cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/vtvmrzs9ch.1
title: Dataset of the performance of Ov16 RDTs versus skin snip microscopy and Ov16 ELISA, Gabon, 2015

description: Skin snip biopsies remain the gold standard for the diagnosis of active infection with Onchocerca volvulus. However, in low-prevalence settings, such as after several rounds of mass drug administration of ivermectin, the sensitivity of skin snipping is low, and the procedure is poorly tolerated.
Detection of Ov16 IgG4 antibodies from whole blood or dried blood spots (DBS) via rapid diagnostic test (RDT) or enzyme linked immunosorbent assay (ELISA), respectively, may therefore provide a more sensitive tool for the assessment of elimination (interruption) of transmission (EoT). RDTs require little training and resources to deploy and are therefore favoured by policy makers in Lower-Middle Income Countries (LMICs).
This dataset was compiled from individual-based surveys conducted across 67 villages in Gabon in 2015, aiming to compare the performance of skin snipping versus that of Ov16 RDTs and ELISAs.
Gabon had not received MDA but features many implementation units (IU) with hypoendemic (&lt;35% microfilarial prevalence) transmission. Local healthcare centres conducted the surveys. The initial participant pool totaled 5,829 individuals, with a median age of 30 years (Interquartile Range (IQR): 11-55 years).
Skin snips were obtained from 4,350 individuals (sheet 1), and RDTs were performed on blood samples collected from 4,257 individuals.
337 individuals (8%) gave a positive skin snip, with 383 individuals (9%) recording a positive Ov16 RDT. Of these, 174 individuals tested positive in both tests, giving the RDT an overall sensitivity of 52% and specificity of 95%.
Sheet 2 shows the data aggregated at the village level. Sheet 3 shows the Ov16 ELISA data. Horse radish peroxidase (HRP) Ov16 ELISAs were conducted on DBS in 58 (87%) of the 67 villages. A total of 3,012 individuals underwent ELISA testing, producing 348 (12%) Ov16 positive results. 
These data highlight, that whilst broadly efficacious, there is a need for caution on the reliance of RDTs in determination of EoT and furthers calls for the development of a novel RDT, with a diagnostic profile providing higher sensitivity and specificity values.
DOI: 10.17632/s7ykfbr69y.1
title: Handmade double-blade scalpel for Mohs surgery

description: Clinical Challenge: Obtaining Mohs surgery stages can be challenging in certain areas with loose or flaccid skin such as the eyelid. This might be helped by double-blade scalpels that make the cut on both sides of the stage simultaneously, while the skin is still tightened. Some authors described the use of scalpel handles with two notches for two blades, although they might not be available in all hospitals.1 Arnon et al.2 described a simple way of making a double-blade scalpel by attaching a second blade to a common scalpel handle with tape. However, the bond relies on the tape, and we found that the use of two scalpel handles provides a more stable union.
 
Solution: One scalpel is attached to the handle as usual, and to keep a 3 mm distance between the blades, the notch of both handles must face each other (Video 1). Therefore, the second blade should be placed oppositely so both blades face the same direction, which although it does not allow for a perfect fit, keeps the scalpel strongly fixed to the handle. Both scalpels are attached with adhesive tape, that can be sterile, allowing for a stable 3 mm distance between blades. We found this handmade double-bladed scalpel especially helpful in obtaining spaghetti Mohs stages on difficult areas where maintaining a tightened skin after the first cut is not easy, although it can also be used for conventional Mohs surgery.

1. Choi KW, Lee YK, Kim YH, Lee CW, Kim KH. Surgical Tip: The Double-Bladed Scalpel in Mohs Micrographic Surgery. Ann Dermatol. 2008 Jun;20(2):86-9.
2. Arnon O, Pagkalos VA, Xanthinaki AA, Silberstein E. Double-bladed scalpel in mohs micrographic surgery. ISRN Dermatol. 2012;2012:617314.
DOI: 10.17632/772vxh6fyf.1
title: Assessment of hearing dysfunction in patients with Graves’ disease and thyroid eye disease without or with teprotumumab

description: SUPPLEMENT
Assessment of hearing dysfunction in patients with Graves’ disease and thyroid eye disease without or with teprotumumab
DOI: 10.17632/crrzx74gfr.1
title: EFECTIVIDAD DE LOS EXTRACTOS DE NEEM (Azadirachta indica) Y JABONCILLO (Sapindus saponaria) PARA EL CONTROL DE PULGÓN NEGRO (Toxoptera aurantii) EN EL CULTIVO DE NARANJA (Citrus sinensis), SITIO PAGUA - EL ORO

description: El uso combinado de neem y jaboncillo es una estrategia eficaz y sostenible para el control de pulgones en cultivos de naranja. El neem, derivado del árbol Azadirachta indica, contiene azadiractina, un compuesto que interfiere con los sistemas hormonales de los pulgones, impidiendo su alimentación y reproducción. El jaboncillo, por su parte, actúa como un insecticida de contacto que desintegra la capa cerosa que protege a los pulgones, causando su deshidratación y muerte. Al combinar ambos, se logra un efecto sinérgico: el neem debilita y reduce la población de pulgones a largo plazo, mientras que el jaboncillo proporciona un control más inmediato. Esta combinación es especialmente valiosa en un enfoque de manejo integrado de plagas, ya que es respetuosa con el medio ambiente y minimiza el impacto sobre insectos beneficiosos, asegurando un control efectivo de las plagas sin recurrir a productos químicos agresivos.
DOI: 10.15468/dl.43mcym
title: Occurrence Download

description: A dataset listing the 4 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Melitaea aetherie Hübner"
 ]
}

 The dataset's 4 records were derived from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0075416-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u3e44m
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON (((144.473041 -36.890026, 144.477532 -36.890026, 144.477532 -36.885535, 144.473041 -36.885535, 144.473041 -36.890026)), ((144.473041 -36.890026, 144.459566 -36.890026, 144.459566 -36.892272, 144.466303 -36.892272, 144.466303 -36.896764, 144.470795 -36.896764, 144.470795 -36.89901, 144.477532 -36.89901, 144.477532 -36.896764, 144.473041 -36.896764, 144.473041 -36.890026)))"
 ]
}

 The dataset includes 35 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0075429-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/n3zjsfdbvr.1
title: Sanctions and Russian Online Prices - Replication Data

description: Data on prices and quantity available for sale (weekly averages) acquired via web scraping from a Russian retail chain. Part of the replication package for the paper "Sanctions and Russian Online Prices", published on Journal of Economic Behavior and Organization (2024). Replication code also available on GitHub: https://github.com/paluigi/sanctions-russian-online-prices
DOI: 10.17632/n3zjsfdbvr.2
title: Sanctions and Russian Online Prices - Replication Data

description: Data on prices and quantity available for sale (weekly averages) acquired via web scraping from a Russian retail chain. Part of the replication package for the paper "Sanctions and Russian Online Prices", published on Journal of Economic Behavior and Organization (2024). Replication code also available on GitHub: https://github.com/paluigi/sanctions-russian-online-prices

Please cite this work as:
Benchimol, J., and Palumbo, L., 2024. Sanctions and Russian online prices. Journal of Economic Behavior & Organization, vol. 225, pages 483-521. doi: 10.1016/j.jebo.2024.07.013

Bibtex entry:
@article{BenchimolPalumbo2024a, title={{Sanctions and Russian online prices}}, author={Benchimol, Jonathan and Palumbo, Luigi}, journal={Journal of Economic Behavior \& Organization}, year=2024, volume={225}, pages={483-521}}
DOI: 10.17632/w23dxzf9m4.1
title: Data from: New opportunities for grassland species in warming temperate winters. Functional Ecology

description: Data from: Lubbe et al. (2024). New opportunities for grassland species in warming temperate winters. Functional ecology.

Three datasets are included:
"Leaf_data.xlsx" - standard spreadsheet format. measured traits of winter and spring leaves (specific leaf area, leaf dry matter content, nitrogen content and photosynthesis efficiency FV/Fm).
"Community_data.csv" - comma-separated format. Cover of green and dead leaves in the community survey with indicated species observed phenology types (fully wintergreen, partially wintergreen and not wintergreen). The table is in a "long format" (needed to fit PGLMM).
"Community_data_winters22-24.csv" - comma-separated format. Species composition data of the community survey conducted in February 2022, 2023 and 2024. The table is in a "wide format".

Please see the paper for full details on sampling design and protocol.
DOI: 10.17632/dxcdv8652s.1
title: Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis

description: Abstract—Human body-pose estimation is a complex problem in computer vision. Recent research interests have been widened specifically on the Sports, Yoga, and Dance (SYD) postures for maintaining health conditions. The SYD pose categories are regarded as a fine-grained image classification task due to the complex movement of body parts. Deep Convolutional Neural Networks (CNNs) have attained significantly improved performance in solving various human body-pose estimation problems. Though decent progress has been achieved in yoga postures recognition using deep learning techniques, fine-grained sports, and dance recognition necessitates ample research attention. However, no benchmark public image dataset with sufficient inter-class and intra-class variations is available yet to address sports and dance postures classification. To solve this limitation, we have proposed two image datasets, one for 102 sport categories and another for 12 dance styles. Two public datasets, Yoga-82 which contains 82 classes and Yoga-107 represents 107 classes are collected for yoga postures. These four SYD datasets are experimented with the proposed deep model, SYD-Net, which integrates a patch-based attention (PbA) mechanism on top of standard backbone CNNs. The PbA module leverages the self-attention mechanism that learns contextual information from a set of uniform and multi-scale patches and emphasizes discriminative features to understand the semantic correlation among patches. Moreover, random erasing data augmentation is applied to improve performance. The proposed SYD-Net has achieved state-of-the-art accuracy on Yoga-82 using five base CNNs. SYD-Net’s accuracy on other datasets is remarkable, implying its efficiency. Our Sports-102 and Dance-12 datasets are publicly available for research only.
DOI: 10.17632/s64wcgmyky.1
title: Socio-Economic

description: This study examines the cultural, socio-economic, and demographic factors influencing technology adoption in 50 developing countries
DOI: 10.17632/dxcdv8652s.2
title: Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis

description: Abstract—Human body-pose estimation is a complex problem in computer vision. Recent research interests have been widened specifically on the Sports, Yoga, and Dance (SYD) postures for maintaining health conditions. The SYD pose categories are regarded as a fine-grained image classification task due to the complex movement of body parts. Deep Convolutional Neural Networks (CNNs) have attained significantly improved performance in solving various human body-pose estimation problems. Though decent progress has been achieved in yoga postures recognition using deep learning techniques, fine-grained sports, and dance recognition necessitates ample research attention. However, no benchmark public image dataset with sufficient inter-class and intra-class variations is available yet to address sports and dance postures classification. To solve this limitation, we have proposed two image datasets, one for 102 sport categories and another for 12 dance styles. Two public datasets, Yoga-82 which contains 82 classes and Yoga-107 represents 107 classes are collected for yoga postures. These four SYD datasets are experimented with the proposed deep model, SYD-Net, which integrates a patch-based attention (PbA) mechanism on top of standard backbone CNNs. The PbA module leverages the self-attention mechanism that learns contextual information from a set of uniform and multi-scale patches and emphasizes discriminative features to understand the semantic correlation among patches. Moreover, random erasing data augmentation is applied to improve performance. The proposed SYD-Net has achieved state-of-the-art accuracy on Yoga-82 using five base CNNs. SYD-Net’s accuracy on other datasets is remarkable, implying its efficiency. Our Sports-102 and Dance-12 datasets are publicly available for research only.


Please cite this work:

A. Bera, M. Nasipuri, O. Krejcar and D. Bhattacharjee, "Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis," in IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-13, 2023, Art no. 5020613, doi: 10.1109/TIM.2023.3293564.

https://ieeexplore.ieee.org/document/10177209
DOI: 10.17632/v8b9fmhbc5.1
title: A dataset from a survey on children's knowledge of entomology in Yucatan, Mexico

description: Yucatan, Mexico is a region where vector-borne diseases are commonly found. It is crucial for children to be able to distinguish between dangerous arthropods and those that are beneficial to the environment and humans. The study's goal was to assess schoolchildren's knowledge of entomology. Data were collected between October 2023 and June 2024. An intervention study was conducted in basic education schools. At the beginning of the study, a visual diagnostic test was applied to measure the children's knowledge about insects and arachnids. The intervention consisted of giving talks about the biology of arthropods, with emphasis on those that suck blood and transmit pathogens. In addition, the schoolchildren, accompanied by entomologists, collected arthropods in the school yard and observed them under a microscope. After the intervention, the same visual diagnostic test was applied to evaluate change in knowledge of entomology.
DOI: 10.17632/gwpwn5fwbs.1
title: Sheyang River Estuary Data

description: Field data of the water elevation, currents, salinity, waves, and suspended sediment concentration (SSC) in the Sheyang River Estuary (SRE).
DOI: 10.17632/dpst24rjn4.1
title: Identification of proteolytic bacteria from Yunnan fermented foods and their use to reduce the allergenicity of β-lactoglobulin

description: Supplementary material
DOI: 10.5281/zenodo.11654112
title: Data from: Investigating the Association of Seasonal Dynamics in GEDI Canopy Cover Profiles and Sentinel-1 Backscatter in Temperate Forests

description: This dataset supports the analysis about Investigating the Association of Seasonal Dynamics in GEDI Canopy Cover Profiles and Sentinel-1 Backscatter in Temperate Forests
description: The change of forest vertical structure influences the carbon, energy and water cycle in terrestrial ecosystem. Light detection and ranging (LiDAR) and synthetic aperture radar (SAR) have been applied for collecting forest vertical structure information. NASA’s Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne LiDAR system which measures the forest vertical structure and its change at global scale. In comparison with GEDI, ESA’s Sentinel-1 SAR satellites have regular revisit, longer operational lifespan and larger spatial coverage. On the one hand, detecting seasonal changes of forest vertical structures with GEDI has not been extensively investigated. On the other hand, the relationship between Sentinel-1 backscatter and GEDI canopy cover at given height is not clear yet. In this study, we firstly generated the time series of GEDI canopy cover profiles at landscape scale for two broadleaved forests and two coniferous forests in Germany. Only GEDI footprints with forest height between 25 m and 30 m were used. We then extracted the Sentinel-1 backscatter at all available GEDI acquisition dates. A regression analysis was finally performed to quantify the influence of vertical structure on backscatter. The results show that layers between 10 m and 25 m have stronger seasonal change in broadleaved forests than coniferous forests. In broadleaved forests, canopy cover time series at 15-20 m layer height shows the strongest correlation with Sentinel-1 VV/VH time series. There is no significant correlation between canopy cover times series and backscatter time series in coniferous forests. This study provides new insights of monitoring the dynamics of forest vertical structure and understanding its influence on SAR backscatter. 
description: This dataset contains:



GEDI_L2B: Unfiltered GEDI Level 2B footprints in each study site (pkl and shapefile files).

Sentinel-1: Radiometric terrain corrected Sentinel-1 backscatter in each study site (raster files).

Sentinel-2: Optical images from Sentinel-2 for getting a quick look over study site (raster files).

Spatiotemporal_aggregated_time_series: GEDI and Sentinel-1 time series generated through the spatiotemporal aggregation (csv files).


Details about generating this dataset can be found in the publication: https://doi.org/10.1109/LGRS.2024.3443525.
DOI: 10.5281/zenodo.11654113
title: Data from: Investigating the Association of Seasonal Dynamics in GEDI Canopy Cover Profiles and Sentinel-1 Backscatter in Temperate Forests

description: This dataset supports the analysis about Investigating the Association of Seasonal Dynamics in GEDI Canopy Cover Profiles and Sentinel-1 Backscatter in Temperate Forests
description: The change of forest vertical structure influences the carbon, energy and water cycle in terrestrial ecosystem. Light detection and ranging (LiDAR) and synthetic aperture radar (SAR) have been applied for collecting forest vertical structure information. NASA’s Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne LiDAR system which measures the forest vertical structure and its change at global scale. In comparison with GEDI, ESA’s Sentinel-1 SAR satellites have regular revisit, longer operational lifespan and larger spatial coverage. On the one hand, detecting seasonal changes of forest vertical structures with GEDI has not been extensively investigated. On the other hand, the relationship between Sentinel-1 backscatter and GEDI canopy cover at given height is not clear yet. In this study, we firstly generated the time series of GEDI canopy cover profiles at landscape scale for two broadleaved forests and two coniferous forests in Germany. Only GEDI footprints with forest height between 25 m and 30 m were used. We then extracted the Sentinel-1 backscatter at all available GEDI acquisition dates. A regression analysis was finally performed to quantify the influence of vertical structure on backscatter. The results show that layers between 10 m and 25 m have stronger seasonal change in broadleaved forests than coniferous forests. In broadleaved forests, canopy cover time series at 15-20 m layer height shows the strongest correlation with Sentinel-1 VV/VH time series. There is no significant correlation between canopy cover times series and backscatter time series in coniferous forests. This study provides new insights of monitoring the dynamics of forest vertical structure and understanding its influence on SAR backscatter. 
description: This dataset contains:



GEDI_L2B: Unfiltered GEDI Level 2B footprints in each study site (pkl and shapefile files).

Sentinel-1: Radiometric terrain corrected Sentinel-1 backscatter in each study site (raster files).

Sentinel-2: Optical images from Sentinel-2 for getting a quick look over study site (raster files).

Spatiotemporal_aggregated_time_series: GEDI and Sentinel-1 time series generated through the spatiotemporal aggregation (csv files).


Details about generating this dataset can be found in the publication: https://doi.org/10.1109/LGRS.2024.3443525.
DOI: 10.17632/7xb5mvg494.1
title: Aplikasi Mikoriza Vesikular Arbuskular (MVA) dan Media Tanam terhadap Pertumbuhan dan Hasil Tanaman Cabai Rawit

description: Jurnal penelitian agroteknologi
DOI: 10.17632/g7xs9f2tzf.1
title: DLHDF Dataset

description: The data set used in this study is uploaded to Mendeley Data and the download link is placed in the Appendix of the revised manuscript so that it could be freely accessed by the public. The description of the data uploaded read as follows: 
“The DLHDF (DL model for hydrate dissociation front ) dataset generated in this paper is provided in this dataset depository [50]. The DLHDF dataset is divided into four CSV files, namely, input.csv, output20.csv, output50.csv, and output50.csv. The arrangement of data in those four files is as follows: 
* input.csv contains the model input data in 31,000 simulation cases, each case has 59 THCM properties across the range defined by the minimum and maximum values. The simulations provide hydrate saturation at each element, recorded every 0.5 days for a 30-day period, which gives 60 time points in each simulation. The input properties for each time point include 60 parameters, including the properties as well as the time point tag (t) to represent the temporal status. Overall, the input.csv consists of 11,600,000 (1,860,000 times 60) inputs. There are 60 columns in the input.csv file representing the 59 parameters and the time point tag (from 1 to 60). There are 1,860,000 (31000 times 60) rows in the input.csv file representing 31,000 simulation cases with 60 time points in each case.
* output20.csv contains the model output data for the location of the 20% dissociation front. Overall, the output20.csv consists of 1,860,000 (1,860,000 times 1) outputs. Each output represents the location of the dissociation front at 20% and is derived based on the first element that reaches the defined degree of dissociation among the 35 elements in the numerical model at every recorded time point. The 1,860,000 rows in the output20.csv file represent 31,000 simulation cases with 60 time points in each case.
* output50.csv contains the model output data for the location of the 50% dissociation front. Overall, the output80.csv consists of 1,860,000 (1,860,000 times 1) outputs. Each output represents the location of the dissociation front at 50% and is derived based on the first element that reaches the defined degree of dissociation among the 35 elements in the numerical model at every recorded time point. The 1,860,000 rows in the output50.csv file represent 31,000 simulation cases with 60 time points in each case.
* output80.csv contains the model output data for the location of the 80% dissociation front. Overall, the output80.csv consists of 1,860,000 (1,860,000 times 1) outputs. Each output represents the location of the dissociation front at 80% and is derived based on the first element that reaches the defined degree of dissociation among the 35 elements in the numerical model at every recorded time point. The 1,860,000 rows in the output80.csv file represent 31,000 simulation cases with 60 time points in each case.