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Text-fig. 5. Platanaceae 1–3. Macginitiea nobilis (NEWBERRY) comb. nov. 1. This specimen is labeled as corresponding to Newberry 1898, pl. 50, fig. 1 although it does not match the published drawing exactly. From near Fort Clark, North Dakota, USNM 6964. 2. Lectotype from Newberry (1898, pl. 34), from near Fort Clark, North Dakota; composite picture assembled from images of both counterparts. USNM 1070. 3. Trilobed leaf from Seven Mile Creek, Montana (orig. figured as Platanus nobilis NEWBERRY by Ward 1886, pl. 41, fig. 1). USNM 4093. 4. Platananthus speirsae PIGG et STOCKEY axis with at least 9 attached pedunculate staminate inflorescences (arrows), Seven Mile Creek, Montana (orig. Ward 1885b, pl. 32, fig. 7), USNM 4225. Scale bars 5 cm. in Revisions To Roland Brown'S North American Paleocene Flora
Text-fig. 5. Platanaceae 1–3. Macginitiea nobilis (NEWBERRY) comb. nov. 1. This specimen is labeled as corresponding to Newberry 1898, pl. 50, fig. 1 although it does not match the published drawing exactly. From near Fort Clark, North Dakota, USNM 6964. 2. Lectotype from Newberry (1898, pl. 34), from near Fort Clark, North Dakota; composite picture assembled from images of both counterparts. USNM 1070. 3. Trilobed leaf from Seven Mile Creek, Montana (orig. figured as Platanus nobilis NEWBERRY by Ward 1886, pl. 41, fig. 1). USNM 4093. 4. Platananthus speirsae PIGG et STOCKEY axis with at least 9 attached pedunculate staminate inflorescences (arrows), Seven Mile Creek, Montana (orig. Ward 1885b, pl. 32, fig. 7), USNM 4225. Scale bars 5 cm.
Figures 11–16. Pacuvia lectotypes. 11. Pacuvia castanea Curtis, 1844 dorsal habitus. 12. Pacuvia castanea oblique habitus. 13. Pacuvia castanea labels. 14. Pacuvia gayana Solier, 1851 dorsal habitus. 15. Pacuvia gayana oblique habitus. 16 in Review of the genus Pacuvia Curtis, 1844 (Coleoptera: Scarabaeidae: Melolonthinae: Diplotaxini)
Figures 11–16. Pacuvia lectotypes. 11. Pacuvia castanea Curtis, 1844 dorsal habitus. 12. Pacuvia castanea oblique habitus. 13. Pacuvia castanea labels. 14. Pacuvia gayana Solier, 1851 dorsal habitus. 15. Pacuvia gayana oblique habitus. 16. Pacuvia gayana labels.
Figs 37–40. Andrena oulskii Radoszkowski, 1867, female. 37, specimen labels. 38 in Revision of the Andrena (Hymenoptera: Andrenidae) fauna of Bulgaria and North Macedonia with description of three new species
Figs 37–40. Andrena oulskii Radoszkowski, 1867, female. 37, specimen labels. 38, head, frontal view. 39, habitus, dorsal view. 40, lateral view. © Thomas J. Wood.
RBC-SatImg: Sentinel-2 Imagery and WatData Labels for Water Mapping
<h2>Data Description</h2> <p>This dataset is linked to the publication "Recursive classification of satellite imaging time-series: An application to land cover mapping". In this paper, we introduce the recursive Bayesian classifier (RBC), which converts any instantaneous classifier into a robust online method through a probabilistic framework that is resilient to non-informative image variations. To reproduce the results presented in the paper, the RBC-SatImg folder and the code in the GitHub repository <a href="https://github.com/neu-spiral/RBC-SatImg">RBC-SatImg</a> are required.</p> <p>The <strong>RBC-SatImg</strong> folder contains:</p> <ul> <li>Sentinel-2 time-series imagery from three key regions: Oroville Dam (CA, USA) and Charles River (Boston, MA, USA) for water mapping, and the Amazon Rainforest (Brazil) for deforestation detection.</li> <li>The <strong>RBC-WatData</strong> dataset with manually generated water mapping labels for the Oroville Dam and Charles River regions. This dataset is well-suited for multitemporal land cover and water mapping research, as it accounts for the dynamic evolution of true class labels over time.</li> <li>Pickle files with output to reproduce the results in the paper, including: <ul> <li>Instantaneous classification results for GMM, LR, SIC, WN, DWM</li> <li>Posterior results obtained with the RBC framework</li> </ul> </li> </ul> <p><strong>The Sentinel-2 images and forest labels used in the deforestation detection experiment for the Amazon Rainforest have been obtained from the <a href="https://sites.google.com/view/rainforest-challenge/multiearth-2023">MultiEarth Challenge dataset</a>.</strong></p> <h2>Folder Structure</h2> <p>The following paths can be changed in the configuration file from the GitHub repository as desired. The <strong>RBC-SatImg</strong> is organized as follows:</p> <ul> <li><strong>`./log/`</strong> (EMPTY): Default path for storing log files generated during code execution.</li> <li><strong>`./evaluation_results/`</strong>: Contains the results to reproduce the findings in the paper, including two sub-folders: <ul> <li><strong>`./classification/`</strong>:<strong> </strong> For each test site, four sub-folders are included as: <ul> <li><strong>`./accuracy/`</strong>: Each sub-folder corresponding to an experimental configuration contains pickle files with balanced classification accuracy results and information about the models. The default configuration used in the paper is "conf_00."</li> <li><strong>`./figures/`</strong>: Includes result figures from the manuscript in SVG format.</li> <li><strong>`./likelihoods/`</strong>: Contains pickle files with instantaneous classification results.</li> <li><strong>`./posteriors/`</strong>: Contains pickle files with posterior results generated by the RBC framework.</li> </ul> </li> <li><strong>`./sensitivity_analysis/`</strong>: Contains sensitivity analysis results, organized by different test sites and epsilon values.</li> </ul> </li> <li> <strong>`./Sentinel2_data/`</strong>: Contains Sentinel-2 images used for training and evaluation, organized by scenarios (Oroville Dam, Charles River, Amazon Rainforest). Selected images have been filtered and processed as explained in the manuscript. The Amazon Rainforest images and labels have been obtained from the MultiEarth dataset, and consequently, the labels are included in this folder instead of the RBC-WatData folder.</li> <li><strong>`./RBC-WatData/`</strong>: Contains the water labels that we manually generated with the <a href="https://labelstud.io/">LabelStudio tool</a>.</li> </ul>
Tobii Pro Spectrum hand-labelled data set
<p>For recording this data set, the Tobii Pro Lab software and a Tobii Pro Spectrum eye tracking device with sampling frequencies up to 600 Hz were used. The provided monitor had a size of 23.8 inches and a 16:9 aspect ratio. The exact procedure for generating this data is described in (1).<br>Three recordings at 300 Hz and three recordings at 600 Hz are extracted from the data in (1). From this data, time spans of about 20 seconds are cut out and the fixations and saccades are labelled by hand. <br><br>(1) Timur Ezer, Matthias Greiner, Lisa Grabinger, Florian Hauser, and Jürgen Mottok. Eye tracking as technology in education: Data quality analysis and improvements. In ICERI2023 Proceedings, 16th annual International Conference of Education, Research and Innovation, pages 4500–4509, Valencia, Spain, 13-15 November, 2023 2023. IATED. ISBN 978-84-09-55942-8. doi: 10.21125/iceri.2023.1127. URL https://doi.org/10.21125/iceri.2023.1127 </p>
CHEN-AND: A Labeled Dataset for Chinese and English Joint Author Name Disambiguation
<p>Abstract: Author name disambiguation (AND) is an important problem in literature databases and is even more prominent in the cross-language (database) context. Extensive research has been conducted in the academic community to eliminate such ambiguity. However, the existing research mainly focuses on monolingual literature, with less attention paid to author disambiguation in cross-language environments. In this regard, this study focuses on a typical cross-language author disambiguation task - Chinese and English joint author disambiguation. We first propose an automated dataset construction method for Chinese-English literature joint AND using online open resources, with this method, we create a dataset named CHEN-AND for joint Chinese and English author disambiguation. Then we propose a merging-first-then-disambiguation (MFTD)--based disambiguation framework and evaluate several variants of this method on the test dataset. For details on building this dataset, please refer to <a href="https://github.com/carmanzhang/CHEN-AND">this repository</a> on my GitHub page.</p>
Linked collectors and determiners for: The importance of correct labelling of types: an example in Tortricidae (Lepidoptera) and its rectification.
Natural history specimen data linked to collectors and determiners held within, "The importance of correct labelling of types: an example in Tortricidae (Lepidoptera) and its rectification". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/e143db18-f7fe-4f11-9457-edbf383c0e5f">https://bionomia.net/dataset/e143db18-f7fe-4f11-9457-edbf383c0e5f</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/e143db18-f7fe-4f11-9457-edbf383c0e5f">https://gbif.org/dataset/e143db18-f7fe-4f11-9457-edbf383c0e5f</a>. Formatted as a Frictionless Data package.
FIG. 6 in High-quality herbarium-label transcription by citizen scientists improves taxonomic and spatial representation of the tropical plant family Annonaceae
FIG. 6. — Temporal distribution of Annonaceae specimens collected in the Herbonautes dataset. The Histogram and left hand axis represent specimens collected per 5-year intervals. The right-hand axis and continuous line represent the cumulative specimens collected in total over the entire time period. The earliest Annonaceae collected and transcribed within the dataset is from 1740, a specimen of Annona squamosa L. collected in China by Pierre Nicolas le Chéron d'Incarville. The newest transcribed specimens are from 2015.
NPC-Labeled SMILES datasets
<p>NPC-Labels annotations for commonly used datasets. At this time, it includes all datasets from GNPS. More will be added in the future.</p>
Webinar: Potential and challenges of certification and development of new labels
<p>The webinar will address the vital topic of new certifications and labels for the renewable, and especially bio-based, economy.</p> <p>Certification and labelling play a crucial role in empowering consumers to make informed and sustainable purchasing decisions by impeding greenwashing and instead providing credible and reliable sustainability information. Furthermore, they facilitate the tracking and traceability of biological and other renewable feedstock throughout value chains, fostering transparency and accountability among stakeholders of the industry. However, there are a lot of challenges for current certification and labelling schemes, since they are often not sufficiently laid-out for bio-based and other renewable products and their value-chains.</p> <p>In this webinar, REDcert and TÜV AUSTRIA will show their sytems for sustainability certification and will present new or soon to be published label and certification schemes (LCS) that are relevant for the bio-based economy. The speakers will talk about challenges on the way to the final label, and which gaps will be closed.</p> <p>Join us on November the 5th and listen to the two presentations by REDcert and TÜV AUSTRIA. Together, we will explore their innovative new LCS that will support shaping a strong circular EU (bio)economy.</p> <p>Speakers:</p> <p>Phillipe Dewolfs (TÜV AUSTRIA): From standardisation to communication – the role of a certification body<br>Simon Schwarzwald (REDcert): Certification of sustainable products in the REDcert² scheme</p> <p> </p>
StreaksYoloDataset: labeled raw astronomical images for streaks detection
<p><strong>StreaksYoloDataset</strong>, is a set of raw astronomical images captured with smart telescopes and annotated with the positions of streaks that are effectively in the images. Images were captured between March 2022 and February 2023 from Luxembourg Greater Region by using the built-in alignment and stacking features of a <u>Stellina</u> smart telescope, based on an Extra Low Dispersion doublet with an aperture of 80 mm and a focal length of 400 mm (focal ratio of f/5), and equipped with a Sony <u>IMX178</u> CMOS sensor with a resolution of 6.4 million pixels.</p> <p><strong>StreaksYoloDataset</strong> can be used to train YOLO detection models on raw astronomical images, enabling the detection of satellites / debris / cosmic rays with astronomical equipment accessible to the public.<strong> </strong></p> <p><strong>StreaksYoloDataset</strong> is structured with the YOLO standard, i.e., with separated files for images and annotations, usable by state-of-the-art training tools and graphical software like MakeSense (https://www.makesense.ai/). More precisely, there is a ZIP file containing images in JPEG format (minimal compression), and text files containing the positions of streaks. Each image has a resolution of 640 × 640 pixels.</p> <p><strong>StreaksYoloDataset </strong>may be used to train models for the Space domain Awareness (SDA).</p> <p>For more details about the dataset, please contact the author: olivier.parisot@list.lu .</p> <p>For more information about Luxembourg of Science and Technology (LIST), please consult: <a href="https://www.list.lu/">https://www.list.lu</a> .</p>
FIG. 5 in High-quality herbarium-label transcription by citizen scientists improves taxonomic and spatial representation of the tropical plant family Annonaceae
FIG. 5. — Spatial distribution of species richness in datasets for Madagascar at 0.5 × 0.5° grid resolution: A, curated expert dataset; B, herbonautes transcribed data; C, GBIF data. Equirectangular (EPSG 4326) projection.
FIG. 4 in High-quality herbarium-label transcription by citizen scientists improves taxonomic and spatial representation of the tropical plant family Annonaceae
FIG. 4. — Global spatial coverage of datasets, showing regions covered by both Herbonautes (P) and GBIF data, just by GBIF and just by Herbonautes. Grid resolution 1 × 1° (c. 110 × 110 km at the equator), equirectangular (EPSG 4326) projection.
Allen Mouse CCF v3.1 ASR - Labels Modulo 65000
<p>If you use this dataset, please cite the original Allen Mouse CCF paper (<a href="https://doi.org/10.1016/j.cell.2020.04.007">https://doi.org/10.1016/j.cell.2020.04.007</a>):</p> <blockquote> <p>Quanxin Wang, Song-Lin Ding, Yang Li, Josh Royall, David Feng, Phil Lesnar, Nile Graddis, Maitham Naeemi, Benjamin Facer, Anh Ho, Tim Dolbeare, Brandon Blanchard, Nick Dee, Wayne Wakeman, Karla E. Hirokawa, Aaron Szafer, Susan M. Sunkin, Seung Wook Oh, Amy Bernard, John W. Phillips, Michael Hawrylycz, Christof Koch, Hongkui Zeng, Julie A. Harris, Lydia Ng,<br>The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas,<br>Cell,<br>Volume 181, Issue 4,<br>2020</p> </blockquote> <p> </p> <p>More information is available in</p> <p><a href="https://community.brain-map.org/t/allen-mouse-ccf-accessing-and-using-related-data-and-tools/359">https://community.brain-map.org/t/allen-mouse-ccf-accessing-and-using-related-data-and-tools/359</a></p> <p>This dataset is a 3D mutichannel image which is derived from the original data introduced by the Allen Mouse Common Coordinate Framework v3 (<a href="http://help.brain-map.org/download/attachments/2818171/MouseCCF.pdf">http://help.brain-map.org/download/attachments/2818171/MouseCCF.pdf</a>).</p> <p>The image file format is a xml/hdf5 file used by ImageJ/Fiji's bigdataviewer (<a href="https://docs.openmicroscopy.org/bio-formats/6.1.0/formats/big-data-viewer.html">https://docs.openmicroscopy.org/bio-formats/6.1.0/formats/big-data-viewer.html</a>).</p> <p>All channels are isotropically sampled with a voxel size of 10 micrometers.</p> <p>Channels:</p> <ul> <li>0: NISSL (<a href="http://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/ara_nissl/ara_nissl_10.nrrd">http://download.alleninstitute.org/informatics-archive/current-release/mouse_ccf/ara_nissl/ara_nissl_10.nrrd</a>)</li> <li>1: LABELS Border - Delimits the borders of the labels</li> <li>2: ARA</li> <li>3: LABELS Mod 65000 : Labels modulo 65000.</li> </ul> <p>The labels modulo 65000 channel is used because the xml/hdf5 file format handles only 16-bits images, while the reference atlas has (sparse) values above 65535. There is no overlap of labels after computing the label value mod 65000. Thus no loss of information in the mod labels channels.</p> <p>The related ontology from the allen brain (file 1.json) is also stored here.</p> <p>The only modification between this version and the previous one is to use an ASR convention for the positioning in 3D. It will match exactly the BrainGlobe allen_mouse atlases.</p> <p>The xml file which prevents the appearance of a fake extra timepoint in the dataset.</p>
Original data sets of a DEER/PELDOR ring test of four doubly spin-labelled mutants of the protein YopO
<p>The dataset is discussed in manuscript "Benchmark test and guidelines for DEER/PELDOR experiments on nitroxide-labeled biomolecules" by Olav Schiemann, Caspar A. Heubach, Dinar Abdullin, Katrin Ackermann, Mykhailo Azarkh, Elena Bagryanskaya, Malte Drescher, Burkhard Endeward, Jack H. Freed, Laura Galazzo, Daniella Goldfarb, Tobias Hett, Laura Esteban Hofer, Luis Fábregas Ibáñez, Eric J. Hustedt, Svetlana Kucher, Ilya Kuprov, Janet E. Lovett, Andreas Meyer, Sharon Ruthstein, Sunil Saxena, Stefan Stoll, Christiane Timmel, Marilena Di Valentin, Hassane S. Mchaourab, Thomas F. Prisner, Bela E. Bode, Enrica Bordignon, Marina Bennati, Gunnar Jeschke. It was generated in a ring test by seven laboratories. The authors names of individual data are not assigned on purpose, rather data sets are referred to by laboratory identifiers A, B, C, D, E, F, G. The Supplementary Information for the above mentioned manuscript, which contains a link to this dataset, describes how the samples were prepared and how the measurements were performed.</p>
Figures 1-4. Xanthocomus spp. lectotype labels and dorsal habitus. 1,3 in Review of the species described in Leptostilbus Casey in North America (Coleoptera: Phalacridae: Xanthocomus Guillebeau)
Figures 1-4. Xanthocomus spp. lectotype labels and dorsal habitus. 1,3) X. rutilans (Casey). 2,4) X. concinnus (Casey).
Figure 1-4. Speyeria atlantis greyi holotype and allotype with associated labels. 1-2 in Nomenclatural faux pas for Speyeria atlantis greyi Moeck, 1950 (Lepidoptera: Nymphalidae)
Figure 1-4. Speyeria atlantis greyi holotype and allotype with associated labels. 1-2) Holotype, dorsal and ventral view. 3-4) Allotype, dorsal and ventral view. Both specimens American Museum of Natural History, New York. Images by James C. Dunford.
Figs 1–7. Dorsal aspects and labels. 1 in Commented catalogue of Cassidinae (Coleoptera: Chrysomelidae) of the state of São Paulo, Brazil, with remarks on the collection of Jaro Mráz in the National Museum in Prague
Figs 1–7. Dorsal aspects and labels. 1 – Charidotella (s.str.) recidiva (Spaeth, 1926), syntype; 2 – Charidotis mrazi Spaeth, 1936, syntype; 3 – Charidotis aerosa Spaeth, 1936, syntype; 4 – Microctenochira triplagiata (Spaeth, 1926), holotype; 5 – Coptocycla (Podostraba) ruficornis Spaeth, 1936, syntype; 6–7 – typical locality labels for Mráz's material. Scale bars unless stated = 1 mm.
Fig. 8. Hortipes genitalia, labeled schematics. a. H in Hortipes, A Huge Genus Of Tiny Afrotropical Spiders (Araneae, Liocranidae)
Fig. 8. Hortipes genitalia, labeled schematics. a. H. falcatus, new species, ventral view of vulva, left half. b. H. silvarum Ledoux and Emerit, ventral view of vulva, right half. c. H. falcatus, new species, male palp, ventral view. d. H. machaeropolion, new species, male palp, retrolateral view.
Text-fig. 1. Location map and original field label of T 68, fragment of arsinoithere upper molar from Oued Grigema, Tunisia. in Arsinoitherium (Embrithopoda) And Other Large Mammals And Plants From The Oligocene Of Tunisia
Text-fig. 1. Location map and original field label of T 68, fragment of arsinoithere upper molar from Oued Grigema, Tunisia.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.