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

description: A dataset containing 97307 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 {
 "or" : [
 "Issue is Geodetic datum assumed WGS84",
 "Issue is Country derived from coordinates"
 ]
 },
 "TaxonKey is Annonaceae"
 ]
}

 The dataset includes 97307 records from 352 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008725-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4nac9r
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cota dubia (Steven) Holub",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 9 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079749-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dk7wvw
title: Occurrence Download

description: A dataset listing the 6 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aeshna juncea (Linnaeus, 1758)"
 ]
}

 The dataset's 6 records were derived from 261 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0259556-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7v1rhv
title: Occurrence Download

description: A dataset containing 54807 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ixodida"
 ]
}

 The dataset includes 54807 records from 370 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009600-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bf8ay9
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dioscorea macrantha Uline ex R.Knuth"
 ]
}

 The dataset includes 15 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0154504-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f8sugm
title: Occurrence Download

description: A dataset containing 12167 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cotoneaster acutifolius Turcz.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 12167 records from 179 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079768-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6fk9kb
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cyrtandra detzneriana Schltr."
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016635-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pltgqf
title: Occurrence Download

description: A dataset containing 502 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Specimen",
 "BasisOfRecord is Literature Occurrence",
 "BasisOfRecord is Living Specimen"
 ]
 },
 "Geometry POLYGON((-105.31494 18.31281,-95.82275 18.31281,-95.82275 20.59165,-105.31494 20.59165,-105.31494 18.31281))",
 "HasCoordinate is true",
 "TaxonKey is Lithobates berlandieri (Baird, 1859)",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 502 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016074-190621201848488/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.93b454
title: Occurrence Download

description: A dataset containing 108 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Amastridium sapperi (Werner, 1903)"
 ]
}

 The dataset includes 108 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016817-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5dwee6
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cyperus thomasii G.C.Tucker"
 ]
}

 The dataset includes 3 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0154505-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7k5x89
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lepidoblepharis festae (Peracca, 1897)"
 ]
}

 The dataset includes 24 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008776-210819072339941/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7mqibd
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "HasCoordinate is true",
 "TaxonKey is Lasius mexicanus Wheeler & W.M., 1914",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 7 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000183-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zhqjuh
title: Occurrence Download

description: A dataset containing 70 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cota segetalis (Ten.) Holub",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 70 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079759-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zrnaep
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dendrophthora fanshawei (Maguire) Kuijt"
 ]
}

 The dataset includes 13 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0154506-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x7xzk2
title: Occurrence Download

description: A dataset listing the 213 species recorded in GBIF matching the query:
{
 "TaxonKey" : [
 "is Thelebolaceae"
 ]
}

 The dataset's 213 records were derived from 162 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0324968-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uqvtot
title: Occurrence Download

description: A dataset containing 17463 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-95 16,-93 16,-93 18,-95 18,-95 16))",
 "HasCoordinate is true",
 "TaxonKey is Reptilia",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 17463 records from 60 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000186-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vrva9m
title: Occurrence Download

description: A dataset containing 274571 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hygrophila costata Nees, Hygrophila difformis (L.fil.) Blume, Hygrophila balsamica (L.fil.) Raf., Hygrophila corymbosa (Blume) Lindau, Hygrophila erecta (Burm.fil.) Hochr., Hygrophila polysperma (Roxb.) T.Anderson, Hygrophila ringens (L.) R.Br., Hygrophila auriculata (Schumach.) Heine, Hygrophila triflora (Nees) Fosberg & Sachet, Hygrophila biplicata (Nees) Sreem., Hygrophila abyssinica (Hochst. ex Nees) T.Anderson, Hygrophila linearis Burkill, Justicia americana (L.) Vahl, Acanthaceae, Justicia laevilinguis (Nees) Lindau)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 274571 records from 816 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0154487-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7swbq3
title: Occurrence Download

description: A dataset containing 759 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Combretum kraussii Hochst."
 ]
}

 The dataset includes 759 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000176-250127130748423/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.28287584
title: Consolidated Data for 23736543 Nomagugu P Shange.pdf (Key informant interviews)

description: Key informant interviews of 29 respondents.
DOI: 10.6084/m9.figshare.28287584.v1
title: Consolidated Data for 23736543 Nomagugu P Shange.pdf (Key informant interviews)

description: Key informant interviews of 29 respondents.
DOI: 10.15468/dl.2v95sm
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tuta absoluta (Meyrick, 1917)",
 "Country is Brazil",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000179-250127130748423/datasets/export for details.
DOI: 10.15468/dl.jacwfh
title: Occurrence Download

description: A dataset containing 38932 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Apricaphanius iberus (Valenciennes, 1846), Apricaphanius baeticus (Doadrio, Carmona & Fernández-Delgado, 2002), Gambusia holbrooki Girard, 1859)",
 "OccurrenceStatus is one of (Present)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 },
 "Year is greater than or equal to 1970"
 ]
}

 The dataset includes 38932 records from 165 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000177-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m8u4gr
title: Occurrence Download

description: A dataset containing 339 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Millettia warneckei Harms"
 ]
}

 The dataset includes 339 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000181-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/yvjwrhtmpg.1
title: Corporate governance and firm performance in Zimbabwe: moderating effect of board diversity

description: Annual data for firms listed on the Zimbabwe stock exchange (ZSE) from 2015 through to 2023.
DOI: 10.25318/3310063801-eng
title: Business or organization expectations of how long supply chain obstacles will last, first quarter of 2023

description: Business or organization expectations of how long supply chain obstacles will last, by North American Industry Classification System (NAICS), business employment size, type of business, business activity and majority ownership, first quarter of 2023.
DOI: 10.17632/5n2wv7jjbv.1
title: Estrategias de evaluación Formativa

description: Documentos y bases de datos anonimizadas que se utilizan de base para el análisis y producción del artículo.
DOI: 10.5281/zenodo.14056079
title: GLEAM4

description: The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms that separatelyestimate the different components of terrestrial evaporation (i.e. 'evapotranspiration') based onsatellite observations: transpiration (Et), interception loss (Ei), bare-soil evaporation (Eb), snowsublimation (Es) and open-water evaporation (Ew). Intermediate outputs of the model includepotential evaporation (Ep), root-zone soil moisture (SMrz), surface soil moisture (SMs), evaporativestress (S), and surface sensible heat flux (H). The rationale of the method is to maximize the recoveryof information about evaporation contained in the available data stack of climatic and environmentalobservations from space.Compared to previous GLEAM versions, GLEAM4 includes the following updates:



Hybrid learning of evaporative stress from eddy-covariance and sapflow data

Improved representation of interception, including short vegetation

Potential evaporation based on Penman's instead of Priestley and Taylor's equation

Explicit consideration of plant access to groundwater

Higher spatial resolution (0.1°) and longer record (1980–2023)


GLEAM4.2a data are accesible via www.gleam.eu.
DOI: 10.5281/zenodo.14056080
title: GLEAM4

description: The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms that separatelyestimate the different components of terrestrial evaporation (i.e. 'evapotranspiration') based onsatellite observations: transpiration (Et), interception loss (Ei), bare-soil evaporation (Eb), snowsublimation (Es) and open-water evaporation (Ew). Intermediate outputs of the model includepotential evaporation (Ep), root-zone soil moisture (SMrz), surface soil moisture (SMs), evaporativestress (S), and surface sensible heat flux (H). The rationale of the method is to maximize the recoveryof information about evaporation contained in the available data stack of climatic and environmentalobservations from space.Compared to previous GLEAM versions, GLEAM4 includes the following updates:



Hybrid learning of evaporative stress from eddy-covariance and sapflow data

Improved representation of interception, including short vegetation

Potential evaporation based on Penman's instead of Priestley and Taylor's equation

Explicit consideration of plant access to groundwater

Higher spatial resolution (0.1°) and longer record (1980–2023)


GLEAM4.1a data are accesible via www.gleam.eu.
DOI: 10.5281/zenodo.14616148
title: GLEAM4

description: The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms that separatelyestimate the different components of terrestrial evaporation (i.e. 'evapotranspiration') based onsatellite observations: transpiration (Et), interception loss (Ei), bare-soil evaporation (Eb), snowsublimation (Es) and open-water evaporation (Ew). Intermediate outputs of the model includepotential evaporation (Ep), root-zone soil moisture (SMrz), surface soil moisture (SMs), evaporativestress (S), and surface sensible heat flux (H). The rationale of the method is to maximize the recoveryof information about evaporation contained in the available data stack of climatic and environmentalobservations from space.Compared to previous GLEAM versions, GLEAM4 includes the following updates:



Hybrid learning of evaporative stress from eddy-covariance and sapflow data

Improved representation of interception, including short vegetation

Potential evaporation based on Penman's instead of Priestley and Taylor's equation

Explicit consideration of plant access to groundwater

Higher spatial resolution (0.1°) and longer record (1980–2023)


GLEAM4.2a data are accesible via www.gleam.eu.
DOI: 10.5281/zenodo.14724263
title: GLEAM4

description: The Global Land Evaporation Amsterdam Model (GLEAM) is a set of algorithms that separatelyestimate the different components of terrestrial evaporation (i.e. 'evapotranspiration') based onsatellite observations: transpiration (Et), interception loss (Ei), bare-soil evaporation (Eb), snowsublimation (Es) and open-water evaporation (Ew). Intermediate outputs of the model includepotential evaporation (Ep), root-zone soil moisture (SMrz), surface soil moisture (SMs), evaporativestress (S), and surface sensible heat flux (H). The rationale of the method is to maximize the recoveryof information about evaporation contained in the available data stack of climatic and environmentalobservations from space.Compared to previous GLEAM versions, GLEAM4 includes the following updates:



Hybrid learning of evaporative stress from eddy-covariance and sapflow data

Improved representation of interception, including short vegetation

Potential evaporation based on Penman's instead of Priestley and Taylor's equation

Explicit consideration of plant access to groundwater

Higher spatial resolution (0.1°) and longer record (1980–2023)


GLEAM4.2a data are accesible via www.gleam.eu.
DOI: 10.17632/dynjsb8z3v.1
title: uncertainty_perception_managers

description: This dataset provides the results of a survey of 28 project managers that captured their perceptions of frequency, magnitude, and signs of uncertainty in southwestern Colombia
DOI: 10.17632/p3h8r89kc2.1
title: SDN UDP Flood Dataset

description: The dataset has been created by simulating a UDP Flood DDOS attack using a Mininet emulator in the SDN Environment. 
The Dataset contains 84 columns and 1,56,908 rows.
DOI: 10.17632/pkxzpw49g8.1
title: Dataset-ISWA_200479

description: The data in the CSV file represents an initial collection used in the system developed for synthesizing emotional expressions using Generative Adversarial Networks (GAN). This dataset captures key metrics and features relevant to evaluating the quality of the generated images and the system's performance under different conditions. The main variables included are described below:
1. Emotion: This column categorizes the emotions generated by the system, covering four central states: Joy, Sadness, Surprise, and Anger. These emotions are critical to assess the model's ability to capture various emotional expressions.
2. SSIM (Structural Similarity Index): This index indicates the structural quality of the generated images, comparing them to authentic expressions. Values range from 0 to 1, with higher values reflecting greater visual and structural similarity.
3. LPIPS (Learned Perceptual Image Patch Similarity) evaluates the visual perception of the generated images. This indicator complements the SSIM and provides a metric more aligned with human perception. Lower values are available for high-quality photos.
4. Precision: Represents the percentage of emotions correctly generated by the system, highlighting its precision in emotional synthesis. It is measured as a percentage value that reflects the model's fidelity in emotional transmission.
5. False Positives: This indicator counts the cases in which the system incorrectly classified a non-existent emotion. It is key to analyzing the model's robustness and ability to avoid classification errors.
6. False Negativesrecords cases where the system failed to detect a present emotion. This indicator is critical to understanding the model's limitations in more complex situations.
7. CPU Usage (%): Represents the percentage of CPU usage during emotion processing, reflecting the system's efficiency in terms of computational resources.
8. GPU Usage (%): Similar to CPU usage, this variable measures the percentage of GPU usage, which is key in executing operations related to deep networks.
9. Memory Usage (MB): This indicates memory consumption during processing, evaluating the system's scalability under high-demand conditions.
10. Frame Rate (FPS): Measures the number of frames processed per second, an essential factor in maintaining a fluid real-time experience.
11. Response Time (ms): This represents the total time from data capture to the generation of the emotional expression. It is essential for evaluating the system's capacity in interactive and high-load scenarios.

This data provides a comprehensive overview of system performance regarding accuracy, generation quality, resource usage, and real-time responsiveness. By analyzing them, areas of improvement and optimization of the model can be identified for applications in virtual educational environments and other interactive platforms.
DOI: 10.17632/2mgfz324rb
title: Code-ISWA_200479

description: The developed code represents an initial version of the system for synthesizing emotional expressions using Generative Adversarial Networks (GAN). This model is composed of a generator that creates images based on latent representations and a discriminator that evaluates the authenticity of these images. The system uses data such as visual quality metrics (SSIM, LPIPS) and performance characteristics to train and evaluate its performance.

The CSV file is an initial data source, providing the metrics necessary to simulate the system environment. Although this implementation does not represent the final version, it offers a functional approximation to test basic concepts and adjust key parameters. Due to copyright restrictions, the complete and optimized version of the system described in the article is not included.
DOI: 10.17632/2mgfz324rb.1
title: Code-ISWA_200479

description: The developed code represents an initial version of the system for synthesizing emotional expressions using Generative Adversarial Networks (GAN). This model is composed of a generator that creates images based on latent representations and a discriminator that evaluates the authenticity of these images. The system uses data such as visual quality metrics (SSIM, LPIPS) and performance characteristics to train and evaluate its performance.

The CSV file is an initial data source, providing the metrics necessary to simulate the system environment. Although this implementation does not represent the final version, it offers a functional approximation to test basic concepts and adjust key parameters. Due to copyright restrictions, the complete and optimized version of the system described in the article is not included.
DOI: 10.17632/jx58r4nw2z.1
title: Some 3D reconstructions of SEM images and their .obj files

description: As told by Shanklin, 2016, 2D SEM images can be turned into 3D object models. “3D surface modeling that can be created using scanning electron microscope absolutely lead to significant understanding of attributes of microscopic surfaces, such as fracture toughness, crack growth and propagation or fracture resistance” (Shanklin, 2016). I considered SEM images, turned them into .ppm format. The .ppm file has been read by a Fortran program to create the 3D mesh, by means of vertices and faces, saved in .obj file format (see please the folder in the dataset). Here I show some cases: Honeycomb, Pores of freeze-dried solutions, Microcellular plastic, Biochar, Wood pores, 'Hexagon' detail of Corbaea scandens, Pollen, Worms, that is a pair of Schistosoma mansoni, and a 'Bassorilievo' and a rendering of Turin Shroud, to show that is it possible to obtain a 3D mesh from pictures. Details and references are given in the .pdf file. Visualizations of .obj files have been obtained by means of https://3dviewer.net/ and GIMP software.