cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e224462
title: USAID DHS Indicator Data

description: This collection consists of summary data for countries, country subdivisions, and demographic categories that were generated from USAID's Demographic Health Survey (DHS). The indicators are population-level estimates that were generated from sample surveys that were conducted in over 90 low and middle income countries at various points over several decades. Almost 2,000 indicators capture information related to: fertility, family planning, maternal and child health, gender, HIV/AIDS, literacy, malaria, nutrition, and sanitation.<br><br>Each set of files is associated with a specific health survey for a given year and country. The surveys include:<br>AIDS Indicator Survey (AIS)Demographic Health Survey (DHS)Malaria Indicator Survey (MIS)Service Provisions Assessment (SPA)Other qualitative surveys (OTH)The top-level folder includes a list of country codes and field names 
used in all of the data files, and a file list with a record count. The data was extracted from the DHS program's API, and is structured as follows:<br>Datasets are organized in folders that are named with a two-letter country code, survey name, and year.Each survey has three sets of files: a National file with country-level records, a Subnational file with country subdivision-level records, and a Background file with categorical records (such as urban / rural, education level, income level, etc.)Each set of files comes in two formats: a long-format JSON file with key-value pairs of labels and values, and a wide-format CSV file where labels have been pivoted to column headings.A _CODEBOOKS folder contains CSV, JSON, and HTML files that describe the variables and all coded values, and PDF documentation for the overall DHS statistics program.Reports associated with a specific survey that were available via the API are stored in the survey folder with the data.A PUBS_NODATA folder includes reports that were available via the API, but that did not have machine-readable data associated with them.Countries for which data was included in the indicators API include:<br><br>Afghanistan, Albania, Angola, Armenia, Azerbaijan, Bangladesh, Benin, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cape Verde, Cambodia, Cameroon, Central African Republic, Chad, Colombia, Comoros, Congo, Congo (Democratic Republic of the), Cote d'Ivoire, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Eswatini (Swaziland), Ethiopia, Gabon, Gambia, Ghana, Guatemala, Guinea, Guyana, Haiti, Honduras, India, Indonesia, Jordan, Kazakhstan, Kenya, Kyrgyzstan, Laos, Lesotho, Liberia, Madagascar, Malawi, Maldives, Mali, Mauritania, Mexico, Moldova, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Papua New Guinea, Paraguay, Peru, Philippines, Rwanda, Samoa, Sao Tome and Principe, Senegal, Sierra Leone, South Africa, Sri Lanka, Sudan, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, Uzbekistan, Viet Nam, Yemen, Zambia, Zimbabwe<br><br><br>
description: Countries that participated in DHS surveys<br>.
DOI: 10.3886/e224462v1
title: USAID DHS Indicator Data

description: This collection consists of summary data for countries, country subdivisions, and demographic categories that were generated from USAID's Demographic Health Survey (DHS). The indicators are population-level estimates that were generated from sample surveys that were conducted in over 90 low and middle income countries at various points over several decades. Almost 2,000 indicators capture information related to: fertility, family planning, maternal and child health, gender, HIV/AIDS, literacy, malaria, nutrition, and sanitation.<br><br>Each set of files is associated with a specific health survey for a given year and country. The surveys include:<br>AIDS Indicator Survey (AIS)Demographic Health Survey (DHS)Malaria Indicator Survey (MIS)Service Provisions Assessment (SPA)Other qualitative surveys (OTH)The top-level folder includes a list of country codes and field names 
used in all of the data files, and a file list with a record count. The data was extracted from the DHS program's API, and is structured as follows:<br>Datasets are organized in folders that are named with a two-letter country code, survey name, and year.Each survey has three sets of files: a National file with country-level records, a Subnational file with country subdivision-level records, and a Background file with categorical records (such as urban / rural, education level, income level, etc.)Each set of files comes in two formats: a long-format JSON file with key-value pairs of labels and values, and a wide-format CSV file where labels have been pivoted to column headings.A _CODEBOOKS folder contains CSV, JSON, and HTML files that describe the variables and all coded values, and PDF documentation for the overall DHS statistics program.Reports associated with a specific survey that were available via the API are stored in the survey folder with the data.A PUBS_NODATA folder includes reports that were available via the API, but that did not have machine-readable data associated with them.Countries for which data was included in the indicators API include:<br><br>Afghanistan, Albania, Angola, Armenia, Azerbaijan, Bangladesh, Benin, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cape Verde, Cambodia, Cameroon, Central African Republic, Chad, Colombia, Comoros, Congo, Congo (Democratic Republic of the), Cote d'Ivoire, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Eswatini (Swaziland), Ethiopia, Gabon, Gambia, Ghana, Guatemala, Guinea, Guyana, Haiti, Honduras, India, Indonesia, Jordan, Kazakhstan, Kenya, Kyrgyzstan, Laos, Lesotho, Liberia, Madagascar, Malawi, Maldives, Mali, Mauritania, Mexico, Moldova, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Papua New Guinea, Paraguay, Peru, Philippines, Rwanda, Samoa, Sao Tome and Principe, Senegal, Sierra Leone, South Africa, Sri Lanka, Sudan, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, Uzbekistan, Viet Nam, Yemen, Zambia, Zimbabwe<br><br><br>
description: Countries that participated in DHS surveys<br>.
DOI: 10.15468/dl.jd4q49
title: Occurrence Download

description: A dataset containing 163 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ranunculus acris L.",
 "Country is Andorra"
 ]
}

 The dataset includes 163 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004551-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7ns4vx
title: Occurrence Download

description: A dataset containing 41 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is USA.1_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Sarracenia purpurea L."
 ]
}

 The dataset includes 41 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004552-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15059173
title: All-Diamond Boron-Doped Microelectrodes for Neurochemical Sensing with Fast-Scan Cyclic Voltammetry

description: Datasets used in the manuscript titled: "All-Diamond Boron-Doped Microelectrodes for Neurochemical Sensing with Fast-Scan Cyclic Voltammetry" are uploaded here.

 

Data contents:

Figure 2

·         Data for slowscan DA, FcCOOH, and RuHex. File types are .bin, opened with CHInstruments potentiostat software.

Figure 3

·         FSCV calibration curve for DA and 5-HT. Data can be opened with HDCV available from UNC.

Figure 4

·         FSCV measurements for DA and 5-HT. Data can be opened with HDCV available from UNC.

SI-1

·         Impedance and CV data can be open with CHInstruments potentiostat software, and CV data can be opened with notepad.

SI-2

·         FSCV data of DA can be opened with HDCV from UNC

SI-3

·         FSCV data of pH can be opened with HDCV from UNC

SI-4

·         Raman data of BDD. Data can be opened with Renshaw analysis software

SI-5

·         FSCV calibration response of DOPAC, AA, and H2O2. Data can be opened with HDCV from UNC.

SI-7

·         In vivo data of BDDME in glass. Data can be opened with HDCV from UNC.

 

Table 1

·         Calculations for the electron transfer coefficient. Data can be opened with Excel.
DOI: 10.15468/dl.fxy7ed
title: Occurrence Download

description: A dataset containing 138 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is USA.1_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Woodwardia virginica (L.) Sm."
 ]
}

 The dataset includes 138 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004554-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11532112
title: Recursive InPainting with Stable Difussion 2 Inpainting

description: Recursive inpainting with Stable Difussion

The dataset contains images extracted from WikiArt that have been recursively versioned using inpainting techniques with a version of the Stable Diffusion model oriented towards instructional inpainting called stable-diffusion-2-inpainting.



 














The dataset contains two folders with two different experiments. Each experiment consists of performing recursive inpainting on 512x512 px images until reaching a percentage of pixels affected by the inpainting of 400% compared to the original image. Each experiment contains the following files:



original_imgs_grid.png file with all the base images in an ordered grid.

mask_64x64/[number_figure]_64x64.png with the inpainting version of [number_figure] and 64x64 mask

mask_128x128/[number_figure]_128x128.png with the inpainting version of [number_figure] and 128x128 mask

mask_256x256/[number_figure]_256x256.png with the inpainting version of [number_figure] and 256x256 mask

lpips_distances.xlsx with the results of applying the Learned Perceptual Image Path Similarity (LPIPS) metric in its variants SqueezeNet, AlexNet, and VGG. The metrics are calculated with respect to the original image and the previous image.


There are two different experiments:



lpips_distances_100_images_1_seed: contains recursive inpaintings of 100 different images with a single seed. This allows the analysis of different images when performing recursive inpainting.

lpips_distances_10_images_15_seeds: contains recursive inpaintings of 10 different images with 15 different seeds. This allows the analysis of the differences in inpaintings when applying different seeds in the inpainting on the same image.
DOI: 10.5281/zenodo.11532401
title: Recursive InPainting with Stable Difussion 2 Inpainting

description: Recursive inpainting with Stable Difussion

The dataset contains images extracted from WikiArt that have been recursively versioned using inpainting techniques with a version of the Stable Diffusion model oriented towards instructional inpainting called stable-diffusion-2-inpainting.



 














The dataset contains two folders with two different experiments. Each experiment consists of performing recursive inpainting on 512x512 px images until reaching a percentage of pixels affected by the inpainting of 400% compared to the original image. Each experiment contains the following files:



original_imgs_grid.png file with all the base images in an ordered grid.

mask_64x64/[number_figure]_64x64.png with the inpainting version of [number_figure] and 64x64 mask

mask_128x128/[number_figure]_128x128.png with the inpainting version of [number_figure] and 128x128 mask

mask_256x256/[number_figure]_256x256.png with the inpainting version of [number_figure] and 256x256 mask

lpips_distances.xlsx with the results of applying the Learned Perceptual Image Path Similarity (LPIPS) metric in its variants SqueezeNet, AlexNet, and VGG. The metrics are calculated with respect to the original image and the previous image.


There are two different experiments:



lpips_distances_100_images_1_seed: contains recursive inpaintings of 100 different images with a single seed. This allows the analysis of different images when performing recursive inpainting.

lpips_distances_10_images_15_seeds: contains recursive inpaintings of 10 different images with 15 different seeds. This allows the analysis of the differences in inpaintings when applying different seeds in the inpainting on the same image.
DOI: 10.5281/zenodo.11532453
title: Recursive InPainting with Stable Difussion 2 Inpainting

description: Recursive inpainting with Stable Difussion

The dataset contains images extracted from WikiArt that have been recursively versioned using inpainting techniques with a version of the Stable Diffusion model oriented towards instructional inpainting called stable-diffusion-2-inpainting.



 














The dataset contains two folders with two different experiments. Each experiment consists of performing recursive inpainting on 512x512 px images until reaching a percentage of pixels affected by the inpainting of 400% compared to the original image. Each experiment contains the following files:



original_imgs_grid.png file with all the base images in an ordered grid.

mask_64x64/[number_figure]_64x64.png with the inpainting version of [number_figure] and 64x64 mask

mask_128x128/[number_figure]_128x128.png with the inpainting version of [number_figure] and 128x128 mask

mask_256x256/[number_figure]_256x256.png with the inpainting version of [number_figure] and 256x256 mask

lpips_distances.xlsx with the results of applying the Learned Perceptual Image Path Similarity (LPIPS) metric in its variants SqueezeNet, AlexNet, and VGG. The metrics are calculated with respect to the original image and the previous image.


There are two different experiments:



lpips_distances_100_images_1_seed: contains recursive inpaintings of 100 different images with a single seed. This allows the analysis of different images when performing recursive inpainting.

lpips_distances_10_images_15_seeds: contains recursive inpaintings of 10 different images with 15 different seeds. This allows the analysis of the differences in inpaintings when applying different seeds in the inpainting on the same image.
DOI: 10.5281/zenodo.11574941
title: Recursive InPainting with Stable Difussion 2 Inpainting

description: Recursive inpainting with Stable Difussion

The dataset contains images extracted from WikiArt that have been recursively versioned using inpainting techniques with a version of the Stable Diffusion model oriented towards instructional inpainting called stable-diffusion-2-inpainting.



 














The dataset contains two folders with two different experiments. Each experiment consists of performing recursive inpainting on 512x512 px images until reaching a percentage of pixels affected by the inpainting of 400% compared to the original image. Each experiment contains the following files:



original_imgs_grid.png file with all the base images in an ordered grid.

mask_64x64/[number_figure]_64x64.png with the inpainting version of [number_figure] and 64x64 mask

mask_128x128/[number_figure]_128x128.png with the inpainting version of [number_figure] and 128x128 mask

mask_256x256/[number_figure]_256x256.png with the inpainting version of [number_figure] and 256x256 mask

lpips_distances.xlsx with the results of applying the Learned Perceptual Image Path Similarity (LPIPS) metric in its variants SqueezeNet, AlexNet, and VGG. The metrics are calculated with respect to the original image and the previous image.


There are two different experiments:



lpips_distances_100_images_1_seed: contains recursive inpaintings of 100 different images with a single seed. This allows the analysis of different images when performing recursive inpainting.

lpips_distances_10_images_15_seeds: contains recursive inpaintings of 10 different images with 15 different seeds. This allows the analysis of the differences in inpaintings when applying different seeds in the inpainting on the same image.


The folder code includes the a README.md with the instructions and the scripts to run the experiments
DOI: 10.5281/zenodo.13857240
title: Recursive InPainting with Stable Difussion 2 Inpainting

description: Recursive inpainting with Stable Difussion

The dataset contains images extracted from WikiArt that have been recursively versioned using inpainting techniques with a version of the Stable Diffusion model oriented towards instructional inpainting called stable-diffusion-2-inpainting, used in the paper How Stable is Stable Diffusion under Recursive InPainting (RIP)? Presented at the GenAI Evaluation KDD2024: KDD workshop on Evaluation and Trustworthiness of Generative AI Models



 




The dataset contains two folders with two different experiments. Each experiment consists of performing recursive inpainting on 512x512 px images until reaching a percentage of pixels affected by the inpainting of 400% compared to the original image. Each experiment contains the following files:



original_imgs_grid.png* file with all the base images in an ordered grid or dataset folder with the original images.

mask_64x64/[number_figure]_64x64.png* with the inpainting version of [number_figure] and 64x64 mask

mask_128x128/[number_figure]_128x128.png* with the inpainting version of [number_figure] and 128x128 mask

mask_256x256/[number_figure]_256x256.png* with the inpainting version of [number_figure] and 256x256 mask

lpips_distances.xlsx with the results of applying the Learned Perceptual Image Path Similarity (LPIPS) metric in its variants SqueezeNet, AlexNet, and VGG. The metrics are calculated with respect to the original image and the previous image.


* Only images that license allow to share

There are three different experiments:



lpips_distances_100_images_1_seed: contains recursive inpaintings of 100 different images with a single seed. This allows the analysis of different images when performing recursive inpainting.

lpips_distances_10_images_15_seeds: contains recursive inpaintings of 10 different images with 15 different seeds. This allows the analysis of the differences in inpaintings when applying different seeds in the inpainting on the same image.

lpisp_distances_styles: contains recursive inpaintings of 80 images taken from seven pictorial styles and from sketches of an architect in subsets to 10 images.



Styles:

Mods folder: contains the same inpaintings but for modifications of the base image:



Black_White

Blue_Green: version with no red components.

Red_Blue: version with no green components.

Red_Green: version with no blue components.






The folder code includes the a README.md with the instructions and the scripts to run the experimentsCite our work in:

@misc{conde2024stablestablediffusionrecursive,
      title={How Stable is Stable Diffusion under Recursive InPainting (RIP)?}, 
      author={Javier Conde and Miguel González and Gonzalo Martínez and Fernando Moral and Elena Merino-Gómez and Pedro Reviriego},
      year={2024},
      eprint={2407.09549},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2407.09549}, 
}
DOI: 10.15468/dl.6zwhtu
title: Occurrence Download

description: A dataset containing 330 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pseudoloricaria laeviuscula (Valenciennes, 1840)"
 ]
}

 The dataset includes 330 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004555-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j4ufd7
title: Occurrence Download

description: A dataset containing 225 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "CoordinateUncertaintyInMeters 10000.0-1000000.0",
 "Geometry POLYGON((-126.38263 25.00363,-66.69949 25.00363,-66.69949 48.89319,-126.38263 48.89319,-126.38263 25.00363))",
 "TaxonKey is Bromus tectorum L."
 ]
}

 The dataset includes 225 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004558-250325103851331/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.28680413
title: Environmental heterogeneity as a determinant of bee diversity patterns in the Atlantic Forest

description: Species occurrence and variables used to construct species distribution models and environmental heterogeneity used in the analyses.<b>occurrences_cleaned_final_FILTERED</b>: cleaned occurrences of bee species native to the Atlantic Forest without correcting the sampling bias (i.e., unfiltered). <b>occurrences_cleaned_final_UNFILTERED</b>: cleaned and bias-corrected species occurrences of bee species native to the Atlantic Forest. Sampling bias was addressed by filtering species in the environmental space. Both datasets have the following columns id: occurrence IDspecies: binomial species namesx: longitude in decimal degree y: latitude in decimal degree<b>Predictor variables SDM</b><b>.zip</b>: Predictor variables in TIF format used to construct Species Distribution Models<b>heterogeneity</b><b>.tif: </b>A multilayer raster in TIF format with environmental heterogeneity variables used in the analyses<br><br>
DOI: 10.15468/dl.w28p5z
title: Occurrence Download

description: A dataset containing 19012 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-50.04239 -6.74958,-49.83134 -6.53847,-49.79943 -6.34184,-49.80315 -6.32206,-49.81107 -6.31028,-49.81187 -6.30996,-49.93192 -6.26454,-49.95428 -6.26206,-50.37827 -6.39743,-50.37954 -6.39796,-50.37956 -6.39798,-50.37983 -6.3982,-50.37984 -6.39821,-50.38046 -6.40192,-50.37544 -6.52291,-50.10741 -6.73079,-50.04239 -6.74958))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 19012 records from 93 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004556-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xjb9hf
title: Occurrence Download

description: A dataset containing 108 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ranunculus acris L.",
 "Country is Liechtenstein"
 ]
}

 The dataset includes 108 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004559-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ebdu4h
title: Occurrence Download

description: A dataset containing 76 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 "TaxonKey is one of (Rhinella nicefori (Cochran & Goin, 1970))"
 ]
}

 The dataset includes 76 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004560-250325103851331/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.