cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0786603.10fu
title: SH0786603.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0786604.10fu
title: SH0786604.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0786605.10fu
title: SH0786605.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0786606.10fu
title: SH0786606.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5281/zenodo.7591899
title: Stordalen Mire July 2016 Metatranscriptome Data from McGivern et al.

description: Tabs MetaT = information for the 27 metatranscriptomes metaT_genes = gene level mapping data metaT_MAGs = MAG level mapping data FUNDING:<br> This research is a contribution of the EMERGE Biology Integration Institute ((https://emerge-bii.github.io/), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br> We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br> This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632. DE-SC0010580. and DE-SC0016440.<br> A portion of this research was performed under the Facilities Integrating Collaborations for User Science (FICUS) program (proposal: 10.46936/fics.proj.2017.49950/60006215 and 10.46936/10.25585/60001148) and used resources at the DOE Joint Genome Institute (https://ror.org/04xm1d337) and the Environmental Molecular Sciences Laboratory (https://ror.org/04rc0xn13), which are DOE Office of Science User Facilities operated under Contract Nos. DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL).
DOI: 10.5281/zenodo.7591900
title: Stordalen Mire July 2016 Metatranscriptome Data from McGivern et al.

description: Tabs MetaT = information for the 27 metatranscriptomes metaT_genes = gene level mapping data metaT_MAGs = MAG level mapping data FUNDING:<br> This research is a contribution of the EMERGE Biology Integration Institute ((https://emerge-bii.github.io/), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br> We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br> This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632. DE-SC0010580. and DE-SC0016440.<br> A portion of this research was performed under the Facilities Integrating Collaborations for User Science (FICUS) program (proposal: 10.46936/fics.proj.2017.49950/60006215 and 10.46936/10.25585/60001148) and used resources at the DOE Joint Genome Institute (https://ror.org/04xm1d337) and the Environmental Molecular Sciences Laboratory (https://ror.org/04rc0xn13), which are DOE Office of Science User Facilities operated under Contract Nos. DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL).
DOI: 10.5281/zenodo.7587534
title: Metagenome-Assembled Genome DRAM Annotations (EMERGE 97% dereplicated MAGs)

description: This is the combined DRAM annotation outputs for the 1,864 97% dereplicated metagenome-assembled genomes from Stordalen Mire, Sweden. 1864_97percentmags_annotations_combined.tsv.gz 1864_97percentmags_metabolism_summary.xlsx product_0.html product_1.html METHODS: MAGs were annotated and distilled using DRAM (v1.4.0). FUNDING:<br> This research is a contribution of the EMERGE Biology Integration Institute ((https://emerge-bii.github.io/), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br> We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br> This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632. DE-SC0010580. and DE-SC0016440.<br> A portion of this research was performed under the Facilities Integrating Collaborations for User Science (FICUS) program (proposal: 10.46936/fics.proj.2017.49950/60006215 and 10.46936/10.25585/60001148) and used resources at the DOE Joint Genome Institute (https://ror.org/04xm1d337) and the Environmental Molecular Sciences Laboratory (https://ror.org/04rc0xn13), which are DOE Office of Science User Facilities operated under Contract Nos. DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL).
DOI: 10.5281/zenodo.7587533
title: Metagenome-Assembled Genome DRAM Annotations (EMERGE 97% dereplicated MAGs)

description: This is the combined DRAM annotation outputs for the 1,864 97% dereplicated metagenome-assembled genomes from Stordalen Mire, Sweden. 1864_97percentmags_annotations_combined.tsv.gz 1864_97percentmags_metabolism_summary.xlsx product_0.html product_1.html METHODS: MAGs were annotated and distilled using DRAM (v1.4.0). FUNDING:<br> This research is a contribution of the EMERGE Biology Integration Institute ((https://emerge-bii.github.io/), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070.<br> We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164.<br> This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632. DE-SC0010580. and DE-SC0016440.<br> A portion of this research was performed under the Facilities Integrating Collaborations for User Science (FICUS) program (proposal: 10.46936/fics.proj.2017.49950/60006215 and 10.46936/10.25585/60001148) and used resources at the DOE Joint Genome Institute (https://ror.org/04xm1d337) and the Environmental Molecular Sciences Laboratory (https://ror.org/04rc0xn13), which are DOE Office of Science User Facilities operated under Contract Nos. DE-AC02-05CH11231 (JGI) and DE-AC05-76RL01830 (EMSL).
DOI: 10.21227/e47t-p857
title: Raw ADC data from FMCW radar at 77 GHz with interference

description: In the attached dataset, a continuous record of 2493 chirp-sequence FMCW radar burst is presented. The data consist of the direct ADC output of an AWR1443BOOST radar board from Texas Instruments during 47.37 s of continuous operation. During this time, two FMCW radars of the model AWR1642BOOST were continuously transmitting sweeps with its main lobe aimed towards the first radar. This produces the apparition of two different uncorrelated interferences at the victim radar side, which can be seen on different indexes of the provided capture. The scenario of the measurements was mostly static, with a small drone of the model DJI Mini 2 flying back and forth in the field of view of the victim radar.The raw data can be used to test interference mitigation techniques and the detection of targets in an interfered setup.
DOI: 10.5281/zenodo.8102080
title: endocladia_muricata_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.5281/zenodo.8102079
title: endocladia_muricata_decontaminated_FSCR.fasta.gz

description: .
DOI: 10.6084/m9.figshare.22691443.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691431.v2
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691431.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.5519/qd.uo7muxts
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (76 records)
DOI: 10.6084/m9.figshare.22691428.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691425.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691422.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691419.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691416.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691413.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691410.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691407.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691404.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691401.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.
DOI: 10.6084/m9.figshare.22691398.v1
title: Untitled Item

description: The MIDOG++ dataset represents an extension of the data set used in the MIDOG 2021 and 2022 challenges. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The human and canine samples were processed and stained at different human and veterinary pathology laboratories with standard H&E dye and digitized with different digital whole slide image scanners. We provide labels for 11,937 mitotic figures that have been differentiated against 14,351 imposter cells in a blinded consensus by two pathologists and a final decision by a third pathologist for disagreed labels.