is slightly more efficiency than rice. Technological progress, particularly advanced labor, significantly impacts productivity in both crops. Kernel density analysis reveals clustering of efficient values around intervals associated with advanced labor practices, emphasizing the importance of promoting technological progress. Meanwhile, the concentration of KDE implies a higher density of efficiency values within these intervals, representing typical levels of agronomic practices’ efficiency or reflecting common technological and managerial practices. Tobit regression analysis shows farm size, family size, market distance and agricultural revenue are important determinants for rice productivity; by contrast, farm size and income are important factors for maize output. The study emphasizes how the efficiency of maize and rice is impacted differently by inputs and social factors. These understandings can help policymakers maximize agricultural inputs and solve social issues to improve sustainability and production.
DOI: 10.6084/m9.figshare.30015490
title: Assessing farmers’ perceptions of climate change and adaptation strategies in the Yilo Krobo Municipality of the Eastern Region, Ghana

description: Limited information exists on farmers’ perceptions of climate change and adaptation strategies from a gender perspective, which impedes the development of climate-smart mango production. Therefore, this study examined farmers’ perceptions of climate change and adaptation strategies. This study used semi-structured questionnaires to capture the perspectives of 116 smallholder mango farmers in the Yilo Krobo Municipality, Ghana. Pearson’s chi-square and Fisher Exact test were used to determine the association between variables of interest. Also, binary logistic regression was used to examine the predictors of climate change awareness. All variables were considered statistically significant at <i>p</i>-value &lt; 0.05. Most farmers (92%) reported that the climate has changed for 20–30 years. Male farmers perceived changes in climatic patterns and associated risks differently than females. More male farmers reported a decrease (85.4%), an increase (52.6%), and the same (65.2%) flooding frequency compared to female farmers, respectively. Both male and female farmers employed various adaptation strategies, including diversification of crops, adjusting planting dates, and changing irrigation practices. Furthermore, farmers’ sex and belonging to a social group were significant predictors of climate change awareness. These findings highlight the need to consider gender and social dynamics when developing climate change awareness interventions.
DOI: 10.6084/m9.figshare.30015490.v1
title: Assessing farmers’ perceptions of climate change and adaptation strategies in the Yilo Krobo Municipality of the Eastern Region, Ghana

description: Limited information exists on farmers’ perceptions of climate change and adaptation strategies from a gender perspective, which impedes the development of climate-smart mango production. Therefore, this study examined farmers’ perceptions of climate change and adaptation strategies. This study used semi-structured questionnaires to capture the perspectives of 116 smallholder mango farmers in the Yilo Krobo Municipality, Ghana. Pearson’s chi-square and Fisher Exact test were used to determine the association between variables of interest. Also, binary logistic regression was used to examine the predictors of climate change awareness. All variables were considered statistically significant at <i>p</i>-value &lt; 0.05. Most farmers (92%) reported that the climate has changed for 20–30 years. Male farmers perceived changes in climatic patterns and associated risks differently than females. More male farmers reported a decrease (85.4%), an increase (52.6%), and the same (65.2%) flooding frequency compared to female farmers, respectively. Both male and female farmers employed various adaptation strategies, including diversification of crops, adjusting planting dates, and changing irrigation practices. Furthermore, farmers’ sex and belonging to a social group were significant predictors of climate change awareness. These findings highlight the need to consider gender and social dynamics when developing climate change awareness interventions.
DOI: 10.6084/m9.figshare.29991451
title: Nitrogen-fixing bacteria <i>Bradyrhizobium yuanmingense</i> SB006-1-2 and <i>Rhizobium</i> spp. S058-A2-3, carbohydrate types, and growth conditions alter the phytocompounds during soybean (<i>Glycine max</i> L.) germination: an innovative study

description: Soybeans are rich in isoflavones beneficial to humans and plants and affected by soil microbiota and nitrogen-fixing bacteria. Effects of three carbohydrates and two nitrogen-fixing <i>Bradyrhizobium yuanmingense</i> SB006-1–2 and <i>Rhizobium</i> spp. on the production of phytocompounds and isoflavonoid–like compounds were evaluated. After plating seeds on 1.5% agar in sterilized and nonsterilized soils, growth characteristics and phytocompound contents in the seedlings were investigated before and after inoculation with 1 × 10<sup>4</sup> and 1 × 10<sup>6</sup> colony formation units (CFUs) on day 5. Using six known isoflavones as standards, the levels of methanol purified phytocompounds were determined by reversed-phase HPLC analysis. During extraction, temperatures at 100 °C reduced the peaks 6 and 8, increased the peak 7, and formed a new compound of peak 4 (glycitein-like) in seed and filtered liquid extract and peak 5 (genistein-like) in seed. Glucose and maltose promoted the synthesis of daidzein-like compounds, and only <i>B. yuanmingense</i> could synthesize higher levels of daidzein- and glycitein-like compounds. Both bacterial species reduced seedling growth and moisture content, and altered phytocompound types and levels in three periods and especially better in nonsterilized soils than in sterilized soils. Furthermore, the total phytocompound content differed between RHB (roots + hypocotyls + buds) and cotyledons, and between bacterial species and doses among the three periods. The soil microbiota altered phytocompound and isoflavonoid-like compound synthesis with lower expression in nonsterilized soil than in sterilized agar and soil. In conclusion, carbohydrate types and nodule bacterial species can change phytocompounds for functional soybean seedlings.
DOI: 10.6084/m9.figshare.29991451.v1
title: Nitrogen-fixing bacteria <i>Bradyrhizobium yuanmingense</i> SB006-1-2 and <i>Rhizobium</i> spp. S058-A2-3, carbohydrate types, and growth conditions alter the phytocompounds during soybean (<i>Glycine max</i> L.) germination: an innovative study

description: Soybeans are rich in isoflavones beneficial to humans and plants and affected by soil microbiota and nitrogen-fixing bacteria. Effects of three carbohydrates and two nitrogen-fixing <i>Bradyrhizobium yuanmingense</i> SB006-1–2 and <i>Rhizobium</i> spp. on the production of phytocompounds and isoflavonoid–like compounds were evaluated. After plating seeds on 1.5% agar in sterilized and nonsterilized soils, growth characteristics and phytocompound contents in the seedlings were investigated before and after inoculation with 1 × 10<sup>4</sup> and 1 × 10<sup>6</sup> colony formation units (CFUs) on day 5. Using six known isoflavones as standards, the levels of methanol purified phytocompounds were determined by reversed-phase HPLC analysis. During extraction, temperatures at 100 °C reduced the peaks 6 and 8, increased the peak 7, and formed a new compound of peak 4 (glycitein-like) in seed and filtered liquid extract and peak 5 (genistein-like) in seed. Glucose and maltose promoted the synthesis of daidzein-like compounds, and only <i>B. yuanmingense</i> could synthesize higher levels of daidzein- and glycitein-like compounds. Both bacterial species reduced seedling growth and moisture content, and altered phytocompound types and levels in three periods and especially better in nonsterilized soils than in sterilized soils. Furthermore, the total phytocompound content differed between RHB (roots + hypocotyls + buds) and cotyledons, and between bacterial species and doses among the three periods. The soil microbiota altered phytocompound and isoflavonoid-like compound synthesis with lower expression in nonsterilized soil than in sterilized agar and soil. In conclusion, carbohydrate types and nodule bacterial species can change phytocompounds for functional soybean seedlings.