cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.3rydtu
title: Occurrence Download

description: A dataset containing 69534 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hygrotus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 69534 records from 359 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018396-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rh5rcd
title: Occurrence Download

description: A dataset containing 209399 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is TRUE",
 "HasGeospatialIssue is FALSE",
 "TaxonKey is Balaenoptera physalus",
 "Year is greater than or equal to 2010",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 209399 records from 237 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018416-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18641901
title: 18S-ITS-LSU ONT DATA (.fastq)

description: You will find in this directory a dataset related to the  MushContig project.

These data originate from an internal control consisting of seven fungal species, sequenced using Oxford Nanopore Technologies.

 




name
dorado version
model
date of sequencing


SQK-NBD114-96_barcode16
0.7.2
dna_r10.4.1_e8.2_400bps_sup@v5.0.0
2025-07-30


SQK-NBD114-96_barcode20
0.7.2
dna_r10.4.1_e8.2_400bps_sup@v5.0.0
2025-05-06


SQK-NBD114-96_barcode28
0.7.2
dna_r10.4.1_e8.2_400bps_sup@v5.0.0
2025-10-22


SQK-NBD114-96_barcode69
0.7.2
dna_r10.4.1_e8.2_400bps_sup@v5.0.0
2026-01-12


SQK-NBD114-96_barcode94
0.7.2
dna_r10.4.1_e8.2_400bps_sup@v5.0.0
2026-02-10
DOI: 10.5281/zenodo.18641902
title: 18S-ITS-LSU ONT DATA (.fastq)

description: You will find in this directory a dataset related to the  MushContig project.

These data originate from an internal control consisting of seven fungal species, sequenced using Oxford Nanopore Technologies.

 




name
dorado version
model
date of sequencing


SQK-NBD114-96_barcode16
0.7.2
dna_r10.4.1_e8.2_400bps_sup@v5.0.0
2025-07-30


SQK-NBD114-96_barcode20
0.7.2
dna_r10.4.1_e8.2_400bps_sup@v5.0.0
2025-05-06


SQK-NBD114-96_barcode28
0.7.2
dna_r10.4.1_e8.2_400bps_sup@v5.0.0
2025-10-22


SQK-NBD114-96_barcode69
0.7.2
dna_r10.4.1_e8.2_400bps_sup@v5.0.0
2026-01-12


SQK-NBD114-96_barcode94
0.7.2
dna_r10.4.1_e8.2_400bps_sup@v5.0.0
2026-02-10
DOI: 10.34740/kaggle/dsv/14839909
title: Mahanagar Yatayat Transport Dataset
DOI: 10.5281/zenodo.18642424
title: Data from Sedda et al. "Designing spatial adaptive surveillance for the emerging malaria vector Anopheles stephensi in East Africa"

description: Anopheles stephensi surveillance data (different times) from Djibouti, Ethiopia and Kenya.

Data fields: Year, Month, Longitude, Latitude, Anopheles stephensi catches if Stage is adult or number of larvae sites within 1 km radious if Stage is larval, and Country.   
description: Full list of contributors:

Luigi Sedda1,2$, Eric Ochomo3,4*, Fitsum G. Tadesse5,6,7,*, Bouh Adbi Khaireh8,*, Assalif Demissew5,9, Mulugeta Demisse5, Dejene Getachew10, Samatar K. Guelleh8, Mohamed M. Ibrahim8, Bernard Abongo3, Vincent Moshi3, Margaret Muchoki3, Brian Polo3

1 Lancaster Ecology and Epidemiology Group, Lancaster Medical School, Lancaster University, UK

2 Environment, Data Management and Training Research Unit, Tropical Infectious Disease Research Centre, Universite D'Abomey-Calavi, Benin

3 Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.

4 Vector Group, Liverpool School of Tropical Medicine, UK

5 Malaria and Neglected Tropical Diseases Division, Armauer Hanson Research Institute, Ethiopia

6 Department of Infection Biology, London School of Hygiene and Tropical Medicine, UK

7 Department of Medical Microbiology, Radbouduc University Medical Center, The Netherlands

8 Direction des Programmes de Santé Prioritaire, Ministère de la Santé, Djibouti.

9 College of Medicine and Health Sciences, Ambo University, Ethiopia

10 Department of Applied Biology, Adama Science and Technology University, Ethiopia
DOI: 10.5281/zenodo.18642425
title: Data from Sedda et al. "Designing spatial adaptive surveillance for the emerging malaria vector Anopheles stephensi in East Africa"

description: Anopheles stephensi surveillance data (different times) from Djibouti, Ethiopia and Kenya.

Data fields: Year, Month, Longitude, Latitude, Anopheles stephensi catches if Stage is adult or number of larvae sites within 1 km radious if Stage is larval, and Country.   
description: Full list of contributors:

Luigi Sedda1,2$, Eric Ochomo3,4*, Fitsum G. Tadesse5,6,7,*, Bouh Adbi Khaireh8,*, Assalif Demissew5,9, Mulugeta Demisse5, Dejene Getachew10, Samatar K. Guelleh8, Mohamed M. Ibrahim8, Bernard Abongo3, Vincent Moshi3, Margaret Muchoki3, Brian Polo3

1 Lancaster Ecology and Epidemiology Group, Lancaster Medical School, Lancaster University, UK

2 Environment, Data Management and Training Research Unit, Tropical Infectious Disease Research Centre, Universite D'Abomey-Calavi, Benin

3 Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya.

4 Vector Group, Liverpool School of Tropical Medicine, UK

5 Malaria and Neglected Tropical Diseases Division, Armauer Hanson Research Institute, Ethiopia

6 Department of Infection Biology, London School of Hygiene and Tropical Medicine, UK

7 Department of Medical Microbiology, Radbouduc University Medical Center, The Netherlands

8 Direction des Programmes de Santé Prioritaire, Ministère de la Santé, Djibouti.

9 College of Medicine and Health Sciences, Ambo University, Ethiopia

10 Department of Applied Biology, Adama Science and Technology University, Ethiopia
DOI: 10.15468/dl.ydfr5y
title: Occurrence Download

description: A dataset containing 471 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mosla chinensis",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 471 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018452-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17121640
title: Dataset for: Micro-scale plant diversity in traditional wood-pastures

description: Dataset for: Micro-scale plant diversity in traditional wood-pastures.

The excel file contains:



Species composition data (presence and relative cover visually estimated).

Information on vegetation layer (w = tree and shrub layer; h = herbaceuous layer)

Geographical coordinates of site, plot position (under the solitary tree canopy, at the edge of solitary tree canopy, outside solitary tree canopy).


Detailed description of the data is available in "Le et al. 2025 Micro-scale plant diversity in traditional wood-pastures. Applied Vegetation Science"
DOI: 10.5281/zenodo.17121641
title: Dataset for: Micro-scale plant diversity in traditional wood-pastures

description: Dataset for: Micro-scale plant diversity in traditional wood-pastures.

The excel file contains:



Species composition data (presence and relative cover visually estimated).

Information on vegetation layer (w = tree and shrub layer; h = herbaceuous layer)

Geographical coordinates of site, plot position (under the solitary tree canopy, at the edge of solitary tree canopy, outside solitary tree canopy).


Detailed description of the data is available in "Le et al. 2025 Micro-scale plant diversity in traditional wood-pastures. Applied Vegetation Science"
DOI: 10.5281/zenodo.17490684
title: Integrated Dataset of Satellite-Derived Water Quality Parameters and Bird Populations in Five Ramsar Wetlands of India Using Sentinel ‑2 Imagery and in ‑Situ Observations

description: A multi-year dataset of water quality parameters derived from Sentinel-2 imagery was compiled for five Ramsar wetlands in India, three in Kerala (Vembanad, Sasthamkotta, Ashtamudi), one in Punjab (Harike), and one in Tamil Nadu (Point Calimere) using QGIS-defined boundaries and Copernicus satellite data from 2016–2024 (Kerala) and 2018–2024 (others). Thirteen spectral bands were resampled to 10 m resolution and used with regression models based on in-situ measurements to estimate water quality indicators: TSS, turbidity, conductivity, pH, ammoniacal nitrogen, BOD, and DO for Kerala; chlorophyll-a, cyanobacteria, turbidity, CDOM, and DOC for Harike and Point Calimere. Data are stored in TSV files by location and date for spatiotemporal analysis, and bird population records from sources like eBird, CNHS, and Bird Count India cover both migratory and resident species across all sites.
DOI: 10.60797/comp.2025.8.2.1
title: Результаты эксперимента по курсу Mathematics-1

description: Cifra. Компьютерные науки и информатика, Выпуск 4 (8) 2025
DOI: 10.5281/zenodo.17500516
title: Replication package for "On the uniform, achromatic rotation of CMB polarization"

description: This record is the registered prediction & replication package for the Letter “On the uniform, achromatic rotation of CMB polarization.” It provides the exact figure PDFs used in the manuscript and the minimal numerical artifacts required to verify the reported numbers, with no external data dependencies.

The archive contains the two journal-sized figure PDFs (fig1_beta_comparison.pdf, fig2_slope_check.pdf), the parameter/convention file constants.yaml, the band-summary file data_beta_summary.csv, and a self-contained script reproduce_prediction.py that recomputes the parameter-free theoretical prediction without using any CMB data. Angles are in degrees and follow the manuscript’s sign convention β ≡ −β_exp; the optional frequency-slope check uses the model β(ν) = β0 + m·(ν/GHz)^−2, so m is expressed in degrees.

Running the script reproduces the prediction β_th = 0.243° ± 0.003° and prints the intermediate value φ0. Version v1.0.4 neutralizes the package branding and fixes the dimensional example for finite renormalizations in the scalar constraint (δI0 = 3·δU/μ^4; hence dL = 3·δU/(8 b1 c3^6 μ^4)). No third-party libraries are required (Python ≥ 3.8).

Figures may be cited or reused directly; numerical values quoted in the paper can be checked against constants.yaml and data_beta_summary.csv. The code is MIT-licensed; figures and data are CC BY 4.0. Please cite the DOI of this dataset together with the manuscript; for a version-agnostic reference, cite the concept DOI.
DOI: 10.5281/zenodo.17419910
title: Replication package for "On the uniform, achromatic rotation of CMB polarization"

description: This record is the registered prediction & replication package for the Letter “On the uniform, achromatic rotation of CMB polarization.” It provides the exact figure PDFs used in the manuscript and the minimal numerical artifacts required to verify the reported numbers, with no external data dependencies.

The archive contains the two journal-sized figure PDFs (fig1_beta_comparison.pdf, fig2_slope_check.pdf), the parameter/convention file constants.yaml, the band-summary file data_beta_summary.csv, and a self-contained script reproduce_prediction.py that recomputes the parameter-free theoretical prediction without using any CMB data. Angles are in degrees and follow the manuscript’s sign convention β ≡ −β_exp; the optional frequency-slope check uses the model β(ν) = β0 + m·(ν/GHz)^−2, so m is expressed in degrees.

Running the script reproduces the prediction β_th = 0.243° ± 0.003° and prints the intermediate value φ0. Version v1.0.4 neutralizes the package branding and fixes the dimensional example for finite renormalizations in the scalar constraint (δI0 = 3·δU/μ^4; hence dL = 3·δU/(8 b1 c3^6 μ^4)). No third-party libraries are required (Python ≥ 3.8).

Figures may be cited or reused directly; numerical values quoted in the paper can be checked against constants.yaml and data_beta_summary.csv. The code is MIT-licensed; figures and data are CC BY 4.0. Please cite the DOI of this dataset together with the manuscript; for a version-agnostic reference, cite the concept DOI.
DOI: 10.15468/dl.yxfxch
title: Occurrence Download

description: A dataset containing 12944 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Acraea terpsicore"
 ]
}

 The dataset includes 12944 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010650-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8n25nd
title: Occurrence Download

description: A dataset containing 192 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Aplocheilus armatus, Aplocheilus panchax, Aplocheilus javanica, Panchax panchax, Haplochilus panchax)",
 "Country is Singapore",
 "OccurrenceStatus is Present",
 "EventDate is 1800,2026"
 ]
}

 The dataset includes 192 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010651-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t322gx
title: Occurrence Download

description: A dataset containing 163010 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Germany",
 "IucnRedListCategory is one of (CR, EN, VU)",
 "OccurrenceStatus is Present",
 "Year 2020-2025"
 ]
}

 The dataset includes 163010 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010639-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13575689
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.6084/m9.figshare.28424282
title: Dietary data from surface and subterranean populations of <i>Speleomantes</i> cave salamanders

description: We provide here a dataset that collects dietary data from 36 populations belonging to seven of the eight <i>Speleomantes</i> species (<i>S. strinatii</i>, <i>S. ambrosii</i>, <i>S. italicus</i>, <i>S. flavus</i>, <i>S. imperialis</i>, <i>S. sarrabusensis</i>, <i>S. genei</i>) and the natural hybrid zone <i>S. italicus</i> × <i>S. ambrosii</i>. Eleven populations were sampled in natural and artificial subterranean environments for a total surveyed area of 4,667 m<sup>2</sup>. Twenty-five surface populations were sampled in woodlands, garrigues, and dry-stone walls for a total surveyed area of 34,640 m<sup>2</sup>. Data collection took place from 2021 to 2024. Twenty-seven populations were surveyed only once; the other nine were surveyed twice during different seasons/years. The dataset contains information on a total of 1,108 captured salamanders. We used stomach flushing to obtain the stomach contents of the salamanders, which were assessed qualitatively and quantitatively using the stereomicroscope. In 930 salamanders, we were able to recognize a total of 8,899 consumed prey items belonging to 50 different prey categories (e.g., order level or lower).
DOI: 10.6084/m9.figshare.28424282.v1
title: Dietary data from surface and subterranean populations of <i>Speleomantes</i> cave salamanders

description: We provide here a dataset that collects dietary data from 36 populations belonging to seven of the eight <i>Speleomantes</i> species (<i>S. strinatii</i>, <i>S. ambrosii</i>, <i>S. italicus</i>, <i>S. flavus</i>, <i>S. imperialis</i>, <i>S. sarrabusensis</i>, <i>S. genei</i>) and the natural hybrid zone <i>S. italicus</i> × <i>S. ambrosii</i>. Eleven populations were sampled in natural and artificial subterranean environments for a total surveyed area of 4,667 m<sup>2</sup>. Twenty-five surface populations were sampled in woodlands, garrigues, and dry-stone walls for a total surveyed area of 34,640 m<sup>2</sup>. Data collection took place from 2021 to 2024. Twenty-seven populations were surveyed only once; the other nine were surveyed twice during different seasons/years. The dataset contains information on a total of 1,108 captured salamanders. We used stomach flushing to obtain the stomach contents of the salamanders, which were assessed qualitatively and quantitatively using the stereomicroscope. In 930 salamanders, we were able to recognize a total of 8,899 consumed prey items belonging to 50 different prey categories (e.g., order level or lower).
DOI: 10.5281/zenodo.17254667
title: Data from: TerraGrow: Integrated Platform for Real Time Plant Monitoring and Automated Watering System With IoT And Fuzzy Sugeno Algorithm

description: Article title

TerraGrow : Integrated Platform for Real Time Plant Monitoring and Automated Watering System With IoT And Fuzzy Sugeno Algorithm

Authors

Prima Wijayakusumaa, Galang Persada Nurani Hakimb, Bin Lia 

Affiliations

a School of Integrated Circuits and Electronics, Beijing Institute of Technology, China

b Department of Electrical Engineering, Faculty of Engineering, Universitas Mercu Buana, Indonesia

Corresponding author’s email address 

3820251033@bit.edu.cn

Abstract

Global water scarcity, climate variability, and rising input costs are pushing agriculture toward precise, evidence-based irrigation, yet many available systems remain proprietary, expensive, and difficult to adapt. TerraGrow is an open-source, low-cost controller that runs all sensing and closed-loop irrigation locally on a single ESP32 using real-time soil-moisture, pH, temperature, and humidity inputs with a Sugeno-type fuzzy policy. Its modular and ergonomic hardware with a printable enclosure, integrated pump driver, and labeled connectors allows non-experts to assemble, calibrate, and service the unit quickly in the field. Compared with earlier low-cost IoT irrigation nodes, TerraGrow’s novelty lies in fully local multi-sensor fuzzy control and an easy-to-deploy form factor supported by complete, reproducible design files. Yielding quantified gains in tests, soil moisture was held within a 60–80% band, enabled by precise sensing and a consistent hardware–firmware implementation. By combining local autonomy, simple deployment, and open documentation, TerraGrow makes practical precision irrigation more accessible to resource-constrained growers. 

Technical info

Fig. 1. End‑to‑end layout of TerraGrow.

Fig. 2. TerraGrow’s architecture diagram systems.

Fig. 3. Sensing Unit.

Fig. 4. Actuation Unit .

Fig. 5. Build wiring overview.

Fig. 6. TerraGrow operation instructions.

Fig. 7. Blynk IoT dashboard configurations.

Fig. 8. The proposed pseudocode of TerraGrow.

Fig.  9. TerraGrow firmware programs flowchart.

Fig. 10. Bench and field first-run setups.

Fig. 11. Soil moisture vs ADC.

Fig. 12. Temperature and Relative humidity  DHT11 vs HTC-2.

Fig. 13. pH response to buffer additions.
DOI: 10.6084/m9.figshare.28910000.v3
title: 四色定理证明

description: 四色定理证明
DOI: 10.21227/bgnp-xs19
title: "CRHE-Long-Term-PPG-BP-Dataset"

description: "The CRHE dataset is a publicly available, multi-wavelength, dual-24h photoplethysmography (PPG) and blood pressure (BP) dataset, collected using wearable devices and ambulatory BP monitors. It contains synchronized PPG signals and reference BP measurements from subjects over two separate 24-hour periods, enabling the study of circadian rhythm effects on BP. In addition to high-quality signal recordings, CRHE features detailed circadian rhythm annotations and supports research on continuous, cuffless BP estimation and physiological monitoring. By providing CRHE, we aim to advance the development and benchmarking of intelligent BP and circadian rhythm monitoring in healthcare IoT systems."
DOI: 10.6084/m9.figshare.30507575
title: A meta-analysis combined with Biomod2 ensemble modeling reveals the climate-sensitive thermal adaptability of the invasive species <i>Phenacoccus solenopsis</i> Tinsley

description: Global warming is profoundly altering the population dynamics and distribution of agricultural pests. The cotton mealybug <i>Phenacoccus solenopsis</i> (Hemiptera: Pseudococcidae), a highly polyphagous invasive pest, poses a growing threat to cotton and ornamental plants worldwide. To quantify its thermal suitability and potential responses to climate change, we combined a global meta-analysis of temperature-dependent physiological traits with an ensemble species distribution model (Biomod2). Results showed that <i>P. solenopsis</i> develops optimally at 30–35 °C, with sharply reduced development time at higher temperatures. Among 13 bioclimatic variables, mean temperature of the wettest quarter (bio08, 24.42%), isothermality (bio03, 20.90%), and mean temperature of the driest quarter (bio09, 18.47%) were the dominant predictors. Current suitable habitats are concentrated in tropical and subtropical regions of Asia, Africa, and the Americas. Under future SSP1–2.6 to SSP5–8.5 scenarios, moderate-suitability areas are projected to expand northward, particularly in East Asia and southern North America, while highly suitable zones may decline under extreme warming. These results indicate that temperature is the primary environmental driver shaping the global distribution of <i>P. solenopsis</i>, and that climate warming may promote its expansion into temperate zones. Our study provides a global-scale quantitative framework for assessing pest thermal ecology and invasion risk, offering valuable insights for early warning, regional monitoring, and adaptive management under future climate change.
DOI: 10.6084/m9.figshare.30507575.v1
title: A meta-analysis combined with Biomod2 ensemble modeling reveals the climate-sensitive thermal adaptability of the invasive species <i>Phenacoccus solenopsis</i> Tinsley

description: Global warming is profoundly altering the population dynamics and distribution of agricultural pests. The cotton mealybug <i>Phenacoccus solenopsis</i> (Hemiptera: Pseudococcidae), a highly polyphagous invasive pest, poses a growing threat to cotton and ornamental plants worldwide. To quantify its thermal suitability and potential responses to climate change, we combined a global meta-analysis of temperature-dependent physiological traits with an ensemble species distribution model (Biomod2). Results showed that <i>P. solenopsis</i> develops optimally at 30–35 °C, with sharply reduced development time at higher temperatures. Among 13 bioclimatic variables, mean temperature of the wettest quarter (bio08, 24.42%), isothermality (bio03, 20.90%), and mean temperature of the driest quarter (bio09, 18.47%) were the dominant predictors. Current suitable habitats are concentrated in tropical and subtropical regions of Asia, Africa, and the Americas. Under future SSP1–2.6 to SSP5–8.5 scenarios, moderate-suitability areas are projected to expand northward, particularly in East Asia and southern North America, while highly suitable zones may decline under extreme warming. These results indicate that temperature is the primary environmental driver shaping the global distribution of <i>P. solenopsis</i>, and that climate warming may promote its expansion into temperate zones. Our study provides a global-scale quantitative framework for assessing pest thermal ecology and invasion risk, offering valuable insights for early warning, regional monitoring, and adaptive management under future climate change.
DOI: 10.5281/zenodo.17500581
title: ISL Keypoints NPZ format dataset
DOI: 10.5281/zenodo.17500582
title: ISL Keypoints NPZ format dataset