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The ACROBAT data set consists of 4,212 whole slide images (WSIs) from 1,153 female primary breast cancer patients. The WSIs in the data set are available at 10X magnification and show tissue sections from breast cancer resection specimens stained with hematoxylin and eosin (H&E) or immunohistochemistry (IHC). For each patient, one WSI of H&E stained tissue and at least one one, and up to four, WSIs of corresponding tissue stained with the routine diagnostic stains ER, PGR, HER2 and KI67 are available. The data set was acquired as part of the CHIME study (chimestudy.se) and its primary purpose was to facilitate the ACROBAT WSI registration challenge (acrobat.grand-challenge.org). The histopathology slides originate from routine diagnostic pathology workflows and were digitised for research purposes at Karolinska Institutet (Stockholm, Sweden). The image acquisition process resembles the routine digital pathology image digitisation workflow, using three different Hamamatsu WSI scanners, specifically one NanoZoomer S360 and two NanoZoomer XR. The WSIs in this data set are accompanied by a data table with one row for each WSI, specifying an anonymised patient ID, the stain or IHC antibody type of each WSI, as well as the magnification and microns per pixel at each available resolution level. Automated registration algorithm performance evaluation is possible through the ACROBAT challenge website based on over 37,000 landmark pair annotations from 13 annotators. While the primary purpose of this data set was the development and evaluation of WSI registration methods, this data set has the potential to facilitate further research in the context of computational pathology, for example in the areas of stain-guided learning, virtual staining, unsupervised learning and stain-independent models.
The data set consists of three subsets, the training, validation and test set, based on the ACROBAT WSI registration challenge. There are 750 cases in the training set, for each of which one H&E WSI and one to four IHC WSIs are available, with 3406 WSIs in total. The validation set consists of 100 cases with 200 WSIs in total and the test set of 303 cases with 606 WSIs in total. Both for the validation and test set, one H&E WSI as well as one randomly selected IHC WSI is available.
WSIs were anonymised by deleting the associated macro images, by generating filenames with random case IDs and by overwriting meta data fields with potentially personal information. Hamamatsu NDPI files were then converted using libvips (libvips.org/). WSIs are available as generic tiled TIFF WSIs (openslide.org/formats/generic-tiff/) at 10X magnification and lower image levels.
The data set is available for download in seven separate ZIP archives, five for the training data (train_part1.zip (71.47 GB), train_part2.zip (70.59 GB), train_part3.zip (75.91 GB), train_part4.zip (71.63 GB) and train_part5.zip (69.09 GB)), one for the validation data (valid.zip 21.79 GB) and one for the test data (test.zip 68.11 GB).
File listings and checksums in SHA1 format are available for checking archive/data integrity when downloading.
While it would be helpful to notify SND of any publications using this data set by sending an email to request@snd.gu.se, please note that this is not required to use the data.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
000 pixels width and 200
Financial overview and grant giving statistics of Friends of Slide Rock State Park Inc.
The dataset consists of 99 H&E-stained whole slide skin images (WSI) - 49 abnormal and 50 normal cases. All significant abnormal findings identified are outlined and categorized into 13 types such as actinic keratosis, basal cell carcinoma and dermatofibroma. Other tissue components, such as epidermis, adnexal structures, as well as the surgical margin are delineated to create a complete histological map. In total, 16741 separate annotations have been made to segment the different tissue structures and link them to ontological information.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Experience the attraction 'Giant Slide' anew! Perfectly plan with statistics from October 2022 on waiting times or queue times and weather data.
Financial overview and grant giving statistics of Slide Youth Baseball Foundation
Attribution-NoDerivs 3.0 (CC BY-ND 3.0)https://creativecommons.org/licenses/by-nd/3.0/
License information was derived automatically
Statistics illustrates consumption, production, prices, and trade of Projectors, image; slide (excluding cinematographic) in China from 2007 to 2024.
This dataset collects the slides that were presented at the Data Collaborations Across Boundaries session in SciDataCon 2022, part of the International Data Week.
The following session proposal was prepared by Tyng-Ruey Chuang and submitted to SciDataCon 2022 organizers for consideration on 2022-02-28. The proposal was accepted on 2022-03-28. Six abstracts were submitted and accepted to this session. Five presentations were delivered online in a virtual session on 2022-06-21.
Data Collaborations Across Boundaries
There are many good stories about data collaborations across boundaries. We need more. We also need to share the lessons each of us has learned from collaborating with parties and communities not in our familiar circles.
By boundaries, we mean not just the regulatory borders in between the nation states about data sharing but the various barriers, readily conceivable or not, that hinder collaboration in aggregating, sharing, and reusing data for social good. These barriers to collaboration exist between the academic disciplines, between the economic players, and between the many user communities, just to name a few. There are also cross-domain barriers, for example those that lay among data practitioners, public administrators, and policy makers when they are articulating the why, what, and how of "open data" and debating its economic significance and fair distribution. This session aims to bring together experiences and thoughts on good data practices in facilitating collaborations across boundaries and domains.
The success of Wikipedia proves that collaborative content production and service, by ways of copyleft licenses, can be sustainable when coordinated by a non-profit and funded by the general public. Collaborative code repositories like GitHub and GitLab demonstrate the enormous value and mass scale of systems-facilitated integration of user contributions that run across multiple programming languages and developer communities. Research data aggregators and repositories such as GBIF, GISAID, and Zenodo have served numerous researchers across academic disciplines. Citizen science projects and platforms, for instance eBird, Galaxy Zoo, and Taiwan Roadkill Observation Network (TaiRON), not only collect data from diverse communities but also manage and release datasets for research use and public benefit (e.g. TaiRON datasets being used to improve road design and reduce animal mortality). At the same time large scale data collaborations depend on standards, protocols, and tools for building registries (e.g. Archival Resource Key), ontologies (e.g. Wikidata and schema.org), repositories (e.g. CKAN and Omeka), and computing services (e.g. Jupyter Notebook). There are many types of data collaborations. The above lists only a few.
This session proposal calls for contributions to bring forward lessons learned from collaborative data projects and platforms, especially about those that involve multiple communities and/or across organizational boundaries. Presentations focusing on the following (non-exclusive) topics are sought after:
Support mechanisms and governance structures for data collaborations across organizations/communities.
Data policies --- such as data sharing agreements, memorandum of understanding, terms of use, privacy policies, etc. --- for facilitating collaborations across organizations/communities.
Traditional and non-traditional funding sources for data collaborations across multiple parties; sustainability of data collaboration projects, platforms, and communities.
Data workflows --- collection, processing, aggregation, archiving, and publishing, etc. --- designed with considerations of (external) collaboration.
Collaborative web platforms for data acquisition, curation, analysis, visualization, and education.
Examples and insights from data trusts, data coops, as well as other formal and informal forms of data stewardship.
Debates on the pros and cons of centralized, distributed, and/or federated data services.
Practical lessons learned from data collaboration stories: failure, success, incidence, unexpected turn of event, aftermath, etc. (no story is too small!).
Attribution-NoDerivs 3.0 (CC BY-ND 3.0)https://creativecommons.org/licenses/by-nd/3.0/
License information was derived automatically
Statistics illustrates consumption, production, prices, and trade of Projectors, image; slide (excluding cinematographic) in Isle of Man from 2007 to 2024.
Attribution-NoDerivs 3.0 (CC BY-ND 3.0)https://creativecommons.org/licenses/by-nd/3.0/
License information was derived automatically
Statistics illustrates consumption, production, prices, and trade of Slide fasteners and parts thereof in the Czech Republic from 2007 to 2024.
Attribution-NoDerivs 3.0 (CC BY-ND 3.0)https://creativecommons.org/licenses/by-nd/3.0/
License information was derived automatically
Statistics illustrates consumption, production, prices, and trade of Slide fasteners and parts thereof in Austria from Jan 2019 to Jul 2025.
Attribution-NoDerivs 3.0 (CC BY-ND 3.0)https://creativecommons.org/licenses/by-nd/3.0/
License information was derived automatically
Statistics illustrates consumption, production, prices, and trade of Slide fasteners and parts thereof in Georgia from 2007 to 2024.
Attribution-NoDerivs 3.0 (CC BY-ND 3.0)https://creativecommons.org/licenses/by-nd/3.0/
License information was derived automatically
Statistics illustrates consumption, production, prices, and trade of Slide fasteners and parts thereof in Eastern Europe from 2007 to 2024.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Experience the attraction 'Jett Slide' anew! Perfectly plan with statistics from April 2025 on waiting times or queue times and weather data.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
United States Exports of slide fasteners and to New Caledonia was US$7.78 Thousand during 2020, according to the United Nations COMTRADE database on international trade. United States Exports of slide fasteners and to New Caledonia - data, historical chart and statistics - was last updated on September of 2025.
Attribution-NoDerivs 3.0 (CC BY-ND 3.0)https://creativecommons.org/licenses/by-nd/3.0/
License information was derived automatically
Statistics illustrates consumption, production, prices, and trade of Slide fasteners and parts thereof in Cote d'Ivoire from 2007 to 2024.
https://www.reportsanddata.com/privacy-policyhttps://www.reportsanddata.com/privacy-policy
Deep-dive into Slide Tray Box Market size, drivers, and share trends with insights mapped to 2034.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Italy Exports of slide fasteners and to Kazakhstan was US$9.26 Thousand during 2023, according to the United Nations COMTRADE database on international trade. Italy Exports of slide fasteners and to Kazakhstan - data, historical chart and statistics - was last updated on July of 2025.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Finland Exports of slide fasteners and to Georgia was US$33 during 2022, according to the United Nations COMTRADE database on international trade. Finland Exports of slide fasteners and to Georgia - data, historical chart and statistics - was last updated on September of 2025.
The following slide sets are available to download for presentational use:
New HIV diagnoses, AIDS and deaths are collected from HIV outpatient clinics, laboratories and other healthcare settings. Data relating to people living with HIV is collected from HIV outpatient clinics. Data relates to England, Wales, Northern Ireland and Scotland, unless stated.
HIV testing, pre-exposure prophylaxis, and post-exposure prophylaxis data relates to activity at sexual health services in England only.
View the pre-release access lists for these statistics.
Previous reports, data tables and slide sets are also available for:
Our statistical practice is regulated by the Office for Statistics Regulation (OSR). The OSR sets the standards of trustworthiness, quality and value in the https://code.statisticsauthority.gov.uk/">Code of Practice for Statistics that all producers of Official Statistics should adhere to.
Additional information on HIV surveillance can be found in the HIV Action Plan for England monitoring and evaluation framework reports. Other HIV in the UK reports published by Public Health England (PHE) are available online.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The ACROBAT data set consists of 4,212 whole slide images (WSIs) from 1,153 female primary breast cancer patients. The WSIs in the data set are available at 10X magnification and show tissue sections from breast cancer resection specimens stained with hematoxylin and eosin (H&E) or immunohistochemistry (IHC). For each patient, one WSI of H&E stained tissue and at least one one, and up to four, WSIs of corresponding tissue stained with the routine diagnostic stains ER, PGR, HER2 and KI67 are available. The data set was acquired as part of the CHIME study (chimestudy.se) and its primary purpose was to facilitate the ACROBAT WSI registration challenge (acrobat.grand-challenge.org). The histopathology slides originate from routine diagnostic pathology workflows and were digitised for research purposes at Karolinska Institutet (Stockholm, Sweden). The image acquisition process resembles the routine digital pathology image digitisation workflow, using three different Hamamatsu WSI scanners, specifically one NanoZoomer S360 and two NanoZoomer XR. The WSIs in this data set are accompanied by a data table with one row for each WSI, specifying an anonymised patient ID, the stain or IHC antibody type of each WSI, as well as the magnification and microns per pixel at each available resolution level. Automated registration algorithm performance evaluation is possible through the ACROBAT challenge website based on over 37,000 landmark pair annotations from 13 annotators. While the primary purpose of this data set was the development and evaluation of WSI registration methods, this data set has the potential to facilitate further research in the context of computational pathology, for example in the areas of stain-guided learning, virtual staining, unsupervised learning and stain-independent models.
The data set consists of three subsets, the training, validation and test set, based on the ACROBAT WSI registration challenge. There are 750 cases in the training set, for each of which one H&E WSI and one to four IHC WSIs are available, with 3406 WSIs in total. The validation set consists of 100 cases with 200 WSIs in total and the test set of 303 cases with 606 WSIs in total. Both for the validation and test set, one H&E WSI as well as one randomly selected IHC WSI is available.
WSIs were anonymised by deleting the associated macro images, by generating filenames with random case IDs and by overwriting meta data fields with potentially personal information. Hamamatsu NDPI files were then converted using libvips (libvips.org/). WSIs are available as generic tiled TIFF WSIs (openslide.org/formats/generic-tiff/) at 10X magnification and lower image levels.
The data set is available for download in seven separate ZIP archives, five for the training data (train_part1.zip (71.47 GB), train_part2.zip (70.59 GB), train_part3.zip (75.91 GB), train_part4.zip (71.63 GB) and train_part5.zip (69.09 GB)), one for the validation data (valid.zip 21.79 GB) and one for the test data (test.zip 68.11 GB).
File listings and checksums in SHA1 format are available for checking archive/data integrity when downloading.
While it would be helpful to notify SND of any publications using this data set by sending an email to request@snd.gu.se, please note that this is not required to use the data.