cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.4spa86
title: Occurrence Download

description: A dataset containing 82 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Living Specimen",
 "BasisOfRecord is Unknown evidence",
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Observation",
 "BasisOfRecord is Material sample",
 "BasisOfRecord is Machine Observation"
 ]
 },
 "Country is Mexico",
 "HasCoordinate is true",
 "TaxonKey is Cheilanthes Sw.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 82 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013499-190415153152247/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc2hxfnj
title: CCDC 2321238: Experimental Crystal Structure Determination

description: Related Article: Boniface Fokwa|2023|CSD Communication|||
description: Related Article: Serges A.M. Mejonang, Bridget N. Ndosiri, Ledoux S. Pouamo, Idelle N. Kamga, Colince Nde, Mohammad Azam, Justin Nenwa|2024|J.Mol.Struct.|1313|138737|doi:10.1016/j.molstruc.2024.138737
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.oyzfur
title: Occurrence Download

description: A dataset containing 270 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cordia morelosana Standl."
 ]
}

 The dataset includes 270 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011267-180730143533302/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gu0pvl
title: Occurrence Download

description: A dataset containing 2672 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Phalaropus tricolor (Vieillot, 1819)"
 ]
}

 The dataset includes 2672 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002717-200127171203522/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gzp14v
title: Occurrence Download

description: A dataset containing 66212 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Impatiens glandulifera Royle"
 ]
}

 The dataset includes 66212 records from 319 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013506-151016162008034/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dfhgvb
title: Occurrence Download

description: A dataset containing 12369 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pterocarpus erinaceus Poir."
 ]
}

 The dataset includes 12369 records from 159 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058685-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6o1fbj
title: Occurrence Download

description: A dataset containing 69 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Mimosa orthocarpa Spruce ex Benth.",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 69 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011268-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hpnxmu
title: Occurrence Download

description: A dataset containing 1959 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "TaxonKey is Momotus momota (Linnaeus, 1766)"
 ]
}

 The dataset includes 1959 records from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006900-200127171203522/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cg8lvb
title: Occurrence Download

description: A dataset containing 3486 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Estudios bióticos (Plantas, Fauna Edáfica, Anfibios y Aves) en los Complejo de Páramos Frontino-Urrao y Citará"
 ]
}

 The dataset includes 3486 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013500-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qstjrb
title: Occurrence Download

description: A dataset containing 12466 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "TaxonKey is Ardeola bacchus (Bonaparte, 1855)"
 ]
 },
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 12466 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011268-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4iqsxn
title: Occurrence Download

description: A dataset containing 410 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hypelate trifoliata Sw."
 ]
}

 The dataset includes 410 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013501-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t5wcej
title: Occurrence Download

description: A dataset listing the 6226 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is Medicinal plants and associated plant collections at Ghana Herbarium"
 ]
}

 The dataset's 6226 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015363-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.lbzy08
title: Occurrence Download

description: A dataset containing 1618 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is South Africa",
 "TaxonKey is Cotyledon L."
 ]
}

 The dataset includes 1618 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011269-180730143533302/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ylx0zh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Specimen",
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Material sample",
 "BasisOfRecord is Literature Occurrence",
 "BasisOfRecord is Machine Observation",
 "BasisOfRecord is Observation"
 ]
 },
 "HasCoordinate is true",
 "TaxonKey is Fabaceae",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 2615 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015405-200127171203522/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4xqtdw
title: Occurrence Download

description: A dataset containing 166307 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Squalus acanthias Linnaeus, 1758"
 ]
}

 The dataset includes 166307 records from 245 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045178-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.js7rkn
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Begonia longiscapa Warb."
 ]
}

 The dataset includes 37 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054924-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.frwh6x
title: Occurrence Download

description: A dataset containing 112809 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Erica arborea L."
 ]
}

 The dataset includes 112809 records from 196 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002712-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yfyd6t
title: Occurrence Download

description: A dataset containing 23266 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((35.32496433333334 32.097476, 69.37031999999999 32.097476, 69.37031999999999 49.184106666666665, 35.32496433333334 49.184106666666665, 35.32496433333334 32.097476))",
 "EventDate is 2020-09-01,2020-09-30"
 ]
}

 The dataset includes 23266 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045199-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hzqwb7
title: Occurrence Download

description: A dataset listing the 29 species recorded in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "Country is one of (Colombia)",
 "TaxonKey is one of (Munidopsis Whiteaves, 1874)"
 ]
 }
 ]
}

 The dataset's 29 records were derived from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010582-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xboahg
title: Occurrence Download

description: A dataset listing the 111258 species recorded in GBIF matching the query:
{
 "Country" : [
 "is Colombia"
 ]
}

 The dataset's 111258 records were derived from 2074 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002717-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dpeu3w
title: Occurrence Download

description: A dataset containing 74 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is Nepal",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Manis pentadactyla Linnaeus, 1758"
 ]
}

 The dataset includes 74 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000489-250212154643175/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.62b64a
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Eurycea nerea (Bishop, 1944))"
 ]
}

 The dataset includes 24 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000490-250212154643175/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/8xyxzg7czv.1
title: Good and Bad classification of Cooked Maggi

description: *Data Description: Good and Bad Classification of Cooked Maggi* 

This dataset is designed for binary classification, distinguishing between *good* and *bad* samples of cooked Maggi. It consists of *1,000 total samples*, with an equal distribution: 

- *500 Good Samples* – Representing well-cooked Maggi that meets desirable quality standards. 
- *500 Bad Samples* – Representing improperly cooked Maggi, which may include undercooked, overcooked, burnt, or soggy variations. 

### *Dataset Attributes* 

Each sample is characterized by various features, which may include: 

#### *1. Image Data (if applicable)* 
- High-resolution images of cooked Maggi for visual classification. 
- Images may capture different angles, lighting conditions, and variations in texture. 

#### *2. Physical & Sensory Features* 
- *Texture*: Softness, hardness, or stickiness. 
- *Color*: Golden-brown (good) vs. dark (burnt) or pale (undercooked). 
- *Moisture Content*: Dry, well-balanced, or excessively watery. 
- *Clumpiness*: Well-separated strands vs. sticky/mushy consistency. 

#### *3. Ingredient & Cooking Factors* 
- *Water Ratio*: Proper vs. excess/insufficient water used. 
- *Cooking Time*: Optimal vs. undercooked/overcooked. 
- *Seasoning Distribution*: Evenly mixed vs. uneven. 
- *Burnt Residue Presence*: Charred vs. clean. 

#### *4. Label (Target Variable)* 
- *0 = Bad Maggi* 
- *1 = Good Maggi* 

### *Potential Applications* 
- Machine learning model training for *automated quality assessment*. 
- Developing AI-driven *food quality monitoring systems*. 
- Enhancing *food industry automation* with real-time detection. 

This dataset is suitable for *computer vision-based classification* (if images are used) or *sensor-based analysis* for food quality control.
DOI: 10.15468/dl.447efs
title: Occurrence Download

description: A dataset containing 3497559 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "DatasetKey is iNaturalist Research-grade Observations",
 "OccurrenceStatus is Present",
 "Year 2019-2019"
 ]
}

 The dataset includes 3497559 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000480-250212154643175/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e219162
title: HUD Small Area Fair Market Rent Demonstration Evaluation Data

description: Small Area Fair Market Rent Demonstration Evaluation Data<br><br>https://www.huduser.gov/portal/datasets/SAFMR-demonstration-evaluation-data.html
DOI: 10.3886/e219162v1
title: HUD Small Area Fair Market Rent Demonstration Evaluation Data

description: Small Area Fair Market Rent Demonstration Evaluation Data<br><br>https://www.huduser.gov/portal/datasets/SAFMR-demonstration-evaluation-data.html
DOI: 10.17632/ygvf6kpgxy
title: Amorphous Ge quantum dot networks in SiC and Si3N4 matrices for application in photosensitive devices

description: It contains data including GISAXS, optical, current-voltage and spectral response of thin films consisting of Ge QDs embedded in SiC and Si3N4 matrices
DOI: 10.17632/ygvf6kpgxy.1
title: Amorphous Ge quantum dot networks in SiC and Si3N4 matrices for application in photosensitive devices

description: It contains data including GISAXS, optical, current-voltage and spectral response of thin films consisting of Ge QDs embedded in SiC and Si3N4 matrices
DOI: 10.17632/ws93jkky2h.1
title: Unravelling Healthcare Biases in Ghana: Addressing Socioeconomic, Ethnic and Structural Inequalities for Equitable Healthcare

description: This guide was used to conduct the interviews on the respondents. All the questions were centered around unravelling Healthcare Biases in Ghana
DOI: 10.17632/s7rxcxkms6.1
title: Good and Bad classification of Simui

description: The dataset for the project on the classification of cooked Simui (a type of traditional dish) consists of 1,000 samples, divided into two categories: *500 good samples* and *500 bad samples*. This dataset aims to build a classification model capable of distinguishing between well-cooked and poorly-cooked Simui. 

### *Dataset Overview:*

The samples in this dataset are based on specific characteristics of the cooked Simui, such as texture, appearance, flavor, and consistency. These qualities are commonly used by culinary experts to assess the quality of cooked Simui. The good and bad samples were carefully labeled based on sensory evaluations conducted by culinary specialists. Each sample was carefully selected to represent real-world scenarios of properly cooked versus poorly cooked Simui.

### *Class Labels:*
- *Good Simui (500 samples):* These are the samples that meet the required standards for a well-prepared Simui dish. They exhibit the right texture (neither too soft nor too dry), a pleasing appearance (uniform and smooth), and an appealing taste and aroma. The cooking method, timing, and ingredients contribute to the final quality of the dish.
 
- *Bad Simui (500 samples):* These samples represent cooked Simui that failed to meet the standards. Common characteristics of bad Simui include overcooked, undercooked, clumped, overly dry, too mushy, or having an unappetizing appearance. The dish may also have an undesirable taste, underseasoning, or improper consistency.

### *Features:*
The dataset includes a variety of features that contribute to the classification of good or bad Simui. These features may include:
1. *Texture:* The mouthfeel of Simui, categorized as soft, firm, or mushy.
2. *Appearance:* The visual quality, including color, shape, and consistency of the Simui strands.
3. *Aroma:* A descriptive analysis of the smell, where a fresh, pleasant aroma denotes good quality, while a burnt or sour smell represents a bad sample.
4. *Taste:* The flavor profile, including sweetness, saltiness, and seasoning balance. This is an important sensory aspect in distinguishing good from bad Simui.
5. *Moisture Content:* This feature measures the water content of the cooked Simui. Proper moisture level is crucial for the perfect texture.
6. *Clumping:* Whether the Simui strands remain separated or clump together in a sticky mass. Clumping is often a sign of overcooking or improper preparation.
7. *Cooking Time:* A feature to track how long the Simui was cooked. Cooking time directly influences the texture and consistency.

Each sample has been labeled according to a set of predefined quality criteria established by culinary experts, ensuring that the dataset is consistent and reliable.
DOI: 10.15468/dl.ppz3mv
title: Occurrence Download

description: A dataset containing 1152 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-70.15179 -14.38934,-69.24420 -14.38934,-69.24420 -14.02646,-70.15179 -14.02646,-70.15179 -14.38934))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 1152 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000492-250212154643175/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/tt5z57rdmx.1
title: Dataset on psychological impacts of mining pollution in Katanga Copperbelt

description: Farmers' perceptions of pollution are essential because they provide valuable insights into how environmental issues are experienced and understood by those directly affected. The dataset contains raw data on urban farmers' perceptions of mining pollution, highlighting its psychological impacts and effects on agriculture in urban farming communities. Data were collected through face-to-face interviews with urban farmers during a survey conducted in Lubumbashi between May and June 2024. Participants were recruited from vegetable production sites in Naviundu, Kilobelobe, Katuba, and Daipen Kisanga—well-known market gardening sites in Lubumbashi. Data are presented in frequencies and percentages ( in figures and tables).
DOI: 10.15468/dl.f3xc5y
title: Occurrence Download

description: A dataset containing 37006 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ligustrum sinense Lour., Ligustrum nokoensis Masam. & K.Mori, Ligustrum sinense Decne.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 37006 records from 150 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000493-250212154643175/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e219163
title: HUD California Wildfire Risk Exposure

description: These maps display wildfire risk exposure of households in the state of California. Estimates of the number of households that fall within areas with the highest wildfire risk are aggregated within small area hexagons and then displayed on the map using a color-coding scheme described in the legend. Lighter yellow hues signify the highest concentration of individual households in highest risk areas. A hexagon that is orange or yellow does not necessarily indicate that wildfire risk is highest, overall, but rather that the hexagon contains the most households in high risk areas, relative to other hexagons. These orange and yellow areas, then, are the most likely to involve conflagrations that impact large populations. Therefore, estimates of concentrations can serve as a guide to conduct further data analysis and vulnerability assessments, emergency planning, and resource allocation.MethodologyTo produce these maps, researchers used 2017 United States Postal Service (USPS) address vacancy data1. The USPS vacancy data is a quarterly updated dataset of the universe of all addresses in the United States and are, thus, the most granular geographic housing unit data available to HUD. The data of occupied residential units can be geographically plotted at the Zip+4 centroid, which typically encompasses 10-30 actual housing units. To assess wildfire risk, researchers matched Zip+4 centroids to the 2018 Wildfire Hazard Potential (WHP) Map produced by the US Forest Service2. This national wildfire risk data is an index generated from multiple data sources that measure attributes such as wildfire likelihood and intensity, fuel and vegetation, and past fire occurrences. The result is a national map of wildfire potential at 270- m.² resolution. For this analysis, Zip+4 centroids that matched with WHP risk of High or Very High are used to convey high-risk households. The maps only display counts of households that are at high or very high risk. The household counts are then aggregated within small area hexagons measuring approximately four-mi.², which are assigned a color based on the number of at-risk households.A note of caution regarding USPS vacancy data. Though this data is more granular than Census data with regard to geographic location, the Zip+4 centroids associate with capture areas of varying geographic size, such that rural Zip+4 centroids tend to include addresses that are more geographically dispersed. As a result, and because of how we used the Zip+4 centroid data to match to granular wildfire risk data, the rural data may be less accurate in flagging wildfire risk, either over-counting or under-counting households within high-risk areas.