cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3712669
title: Dataset related to article "Computer-aided assessment of the extra-cellular matrix during pancreatic carcinogenesis: a pilot study."

description: BACKGROUND: A hallmark of pancreatic ductal adenocarcinoma is the desmoplastic reaction, but its impact on the tumor behavior remains controversial. Our aim was to introduce a computer -aided method to precisely quantify the amount of pancreatic collagenic extra-cellular matrix, its spatial distribution pattern, and the degradation process. METHODS: A series of normal, inflammatory and neoplastic pancreatic ductal adenocarcinoma formalin-fixed and paraffin-embedded Sirius red stained sections were automatically digitized and analyzed using a computer-aided method. RESULTS: We found a progressive increase of pancreatic collagenic extra-cellular matrix from normal to the inflammatory and pancreatic ductal adenocarcinoma. The two-dimensional fractal dimension showed a significant difference in the collagenic extra-cellular matrix spatial complexity between normal versus inflammatory and pancreatic ductal adenocarcinoma. A significant difference when comparing the number of cycles necessary to degrade the pancreatic collagenic extra-cellular matrix in normal versus inflammatory and pancreatic ductal adenocarcinoma was also found. The difference between inflammatory and pancreatic ductal adenocarcinoma was also significant. Furthermore, the mean velocity of collagenic extra-cellular matrix degradation was found to be faster in inflammatory and pancreatic ductal adenocarcinoma than in normal. CONCLUSION: These findings demonstrate that inflammatory and pancreatic ductal adenocarcinomas are characterized by an increased amount of pancreatic collagenic extra-cellular matrix and by changes in their spatial complexity and degradation. Our study defines new features about the pancreatic collagenic extra-cellular matrix, and represents a basis for further investigations into the clinical behavior of pancreatic ductal adenocarcinoma and the development of therapeutic strategies.
DOI: 10.5281/zenodo.3712668
title: Dataset related to article "Computer-aided assessment of the extra-cellular matrix during pancreatic carcinogenesis: a pilot study."

description: BACKGROUND: A hallmark of pancreatic ductal adenocarcinoma is the desmoplastic reaction, but its impact on the tumor behavior remains controversial. Our aim was to introduce a computer -aided method to precisely quantify the amount of pancreatic collagenic extra-cellular matrix, its spatial distribution pattern, and the degradation process. METHODS: A series of normal, inflammatory and neoplastic pancreatic ductal adenocarcinoma formalin-fixed and paraffin-embedded Sirius red stained sections were automatically digitized and analyzed using a computer-aided method. RESULTS: We found a progressive increase of pancreatic collagenic extra-cellular matrix from normal to the inflammatory and pancreatic ductal adenocarcinoma. The two-dimensional fractal dimension showed a significant difference in the collagenic extra-cellular matrix spatial complexity between normal versus inflammatory and pancreatic ductal adenocarcinoma. A significant difference when comparing the number of cycles necessary to degrade the pancreatic collagenic extra-cellular matrix in normal versus inflammatory and pancreatic ductal adenocarcinoma was also found. The difference between inflammatory and pancreatic ductal adenocarcinoma was also significant. Furthermore, the mean velocity of collagenic extra-cellular matrix degradation was found to be faster in inflammatory and pancreatic ductal adenocarcinoma than in normal. CONCLUSION: These findings demonstrate that inflammatory and pancreatic ductal adenocarcinomas are characterized by an increased amount of pancreatic collagenic extra-cellular matrix and by changes in their spatial complexity and degradation. Our study defines new features about the pancreatic collagenic extra-cellular matrix, and represents a basis for further investigations into the clinical behavior of pancreatic ductal adenocarcinoma and the development of therapeutic strategies.
DOI: 10.21227/vfzc-nd90
title: Corona Virus (COVID-19) Tweets Dataset

description: Tweets Counter: 14.8 Million plus (collecting since Feb 1, 2020)Update: I have been receiving a lot of requests asking to add the user's ID & location for each collected tweets. I've made necessary changes to the model to include these two details as well. Tweets tweeted after March 17, 2020 12:20 GMT+5:45 will have these extra details.The first column has date and time in the Unix timestamp. You can easily make it readable using pandas: df['date'] = pd.to_datetime[df['unix'], unit='ms'].Each database (*.db) contain five columns.First column: date and time of the tweetSecond column: user's ID (tweets collected after March 17, 2020 12:20 GMT+5:45 )Third column: user's location (tweets collected after March 17, 2020 12:20 GMT+5:45 )Fourth column: tweetFifth column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live.To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [2] has been appended as the last column. New databases will be added to this dataset every week. Bookmark this page for further updates. [1] https://sentiment.live/ [2] https://textblob.readthedocs.io/en/dev/
DOI: 10.21227/xa08-wh34
title: Corona Virus (COVID-19) Tweets Dataset

description: Tweets Counter: 12.9 Million plus (collecting since Feb 1, 2020)Update: I have been receiving a lot of requests asking to add the user's ID & location for each collected tweets. I've made necessary changes to the model to include these two details as well. Tweets tweeted after March 17, 2020 12:20 GMT 5:45 will have these extra details.The first column has date and time in the Unix timestamp. You can easily make it readable using pandas: df['date'] = pd.to_datetime[df['unix'], unit='ms'].Each database (*.db) contain five columns.First column: date and time of the tweetSecond column: user's ID (tweets collected after March 17, 2020 12:20 GMT 5:45 )Third column: user's location (tweets collected after March 17, 2020 12:20 GMT 5:45 )Fourth column: tweetFifth column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.The tweets have been collected by the LSTM model deployed here at sentiment.live.To make it easy for the NLP researchers to get access to the sentiment analysis of each collected tweet, the sentiment score out of TextBlob [2] has been appended as the last column. New databases will be added to this dataset every week. Bookmark this page for further updates. [1] https://sentiment.live/ [2] https://textblob.readthedocs.io/en/dev/
DOI: 10.5281/zenodo.3712692
title: Alvi15 Originality: Text Alignment

description: We provide you with a training corpus that consists of pairs of documents, one of which may contain passages of text reused from the other. The reused text is subject to various kinds of (automatic) obfuscation to hide the fact it has been reused. Enclosed in the evaluation corpora, a file named pairs is found, which lists all pairs of suspicious documents and source documents.
description: {"references": ["Faisal Alvi, Mark Stevenson, and Paul Clough. The Short Stories Corpus\u2014Notebook for PAN at CLEF 2015. In Linda Cappellato, Nicola Ferro, Gareth Jones, and Eric San Juan, editors, CLEF 2015 Evaluation Labs and Workshop \u2013 Working Notes Papers, 8-11 September, Toulouse, France, September 2015. CEUR-WS.org. ISSN 1613-0073.", "Efstathios Stamatatos, Martin Potthast, Francisco Rangel, Paolo Rosso, and Benno Stein. Overview of the PAN/CLEF 2015 Evaluation Lab. In Josiane Mothe et al, editors, Experimental IR Meets Multilinguality, Multimodality, and Interaction. 6th International Conference of the CLEF Initiative (CLEF 2015), pages 518-538, Berlin Heidelberg New York, September 2015. Springer. ISBN 978-3-319-24026-8."]}
DOI: 10.5281/zenodo.3712691
title: Alvi15 Originality: Text Alignment

description: We provide you with a training corpus that consists of pairs of documents, one of which may contain passages of text reused from the other. The reused text is subject to various kinds of (automatic) obfuscation to hide the fact it has been reused. Enclosed in the evaluation corpora, a file named pairs is found, which lists all pairs of suspicious documents and source documents.
description: {"references": ["Faisal Alvi, Mark Stevenson, and Paul Clough. The Short Stories Corpus\u2014Notebook for PAN at CLEF 2015. In Linda Cappellato, Nicola Ferro, Gareth Jones, and Eric San Juan, editors, CLEF 2015 Evaluation Labs and Workshop \u2013 Working Notes Papers, 8-11 September, Toulouse, France, September 2015. CEUR-WS.org. ISSN 1613-0073.", "Efstathios Stamatatos, Martin Potthast, Francisco Rangel, Paolo Rosso, and Benno Stein. Overview of the PAN/CLEF 2015 Evaluation Lab. In Josiane Mothe et al, editors, Experimental IR Meets Multilinguality, Multimodality, and Interaction. 6th International Conference of the CLEF Initiative (CLEF 2015), pages 518-538, Berlin Heidelberg New York, September 2015. Springer. ISBN 978-3-319-24026-8."]}
DOI: 10.5281/zenodo.3712690
title: Link to dataset related to article "Development, application and computational analysis of high-dimensional fluorescent antibody panels for single-cell flow cytometry"

description: The interrogation of single cells is revolutionizing biology, especially our understanding of the immune system. Flow cytometry is still one of the most versatile and high-throughput approaches for single-cell analysis, and its capability has been recently extended to detect up to 28 colors, thus approaching the utility of cytometry by time of flight (CyTOF). However, flow cytometry suffers from autofluorescence and spreading error (SE) generated by errors in the measurement of photons mainly at red and far-red wavelengths, which limit barcoding and the detection of dim markers. Consequently, development of 28-color fluorescent antibody panels for flow cytometry is laborious and time consuming. Here, we describe the steps that are required to successfully achieve 28-color measurement capability. To do this, we provide a reference map of the fluorescence spreading errors in the 28-color space to simplify panel design and predict the success of fluorescent antibody combinations. Finally, we provide detailed instructions for the computational analysis of such complex data by existing, popular algorithms (PhenoGraph and FlowSOM). We exemplify our approach by designing a high-dimensional panel to characterize the immune system, but we anticipate that our approach can be used to design any high-dimensional flow cytometry panel of choice. The full protocol takes a few days to complete, depending on the time spent on panel design and data analysis. link related to dataset: https://flowrepository.org/id/FR-FCM-ZYV3
DOI: 10.5281/zenodo.3712689
title: Link to dataset related to article "Development, application and computational analysis of high-dimensional fluorescent antibody panels for single-cell flow cytometry"

description: The interrogation of single cells is revolutionizing biology, especially our understanding of the immune system. Flow cytometry is still one of the most versatile and high-throughput approaches for single-cell analysis, and its capability has been recently extended to detect up to 28 colors, thus approaching the utility of cytometry by time of flight (CyTOF). However, flow cytometry suffers from autofluorescence and spreading error (SE) generated by errors in the measurement of photons mainly at red and far-red wavelengths, which limit barcoding and the detection of dim markers. Consequently, development of 28-color fluorescent antibody panels for flow cytometry is laborious and time consuming. Here, we describe the steps that are required to successfully achieve 28-color measurement capability. To do this, we provide a reference map of the fluorescence spreading errors in the 28-color space to simplify panel design and predict the success of fluorescent antibody combinations. Finally, we provide detailed instructions for the computational analysis of such complex data by existing, popular algorithms (PhenoGraph and FlowSOM). We exemplify our approach by designing a high-dimensional panel to characterize the immune system, but we anticipate that our approach can be used to design any high-dimensional flow cytometry panel of choice. The full protocol takes a few days to complete, depending on the time spent on panel design and data analysis. link related to dataset: https://flowrepository.org/id/FR-FCM-ZYV3
DOI: 10.5281/zenodo.3712688
title: Niobe Planitia Region (I-2467), Venus: base maps for geologic mapping.

description: This data repository contains the base map for the geologic mapping of the Niobe Planitia Region (I-2467) of Venus. Data for this study were provided by the U.S. Geological Survey (USGS) Astrogeology Team in the projection parameters (Mercator projection). The data are also available online from the USGS Map-a-planet website (https://astrocloud.wr.usgs.gov/ ). The datasets include: (a) Cycle 1 (east-directed illumination, or left-looking) SAR images cover essentially the entire I-2467 map area; (b) Topography (Global Topographic Data Record 3; GTDR 3); (c) Slope data (Global Slope Data Record; GSDR); (d) Reflectivity (Global Reflectivity Data Record; GRDR); and (e) Emissivity (Global Emissivity Data Record; GEDR).
DOI: 10.5281/zenodo.3712700
title: Checklist of the mosses of mountain ranges of the Pechora-Ilych State Nature Reserve (Northern Urals, Basins of the Pechora and Ilych Rivers)

description: The Checklist of the mosses of mountain ranges of the Pechora-Ilych State Nature Reserve (Northern Urals, Basins of the Pechora and Ilych Rivers) is presented. The Checklist includes 173 species of mosses from 78 genera and 33 families, which is almost half of the species diversity of mosses in the Pechora-Ilych State Nature Reserve (348 species ).
description: {"references": ["1. Abramova A. L., Abramov I. I. K brioflore Severo-Vostoka evropejskoj chasti SSSR [Bryoflora to the North-East of the European part of the USSR]. Nov. sist. nizsh. rast. 1983. Vol. 20. P. 168\u2013173.\u00ac", "2. Bezgodov A. G., Gol'dberg I. L., Dulin M. V., Shubina T. P., Kucherov I. B. Dopolneniya k brioflore Pechoro-Ilychskogo biosfernogo zapovednika (Severnyj Ural) [On the bryoflora of Pechora-Ilych biosphere reserve (the Northern Urals)] / \u0410rctoa. 2003. Vol. 12. P. 169\u2013178. doi: 10.15298/arctoa.12.17", "3. Bezgodov A. G. New moss records from Komi Republic. 5 / New Bryophyte Records. 4 // \u0410rctoa. 2015. Vol. 24. P. 227\u2013228. doi: 10.15298/arctoa.24.51", "4. Check-list of mosses of East Europe and North Asia / M. S. Ignatov, O. M. Afonina, E. A. Ignatova et al. Arctoa. 2006. Vol. 15. P. 1\u2013130. doi: 10.15298/arctoa.15.01", "5. Korchagin A. A. Rastitel'nost' severnoj poloviny Pechoro-Ilychskogo zapovednika [Vegetation of the Northern half of the Pechora-Ilych Reserve]. Trudy Pech.-Ilych. zapoved. [Proceed. Pech.-Ilych. Reserve]. Moskow. 1940. Vol. 2. 416 p.", "6. Vzaimosvyazi komponentov lesnyh i bolotnyh ekosistem srednej tajgi Priural'ya [Interrelations of components of forest and swamp ecosystems of the middle taiga of the Urals]. Ed. V. I. Vasilevich. Leningrad: Nauka, 1980. 254 p.", "7. Zheleznova G. V., Shubina T. P. Mohoobraznye Pechoro-Ilychskogo zapovednika (annotirovannyj spisok vidov) [Mosses of the Pechora-Ilych Reserve (annotated list of species)]. Flora i fauna zapovednikov [Flora and fauna of nature reserves]. Moskow. 1998. Vol. 65. 34 p.", "8. Zheleznova G. V., Shubina T. P. Vidovoe raznoobrazie listostebel'nyh mhov Pechero-Ilychskogo zapovednika [Species diversity of mosses of the Pechora-Ilych Reserve]. Trudy Pech.-Ilych. zapoved. [Proceed. Pech.-Ilych. Reserve]. Syktyvkar. 2005 Vol. 14. P. 58\u201362.", "9. Zheleznova G. V., Shubina T. P. Annotirovannyj spisok listostebel'nyh mhov Pechoro-Ilychskogo biosfernogo zapovednika [Annotated list of mosses of the Pechora-Ilych biosphere reserve]. Trudy Pech.-Ilych. zapoved. [Proceed. Pech.-Ilych. Reserve]. Syktyvkar. 2015. Vol. 17. P. 76\u201391.", "10. Zheleznova G. V., Shubina T. P. Brioflora Pechoro-Ilychskogo biosfernogo zapovednika [Bryoflora of the Pechora-Ilych Biosphere Reserve]. Flora i rastitel'nost' Pechoro-Ilychskogo biosfernogo zapovednika [Flora and vegetation of the Pechora-Ilych biosphere reserve]. Ekaterinburg: UB RAS, 1997. P. 177\u2013210."]}
DOI: 10.5281/zenodo.3712687
title: Niobe Planitia Region (I-2467), Venus: base maps for geologic mapping.

description: This data repository contains the base map for the geologic mapping of the Niobe Planitia Region (I-2467) of Venus. Data for this study were provided by the U.S. Geological Survey (USGS) Astrogeology Team in the projection parameters (Mercator projection). The data are also available online from the USGS Map-a-planet website (https://astrocloud.wr.usgs.gov/ ). The datasets include: (a) Cycle 1 (east-directed illumination, or left-looking) SAR images cover essentially the entire I-2467 map area; (b) Topography (Global Topographic Data Record 3; GTDR 3); (c) Slope data (Global Slope Data Record; GSDR); (d) Reflectivity (Global Reflectivity Data Record; GRDR); and (e) Emissivity (Global Emissivity Data Record; GEDR).
DOI: 10.26066/rds.36036
title: Ground Penetrating Radar (GPR) point data of the thickness of Rikha Samba glacier in 2010

description: Point thickness data of Rikha Samba glacier in Nepal collected with an impulse GPR transmitter (Ohio State University) with a set of half-wavelength 5 MHz dipole antennas in 2010.
DOI: 10.5281/zenodo.3712705
title: Frequency dependence of In-suit permeability estimation of well-aquifer system from periodic loading

description: Data used in manuscript named "Frequency dependence of In-suit permeability estimation of well-aquifer system from periodic loading"
DOI: 10.5281/zenodo.3712706
title: Frequency dependence of In-suit permeability estimation of well-aquifer system from periodic loading

description: Data used in manuscript named "Frequency dependence of In-suit permeability estimation of well-aquifer system from periodic loading"
DOI: 10.21227/n2fv-ae84
title: Brainwave EEG Dataset

description: This dataset is a collection of brainwave EEG signals from eight subjects. The data is collected in a lab controlled environment under a specific visualization experiment.
DOI: 10.21227/wx49-nx85
title: Brainwave EEG Dataset

description: This dataset is a collection of brainwave EEG signals from eight subjects. The data is collected in a lab controlled environment under a specific visualization experiment.
DOI: 10.5281/zenodo.3712719
title: Hanif15 Originality: Text Alignment

description: We provide you with a training corpus that consists of pairs of documents, one of which may contain passages of text reused from the other. The reused text is subject to various kinds of (automatic) obfuscation to hide the fact it has been reused. Enclosed in the evaluation corpora, a file named pairs is found, which lists all pairs of suspicious documents and source documents.
description: {"references": ["Efstathios Stamatatos, Martin Potthast, Francisco Rangel, Paolo Rosso, and Benno Stein. Overview of the PAN/CLEF 2015 Evaluation Lab. In Josiane Mothe et al, editors, Experimental IR Meets Multilinguality, Multimodality, and Interaction. 6th International Conference of the CLEF Initiative (CLEF 2015), pages 518-538, Berlin Heidelberg New York, September 2015. Springer. ISBN 978-3-319-24026-8.", "Israr Hanif, Adeel Nawab, Affifa Arbab, Huma Jamshed, Sara Riaz, and Ehsan Munir. Cross-Language Urdu-English (CLUE) Text Alignment Corpus\u2014Notebook for PAN at CLEF 2015. In Linda Cappellato, Nicola Ferro, Gareth Jones, and Eric San Juan, editors, CLEF 2015 Evaluation Labs and Workshop \u2013 Working Notes Papers, 8-11 September, Toulouse, France, September 2015. CEUR-WS.org. ISSN 1613-0073."]}
DOI: 10.5281/zenodo.3712718
title: Hanif15 Originality: Text Alignment

description: We provide you with a training corpus that consists of pairs of documents, one of which may contain passages of text reused from the other. The reused text is subject to various kinds of (automatic) obfuscation to hide the fact it has been reused. Enclosed in the evaluation corpora, a file named pairs is found, which lists all pairs of suspicious documents and source documents.
description: {"references": ["Efstathios Stamatatos, Martin Potthast, Francisco Rangel, Paolo Rosso, and Benno Stein. Overview of the PAN/CLEF 2015 Evaluation Lab. In Josiane Mothe et al, editors, Experimental IR Meets Multilinguality, Multimodality, and Interaction. 6th International Conference of the CLEF Initiative (CLEF 2015), pages 518-538, Berlin Heidelberg New York, September 2015. Springer. ISBN 978-3-319-24026-8.", "Israr Hanif, Adeel Nawab, Affifa Arbab, Huma Jamshed, Sara Riaz, and Ehsan Munir. Cross-Language Urdu-English (CLUE) Text Alignment Corpus\u2014Notebook for PAN at CLEF 2015. In Linda Cappellato, Nicola Ferro, Gareth Jones, and Eric San Juan, editors, CLEF 2015 Evaluation Labs and Workshop \u2013 Working Notes Papers, 8-11 September, Toulouse, France, September 2015. CEUR-WS.org. ISSN 1613-0073."]}
DOI: 10.21227/w1nt-2276
title: Brainwave EEG Dataset

description: This dataset is a collection of brainwave EEG signals from eight subjects. The data is collected in a lab controlled environment under a specific visualization experiment.
DOI: 10.5281/zenodo.3712710
title: Cheema15 Originality: Text Alignment

description: We provide you with a training corpus that consists of pairs of documents, one of which may contain passages of text reused from the other. The reused text is subject to various kinds of (automatic) obfuscation to hide the fact it has been reused. Enclosed in the evaluation corpora, a file named pairs is found, which lists all pairs of suspicious documents and source documents.
description: {"references": ["Waqas Arshad Cheema, Fahad Najib, Shakil Ahmed, Syed Husnain Bukhari, Abdul Sittar, and Rao Muhammad Adeel Nawab. A Corpus for Analyzing Text Reuse by People of Different Groups\u2014Notebook for PAN at CLEF 2015. In Linda Cappellato, Nicola Ferro, Gareth Jones, and Eric San Juan, editors, CLEF 2015 Evaluation Labs and Workshop \u2013 Working Notes Papers, 8-11 September, Toulouse, France, September 2015. CEUR-WS.org. ISSN 1613-0073.", "Efstathios Stamatatos, Martin Potthast, Francisco Rangel, Paolo Rosso, and Benno Stein. Overview of the PAN/CLEF 2015 Evaluation Lab. In Josiane Mothe et al, editors, Experimental IR Meets Multilinguality, Multimodality, and Interaction. 6th International Conference of the CLEF Initiative (CLEF 2015), pages 518-538, Berlin Heidelberg New York, September 2015. Springer. ISBN 978-3-319-24026-8."]}