cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21233/54ph-3530
title: SBBC0806 dinoflagellates dataset

description: Raw data for the SBBC0806 dinoflagellates dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/2pbn-ys62
title: Fiekers Busch pollen dataset

description: Raw data for the Fiekers Busch pollen dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/vryt-wg29
title: Wavy Lake water chemistry dataset

description: Raw data for the Wavy Lake water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/8sdp-ts83
title: Caswell testate amoebae surface sample dataset

description: Raw data for the Caswell testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/8txf-v296
title: Dallican Water charcoal dataset

description: Raw data for the Dallican Water charcoal dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/akg3-3z07
title: Lake Biwa, North Basin ostracode surface sample dataset

description: Raw data for the Lake Biwa, North Basin ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/25s4-t457
title: GRCA Cave CC:5:1 vertebrate fauna dataset

description: Raw data for the GRCA Cave CC:5:1 vertebrate fauna dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/z3jd-tm32
title: Fifth Depot Lake water chemistry dataset

description: Raw data for the Fifth Depot Lake water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/hs53-cn65
title: Lake Carpentaria pollen dataset

description: Raw data for the Lake Carpentaria pollen dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/06ad-9955
title: Upper Lake Mary water chemistry dataset

description: Raw data for the Upper Lake Mary water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/azbx-t372
title: LDDsite2091 water chemistry dataset

description: Raw data for the LDDsite2091 water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/hmtx-h443
title: Three Mile Lake diatom dataset

description: Raw data for the Three Mile Lake diatom dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/6z0j-m754
title: Lej Nair testate amoebae surface sample dataset

description: Raw data for the Lej Nair testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.5281/zenodo.13851576
title: Data from: Geographical and habitat factors affecting migratory waterbirds along rivers in the Palearctic

description: Rivers are important habitats for many waterbird species, offering crucial resources such as feeding and resting areas during migration. The migratory patterns of this group of birds are influenced by a variety of factors, including geographical location, habitat availability, and environmental conditions. Despite the huge ecological importance of rivers, there are few studies on how riverine environments support migratory bird populations across a broad geographical scale. This study shows waterbird populations during migration along 14 big and unregulated rivers spanning Europe and Asia, documenting a total of 63,383 birds across 112 species. Analysis revealed significant associations between species richness and bird abundance and geographical proximity, with neighboring rivers displaying greater similarity in species composition and density. The presence of sand banks and islands emerged as critical factors influencing species richness and bird abundance, while forested river segments negatively impacted species richness compared to open landscapes like fields. Unexpectedly, river width showed no significant effect on avian assemblages.

The study was conducted during the autumn migration of birds. Through a combination of ecological assessments and statistical analyses, we identified the primary drivers influencing migratory bird distribution, species richness, and abundance.
DOI: 10.5281/zenodo.13851577
title: Data from: Geographical and habitat factors affected migratory waterbirds along rivers in the Palearctic

description: Rivers are important habitats for many waterbird species, offering crucial resources such as feeding and resting areas during migration. The migratory patterns of this group of birds are influenced by a variety of factors, including geographical location, habitat availability, and environmental conditions. Despite the huge ecological importance of rivers, there are few studies on how riverine environments support migratory bird populations across a broad geographical scale. This study shows waterbird populations during migration along 14 big and unregulated rivers spanning Europe and Asia, documenting a total of 63,383 birds across 112 species. Analysis revealed significant associations between species richness and bird abundance and geographical proximity, with neighboring rivers displaying greater similarity in species composition and density. The presence of sand banks and islands emerged as critical factors influencing species richness and bird abundance, while forested river segments negatively impacted species richness compared to open landscapes like fields. Unexpectedly, river width showed no significant effect on avian assemblages.

The study was conducted during the autumn migration of birds. Through a combination of ecological assessments and statistical analyses, we identified the primary drivers influencing migratory bird distribution, species richness, and abundance.
DOI: 10.5281/zenodo.15165694
title: Data from: Geographical and habitat factors affecting migratory waterbirds along rivers in the Palearctic

description: Rivers are important habitats for many waterbird species, offering crucial resources such as feeding and resting areas during migration. The migratory patterns of this group of birds are influenced by a variety of factors, including geographical location, habitat availability, and environmental conditions. Despite the huge ecological importance of rivers, there are few studies on how riverine environments support migratory bird populations across a broad geographical scale. This study shows waterbird populations during migration along 14 big and unregulated rivers spanning Europe and Asia, documenting a total of 63,383 birds across 112 species. Analysis revealed significant associations between species richness and bird abundance and geographical proximity, with neighboring rivers displaying greater similarity in species composition and density. The presence of sand banks and islands emerged as critical factors influencing species richness and bird abundance, while forested river segments negatively impacted species richness compared to open landscapes like fields. Unexpectedly, river width showed no significant effect on avian assemblages.

The study was conducted during the autumn migration of birds. Through a combination of ecological assessments and statistical analyses, we identified the primary drivers influencing migratory bird distribution, species richness, and abundance.
DOI: 10.5281/zenodo.15384727
title: Data from: Geographical and habitat factors affecting migratory waterbirds along rivers in the Palearctic

description: Rivers are important habitats for many waterbird species, offering crucial resources such as feeding and resting areas during migration. The migratory patterns of this group of birds are influenced by a variety of factors, including geographical location, habitat availability, and environmental conditions. Despite the huge ecological importance of rivers, there are few studies on how riverine environments support migratory bird populations across a broad geographical scale. This study shows waterbird populations during migration along 14 big and unregulated rivers spanning Europe and Asia, documenting a total of 63,383 birds across 112 species. Analysis revealed significant associations between species richness and bird abundance and geographical proximity, with neighboring rivers displaying greater similarity in species composition and density. The presence of sand banks and islands emerged as critical factors influencing species richness and bird abundance, while forested river segments negatively impacted species richness compared to open landscapes like fields. Unexpectedly, river width showed no significant effect on avian assemblages.

The study was conducted during the autumn migration of birds. Through a combination of ecological assessments and statistical analyses, we identified the primary drivers influencing migratory bird distribution, species richness, and abundance.
DOI: 10.5281/zenodo.15384698
title: The Street View House Numbers (SVHN) Dataset

description: SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting. It can be seen as similar in flavor to MNIST (e.g., the images are of small cropped digits), but incorporates an order of magnitude more labeled data (over 600,000 digit images) and comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images.

Overview



10 classes, 1 for each digit. Digit '1' has label 1, '9' has label 9 and '0' has label 10.

73257 digits for training, 26032 digits for testing, and 531131 additional, somewhat less difficult samples, to use as extra training data

Comes in two formats:

1. Original images with character level bounding boxes.2. MNIST-like 32-by-32 images centered around a single character (many of the images do contain some distractors at the sides).



Format 1 *.tar.gz files

These are the original, variable-resolution, color house-number images with character level bounding boxes, as shown in the examples images above. (The blue bounding boxes here are just for illustration purposes. The bounding box information are stored in digitStruct.mat instead of drawn directly on the images in the dataset.) Each tar.gz file contains the orignal images in png format, together with a digitStruct.mat file, which can be loaded using Matlab. The digitStruct.mat file contains a struct called digitStruct with the same length as the number of original images. Each element in digitStruct has the following fields: name which is a string containing the filename of the corresponding image. bbox which is a struct array that contains the position, size and label of each digit bounding box in the image. Eg: digitStruct(300).bbox(2).height gives height of the 2nd digit bounding box in the 300th image. 

Format 2 *.mat files

Character level ground truth in an MNIST-like format. All digits have been resized to a fixed resolution of 32-by-32 pixels. The original character bounding boxes are extended in the appropriate dimension to become square windows, so that resizing them to 32-by-32 pixels does not introduce aspect ratio distortions. Nevertheless this preprocessing introduces some distracting digits to the sides of the digit of interest. Loading the .mat files creates 2 variables: X which is a 4-D matrix containing the images, and y which is a vector of class labels. To access the images, X(:,:,:,i) gives the i-th 32-by-32 RGB image, with class label y(i).

Reference

Please cite the following reference in papers using this dataset:

Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng Reading Digits in Natural Images with Unsupervised Feature Learning NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011. (PDF)

Please use http://ufldl.stanford.edu/housenumbers as the URL for this site when necessaryFor questions regarding the dataset, please contact streetviewhousenumbers@gmail.com
DOI: 10.5281/zenodo.15384699
title: The Street View House Numbers (SVHN) Dataset

description: SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data preprocessing and formatting. It can be seen as similar in flavor to MNIST (e.g., the images are of small cropped digits), but incorporates an order of magnitude more labeled data (over 600,000 digit images) and comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images.

Overview



10 classes, 1 for each digit. Digit '1' has label 1, '9' has label 9 and '0' has label 10.

73257 digits for training, 26032 digits for testing, and 531131 additional, somewhat less difficult samples, to use as extra training data

Comes in two formats:

1. Original images with character level bounding boxes.2. MNIST-like 32-by-32 images centered around a single character (many of the images do contain some distractors at the sides).



Format 1 *.tar.gz files

These are the original, variable-resolution, color house-number images with character level bounding boxes, as shown in the examples images above. (The blue bounding boxes here are just for illustration purposes. The bounding box information are stored in digitStruct.mat instead of drawn directly on the images in the dataset.) Each tar.gz file contains the orignal images in png format, together with a digitStruct.mat file, which can be loaded using Matlab. The digitStruct.mat file contains a struct called digitStruct with the same length as the number of original images. Each element in digitStruct has the following fields: name which is a string containing the filename of the corresponding image. bbox which is a struct array that contains the position, size and label of each digit bounding box in the image. Eg: digitStruct(300).bbox(2).height gives height of the 2nd digit bounding box in the 300th image. 

Format 2 *.mat files

Character level ground truth in an MNIST-like format. All digits have been resized to a fixed resolution of 32-by-32 pixels. The original character bounding boxes are extended in the appropriate dimension to become square windows, so that resizing them to 32-by-32 pixels does not introduce aspect ratio distortions. Nevertheless this preprocessing introduces some distracting digits to the sides of the digit of interest. Loading the .mat files creates 2 variables: X which is a 4-D matrix containing the images, and y which is a vector of class labels. To access the images, X(:,:,:,i) gives the i-th 32-by-32 RGB image, with class label y(i).

Reference

Please cite the following reference in papers using this dataset:

Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng Reading Digits in Natural Images with Unsupervised Feature Learning NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011. (PDF)

Please use http://ufldl.stanford.edu/housenumbers as the URL for this site when necessaryFor questions regarding the dataset, please contact streetviewhousenumbers@gmail.com
DOI: 10.21233/cxmp-zk48
title: Lake Wanalain water chemistry dataset

description: Raw data for the Lake Wanalain water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/tjav-bb51
title: Shoepack Lake water chemistry dataset

description: Raw data for the Shoepack Lake water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/4fb7-0379
title: Bakchar mire testate amoebae surface sample dataset

description: Raw data for the Bakchar mire testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/a0md-kh70
title: Bayfield River ostracode surface sample dataset

description: Raw data for the Bayfield River ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/qj8j-qs92
title: Bílé Karpaty 12 pollen surface sample dataset

description: Raw data for the Bílé Karpaty 12 pollen surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/w38a-hk51
title: Nakama ostracode surface sample dataset

description: Raw data for the Nakama ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/z03f-7642
title: Round Lake (Va) diatom surface sample dataset

description: Raw data for the Round Lake (Va) diatom surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/r6nj-2w16
title: Racoon Creek water chemistry dataset

description: Raw data for the Racoon Creek water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/n1rx-sm67
title: Roma testate amoebae surface sample dataset

description: Raw data for the Roma testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/mkcy-9d96
title: Cheng et al. 2020 Site 162 pollen surface sample dataset

description: Raw data for the Cheng et al. 2020 Site 162 pollen surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/ghmq-t857
title: Otsu ostracode surface sample dataset

description: Raw data for the Otsu ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/021j-r328
title: Lake Schulze water chemistry dataset

description: Raw data for the Lake Schulze water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/hgzm-dc69
title: Newman Lake water chemistry dataset

description: Raw data for the Newman Lake water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.