Find research datasets worth reusing

opencc-by-4.0Jul 2019View details →

Data from: Andriollo T., Gillet F., Michaux J.R., Ruedi M. (2019). The menu varies with metabarcoding practices: A case study with the bat Plecotus auritus. PLoS ONE 14(7)

Supporting data for: Andriollo T., Gillet F., Michaux J.R., Ruedi M. (2019). The menu varies with metabarcoding practices: a case study with the bat Plecotus auritus. PLoS ONE 14(7): e0219135. https://doi.org/10.1371/journal.pone.0219135 Raw DNA sequences of prey of Plecotus auritus. Sampling information separated by semicolums as folows: >Sequence number; Colony; Date; Sample name; Dataset; Is the sequence attributable to the diet or not (Diet); Read numbers (Size); DNA sequence

opencc-zeroAug 2024View details →

EOL Dynamic Hierarchy Trunk (trunk): Dynamic Hierarchy Trunk 14 June 2017

This is the trunk for the EOL reference hierarchy. It determines the relationships among the higher taxa and adds a few taxa that are not covered by other resources. The EOL DH trunk is maintained in [TTT](http://ttt.biodinfo.org/) developed by Colin (Congtian Lin) and Jiangning Wang from Biodiversity Informatics Group of the Institute of Zoology, Chinese Academy of Sciences. ##References Adl, S. M., et al. 2019. Revisions to the classification, nomenclature, and diversity of eukaryotes. Journal of Eukaryotic Microbiology 66, 4–119. https://doi.org/10.1111/jeu.12691 Aguiar, A.P., Deans, A.R., Engel, M.S., Forshage, M., Huber, J.T., Jennings, J.T., Johnson, N.F., Lelej, A.S., Longino, J.T., Lohrmann, V., Mikó, I., Ohl, M., Rasmussen, C., Taeger, A., Yu, D.S.K., 2013. Order Hymenoptera . In : Zhang, Z.-Q. (Ed.) Animal Biodiversity: An Outline of Higher-level Classification and Survey of Taxonomic Richness (Addenda 2013). Zootaxa 3703, 51–62. https://doi.org/10.11646/zootaxa.3703.1.12 Aspöck, U., Haring, E., Aspöck, H., 2012. The phylogeny of the Neuropterida: long lasting and current controversies and challenges (Insecta: Endopterygota). Arthropod Systematics & Phylogeny 70, 119–129. Benton, M., 2014. Vertebrate Palaeontology. John Wiley & Sons. Betancur-R, R., Wiley, E.O., Arratia, G., Acero, A., Bailly, N., Miya, M., Lecointre, G., Ortí, G., 2017. Phylogenetic classification of bony fishes. BMC Evolutionary Biology 17, 162. https://doi.org/10.1186/s12862-017-0958-3 Bleidorn, Christoph. 2019. Recent Progress in Reconstructing Lophotrochozoan (Spiralian) Phylogeny." Organisms Diversity & Evolution 19, no. 4 (December 1, 2019): 557–66. https://doi.org/10.1007/s13127-019-00412-4. Bouchard, P., Bousquet, Y., Davies, A., Alonso-Zarazaga, M., Lawrence, J., Lyal, C., Newton, A., Reid, C., Schmitt, M., Slipinski, A., Smith, A., 2011. Family-Group Names In Coleoptera (Insecta). ZooKeys 88, 1–972. https://doi.org/10.3897/zookeys.88.807 Cannon, Johanna Taylor, Bruno Cossermelli Vellutini, Julian Smith, Fredrik Ronquist, Ulf Jondelius, and Andreas Hejnol. 2016. Xenacoelomorpha Is the Sister Group to Nephrozoa. Nature 530(7588):89–93. https://doi.org/10.1038/nature16520. Davis, R.B., Baldauf, S.L., Mayhew, P.J., 2010. The origins of species richness in the Hymenoptera: insights from a family-level supertree. BMC Evolutionary Biology 10, 109. https://doi.org/10.1186/1471-2148-10-109 Dunlop, J. A., Penney, D. & Jekel, D. 2015. A summary list of fossil spiders and their relatives. In World Spider Catalog. Natural History Museum Bern, online at http://wsc.nmbe.ch Dunn, C.W., Giribet, G., Edgecombe, G.D., Hejnol, A., 2014. Animal Phylogeny and Its Evolutionary Implications. Annu. Rev. Ecol. Evol. Syst. 45, 371–395. https://doi.org/10.1146/annurev-ecolsys-120213-091627 Foottit, R. G., Adler, P. H., eds. 2017. Insect Biodiversity: Science and Society, Volume 1 & 2. 2nd Edition. Wiley-Blackwell. Fritz, U., Havaš, P., 2013. Order Testudines: 2013 update. In : Zhang, Z.-Q. (Ed.) Animal Biodiversity: An Outline of Higher-level Classification and Survey of Taxonomic Richness (Addenda 2013). Zootaxa 3703, 12–14. https://doi.org/10.11646/zootaxa.3703.1.4 Giribet, Gonzalo. 2016. New Animal Phylogeny: Future Challenges for Animal Phylogeny in the Age of Phylogenomics. Organisms Diversity & Evolution 16 (2):419–26. https://doi.org/10.1007/s13127-015-0236-4. Giribet, Gonzalo, and Gregory D. Edgecombe. 2020. The Invertebrate Tree of Life. Princeton, United States: Princeton University Press, 2020. Guy, L., Ettema, T.J.G., 2011. The archaeal __TACK__ superphylum and the origin of eukaryotes. Trends in Microbiology 19, 580–587. https://doi.org/10.1016/j.tim.2011.09.002 Hinchliff, C.E., Smith, S.A., Allman, J.F., Burleigh, J.G., Chaudhary, R., Coghill, L.M., Crandall, K.A., Deng, J., Drew, B.T., Gazis, R., Gude, K., Hibbett, D.S., Katz, L.A., Laughinghouse, H.D., McTavish, E.J., Midford, P.E., Owen, C.L., Ree, R.H., Rees, J.A., Soltis, D.E., Williams, T., Cranston, K.A., 2015. Synthesis of phylogeny and taxonomy into a comprehensive tree of life. PNAS 112, 12764–12769. https://doi.org/10.1073/pnas.1423041112 Holzenthal, R.W., Morse, J.C., Kjer, K.M., 2011. Order Trichoptera Kirby, 1813. In: Zhang, Z.-Q. (Ed.) Animal biodiversity: An outline of higher-level classification and survey of taxonomic richness. Zootaxa 3148, 209. https://doi.org/10.11646/zootaxa.3148.1.40 Hormiga, G., Griswold, C.E., 2014. Systematics, Phylogeny, and Evolution of Orb-Weaving Spiders. Annu. Rev. Entomol. 59, 487–512. https://doi.org/10.1146/annurev-ento-011613-162046 James, S.W., Davidson, S.K., 2012. Molecular phylogeny of earthworms (Annelida:Crassiclitellata) based on 28S, 18S and 16S gene sequences. Invertebrate Systematics 26, 213. https://doi.org/10.1071/IS11012 Jarvis, E.D., Mirarab, S., Aberer, A.J., Li, B., Houde, P., Li, C., Ho, S.Y.W., Faircloth, B.C., Nabholz, B., Howard, J.T., Suh, A., Weber, C.C., Fonseca, R.R. da, Li, J., Zhang, F., Li, H., Zhou, L., Narula, N., Liu, L., Ganapathy, G., Boussau, B., Bayzid, M.S., Zavidovych, V., Subramanian, S., Gabaldón, T., Capella-Gutiérrez, S., Huerta-Cepas, J., Rekepalli, B., Munch, K., Schierup, M., Lindow, B., Warren, W.C., Ray, D., Green, R.E., Bruford, M.W., Zhan, X., Dixon, A., Li, S., Li, N., Huang, Y., Derryberry, E.P., Bertelsen, M.F., Sheldon, F.H., Brumfield, R.T., Mello, C.V., Lovell, P.V., Wirthlin, M., Schneider, M.P.C., Prosdocimi, F., Samaniego, J.A., Velazquez, A.M.V., Alfaro-Núñez, A., Campos, P.F., Petersen, B., Sicheritz-Ponten, T., Pas, A., Bailey, T., Scofield, P., Bunce, M., Lambert, D.M., Zhou, Q., Perelman, P., Driskell, A.C., Shapiro, B., Xiong, Z., Zeng, Y., Liu, S., Li, Z., Liu, B., Wu, K., Xiao, J., Yinqi, X., Zheng, Q., Zhang, Y., Yang, H., Wang, J., Smeds, L., Rheindt, F.E., Braun, M., Fjeldsa, J., Orlando, L., Barker, F.K., Jønsson, K.A., Johnson, W., Koepfli, K.-P., O__Brien, S., Haussler, D., Ryder, O.A., Rahbek, C., Willerslev, E., Graves, G.R., Glenn, T.C., McCormack, J., Burt, D., Ellegren, H., Alström, P., Edwards, S.V., Stamatakis, A., Mindell, D.P., Cracraft, J., Braun, E.L., Warnow, T., Jun, W., Gilbert, M.T.P., Zhang, G., 2014. Whole-genome analyses resolve early branches in the tree of life of modern birds. Science 346, 1320–1331. https://doi.org/10.1126/science.1253451 Kathirithamby, J., Engel, M.S., 2014. A Revised Key to the Living and Fossil Families of Strepsiptera, with the Description of a New Family, Cretostylopidae. Journal of the Kansas Entomological Society 87, 385–388. https://doi.org/10.2317/JKES140407.1 Kjer, K.M., Simon, C., Yavorskaya, M., Beutel, R.G., 2016. Progress, pitfalls and parallel universes: a history of insect phylogenetics. Journal of The Royal Society Interface 13, 20160363. https://doi.org/10.1098/rsif.2016.0363 Klopfstein, S., Vilhelmsen, L., Heraty, J.M., Sharkey, M., Ronquist, F., 2013. The Hymenopteran Tree of Life: Evidence from Protein-Coding Genes and Objectively Aligned Ribosomal Data. PLoS ONE 8, e69344. https://doi.org/10.1371/journal.pone.0069344 Kocot, Kevin M., Torsten H. Struck, Julia Merkel, Damien S. Waits, Christiane Todt, Pamela M. Brannock, David A. Weese, et al. 2016. Phylogenomics of Lophotrochozoa with Consideration of Systematic Error. Systematic Biology, syw079. https://doi.org/10.1093/sysbio/syw079. Laumer, Christopher E., Rosa Fernández, Sarah Lemer, David Combosch, Kevin M. Kocot, Ana Riesgo, Sónia C. S. Andrade, Wolfgang Sterrer, Martin V. Sørensen, and Gonzalo Giribet. 2019. Revisiting Metazoan Phylogeny with Genomic Sampling of All Phyla. Proceedings of the Royal Society B: Biological Sciences 286 (1906): 20190831. https://doi.org/10.1098/rspb.2019.0831. Laumer, Christopher E., Nicolas Bekkouche, Alexandra Kerbl, Freya Goetz, Ricardo C. Neves, Martin V. Sørensen, Reinhardt M. Kristensen, et al. 2015. Spiralian Phylogeny Informs the Evolution of Microscopic Lineages. Current Biology 25(15): 2000–2006. https://doi.org/10.1016/j.cub.2015.06.068. Leschen, R.A.B., Beutel, R.G., 2014. Morphology and Systematics: Phytophaga. Walter de Gruyter. Li, H., Shao, R., Song, N., Song, F., Jiang, P., Li, Z., Cai, W., 2015. Higher-level phylogeny of paraneopteran insects inferred from mitochondrial genome sequences. Scientific Reports 5. https://doi.org/10.1038/srep08527 Lozano-Fernandez, J., Tanner, A.R., Giacomelli, M., Carton, R., Vinther, J., Edgecombe, G.D., Pisani, D., 2019. Increasing species sampling in chelicerate genomic-scale datasets provides support for monophyly of Acari and Arachnida. Nature Communications 10, 2295. https://doi.org/10.1038/s41467-019-10244-7 Malm, T., Nyman, T., 2015. Phylogeny of the symphytan grade of Hymenoptera: new pieces into the old jigsaw(fly) puzzle. Cladistics 31, 1–17. https://doi.org/10.1111/cla.12069 Marlétaz, Ferdinand, Katja T. C. A. Peijnenburg, Taichiro Goto, Noriyuki Satoh, and Daniel S. Rokhsar. 2019. A New Spiralian Phylogeny Places the Enigmatic Arrow Worms among Gnathiferans. Current Biology 29(2):312-318.e3. https://doi.org/10.1016/j.cub.2018.11.042. Nakano, T., Ramlah, Z., Hikida, T., 2012. Phylogenetic position of gastrostomobdellid leeches (Hirudinida, Arhynchobdellida, Erpobdelliformes) and a new family for the genus Orobdella. Zoologica Scripta 41, 177–185. https://doi.org/10.1111/j.1463-6409.2011.00506.x Naylor, G.J.P., Caira, J.N., Jensen, K.R.E., Rosana, K.M., Straube, N., Lakner, C., 2012. Elasmobranch Phylogeny: A Mitochondrial Estimate Based on 595 Species. In J.C. Carrier, J.A. Musick and M.R. Heithaus (editors), The Biology of Sharks and Their Relatives. 31-56. CRC Press, Taylor & Francis Group. Nesbitt, S.J., 2011. The Early Evolution of Archosaurs: Relationships and the Origin of Major Clades. Bulletin of the American Museum of Natural History, 2011(352):1-292. https://doi.org/10.1206/352.1 Nesnidal, Maximilian P., Martin Helmkampf, Achim Meyer, Alexander Witek, Iris Bruchhaus, Ingo Ebersberger, Thomas Hankeln, Bernhard Lieb, Torsten H. Struck, and Bernhard Hausdorf. 2013. New Phylogenomic Data Support the Monophyly of Lophophorata and an Ectoproct-Phoronid Clade and Indicate That Polyzoa and Kryptrochozoa Are Caused by Systematic Bias. BMC Evolutionary Biology 13(1): 253. https://doi.org/10.1186/1471-2148-13-253. Oaks, J.R., 2011. A Time-Calibrated Species Tree of Crocodylia Reveals a Recent Radiation of the True Crocodiles. Evolution 65, 3285–3297. https://doi.org/10.1111/j.1558-5646.2011.01373.x Okamura, B., Gruhl, A., Reft, A.J., 2015. Cnidarian Origins of the Myxozoa, in: Okamura, B., Gruhl, A., Bartholomew, J.L. (Eds.), Myxozoan Evolution, Ecology and Development. Springer International Publishing, Cham, pp. 45–68. https://doi.org/10.1007/978-3-319-14753-6_3 Pyron, R.A., Burbrink, F.T., Wiens, J.J., 2013. A phylogeny and revised classification of Squamata, including 4161 species of lizards and snakes. BMC Evolutionary Biology 13, 93. https://doi.org/10.1186/1471-2148-13-93 Robertson, J.A., Ślipiński, A., Moulton, M., Shockley, F.W., Giorgi, A., Lord, N.P., Mckenna, D.D., Tomaszewska, W., Forrester, J., Miller, K.B., Whiting, M.F., Mchugh, J.V., 2015. Phylogeny and classification of Cucujoidea and the recognition of a new superfamily Coccinelloidea (Coleoptera: Cucujiformia): Systematics of Cucujoidea and Coccinelloidea. Systematic Entomology 40, 745–778. https://doi.org/10.1111/syen.12138 Rouse, Greg W., Nerida G. Wilson, Jose I. Carvajal, and Robert C. Vrijenhoek. 2016. New Deep-Sea Species of Xenoturbella and the Position of Xenacoelomorpha. Nature 530(7588):94–97. https://doi.org/10.1038/nature16545. Ruhfel, B.R., Gitzendanner, M.A., Soltis, P.S., Soltis, D.E., Burleigh, J.G., 2014. From algae to angiosperms–inferring the phylogeny of green plants (Viridiplantae) from 360 plastid genomes. BMC Evolutionary Biology 14, 23. https://doi.org/10.1186/1471-2148-14-23 Schiffer, Philipp H., Helen E. Robertson, and Maximilian J. Telford. 2018. Orthonectids Are Highly Degenerate Annelid Worms. Current Biology 28(12):1970-1974.e3. https://doi.org/10.1016/j.cub.2018.04.088. The Angiosperm Phylogeny Group, 2016. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG IV. Bot. J. Linn. Soc. 181, 1–20. https://doi.org/10.1111/boj.12385 Van Nieukerken, E.J., Kaila, L., Kitching, I.J., Kristensen, N.P., Lees, D.C., Minet, J., Mitter, C., Mutanen, M., Regier, J.C., Simonsen, T.J., Wahlberg, N., Yen, S.-H., Zahiri, R., Adamski, D., Baixeras, J., Bartsch, D., Bengtsson, B.Å., Brown, J.W., Bucheli, S.R., Davis, D.R., Prins, J.D., Prins, W.D., Epstein, M.E., Gentili-Poole, P., Gielis, C., Hättenschwiler, P., Hausmann, A., Holloway, J.D., Kallies, A., Karsholt, O., Kawahara, A.Y., Koster, S.J.C., Kozlov, M.V., Lafontaine, J.D., Lamas, G., Landry, J.-F., Lee, S., Nuss, M., Park, K.-T., Penz, C., Rota, J., Schintlmeister, A., Schmidt, B.C., Sohn, J.-C., Solis, M.A., Tarmann, G.M., Warren, A.D., Weller, S., Yakovlev, R.V., Zolotuhin, V.V., Zwick, A., 2011. Order Lepidoptera Linnaeus, 1758. In: Zhang, Z.-Q. (Ed.) Animal biodiversity: An outline of higher-level classification and survey of taxonomic richness. Zootaxa 3148, 212. https://doi.org/10.11646/zootaxa.3148.1.41 Vea, I.M., Grimaldi, D.A., 2015. Diverse New Scale Insects (Hemiptera: Coccoidea) in Amber from the Cretaceous and Eocene with a Phylogenetic Framework for Fossil Coccoidea. novi 2015, 1–15. https://doi.org/10.1206/3823.1 Vélez-Zuazo, X., Agnarsson, I., 2011. Shark tales: A molecular species-level phylogeny of sharks (Selachimorpha, Chondrichthyes). Molecular Phylogenetics and Evolution 58, 207–217. https://doi.org/10.1016/j.ympev.2010.11.018 Weigert, A., Bleidorn, C., 2016. Current status of annelid phylogeny. Org Divers Evol 16, 345–362. https://doi.org/10.1007/s13127-016-0265-7 Weirauch, C., Schuh, R.T., 2011. Systematics and Evolution of Heteroptera: 25 Years of Progress. Annu. Rev. Entomol. 56, 487–510. https://doi.org/10.1146/annurev-ento-120709-144833 Wiegmann, B.M., Trautwein, M.D., Winkler, I.S., Barr, N.B., Kim, J.-W., Lambkin, C., Bertone, M.A., Cassel, B.K., Bayless, K.M., Heimberg, A.M., Wheeler, B.M., Peterson, K.J., Pape, T., Sinclair, B.J., Skevington, J.H., Blagoderov, V., Caravas, J., Kutty, S.N., Schmidt-Ott, U., Kampmeier, G.E., Thompson, F.C., Grimaldi, D.A., Beckenbach, A.T., Courtney, G.W., Friedrich, M., Meier, R., Yeates, D.K., 2011. Episodic radiations in the fly tree of life. Proceedings of the National Academy of Sciences 108, 5690–5695. https://doi.org/10.1073/pnas.1012675108 Winterton, S.L., Hardy, N.B., Wiegmann, B.M., 2010. On wings of lace: phylogeny and Bayesian divergence time estimates of Neuropterida (Insecta) based on morphological and molecular data. Systematic Entomology 35, 349–378. https://doi.org/10.1111/j.1365-3113.2010.00521.x Yuri, T., Kimball, R.T., Harshman, J., Bowie, R.C.K., Braun, M.J., Chojnowski, J.L., Han, K.-L., Hackett, S.J., Huddleston, C.J., Moore, W.S., Reddy, S., Sheldon, F.H., Steadman, D.W., Witt, C.C., Braun, E.L., 2013. Parsimony and Model-Based Analyses of Indels in Avian Nuclear Genes Reveal Congruent and Incongruent Phylogenetic Signals. Biology 2, 419–444. https://doi.org/10.3390/biology2010419 Zverkov, Oleg A., Kirill V. Mikhailov, Sergey V. Isaev, Leonid Y. Rusin, Olga V. Popova, Maria D. Logacheva, Alexey A. Penin, et al. 2019. Dicyemida and Orthonectida: Two Stories of Body Plan Simplification. Frontiers in Genetics 10. https://doi.org/10.3389/fgene.2019.00443.More minor tweaks to fix homonym problems. The Encyclopedia of Life (EOL, eol.org) aggregates biodiversity information from more than 400 sources and provides access to the data through taxon pages, visual query and application programming interfaces. Scientific names are essential elements of the data integration infrastructure, but their shortcomings as key identifiers are well documented (Patterson et al., 2016). Complex automated workflows and continuous manual curation are required to address idiosyncrasies of source taxonomies, variation in data quality, and conflicting taxonomic opinions. To achieve a harmonized taxonomic view of EOL content, names from data sources are mapped to a dynamic reference hierarchy ([see current version here](https://opendata.eol.org/dataset/tram-807-808-809-810-dh-v1-1/resource/00adb47b-57ed-4f6b-8f66-83bfdb5120e8)) using an algorithm that leverages canonical name strings, hierarchical information (ancestry, descendants), taxonomic ranks, synonym data, and author strings. Names that cannot be associated with a reference taxon are still accessible, but their unmapped status excludes them and any associated content from certain core EOL functions. For more information about the EOL taxonomy, see [EOL Dynamic Hierarchy](https://eol.org/docs/eol-dynamic-hierarchy)

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 2 of 14

This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the second part of 14 parts of the full dataset (2/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms,  500ms, and echo time (TE) = 15ms. Under each simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each  simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references. Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 3 of 14

This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the third part of 14 parts of the full dataset (3/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms,  500ms, and echo time (TE) = 20ms. Under each simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each  simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references. Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 12 of 14

This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the twelfth part of 14 parts of the full dataset (12/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms,  800ms, and echo time (TE) = 30ms. Under each simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references. Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 5 of 14

This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the fifth part of 14 parts of the full dataset (5/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms,  500ms, and echo time (TE) = 30ms. Under each simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references. Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 4 of 14

This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the fourth part of 14 parts of the full dataset (4/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms,  500ms, and echo time (TE) = 25ms. Under each simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each of simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references. Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 11 of 14

This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the eleventh part of 14 parts of the full dataset (11/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms,  800ms, and echo time (TE) = 25ms. Under each simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references. Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 10 of 14

This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the tenth part of 14 parts of the full dataset (10/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms,  800ms, and echo time (TE) = 20ms. Under each simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references. Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 14 of 14

This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the fourteenth and final part of 14 parts of the full dataset (14/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms,  800ms, and echo time (TE) = 40ms. Under each simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references. Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 6 of 14

This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the sixth part of 14 parts of the full dataset (6/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms,  500ms, and echo time (TE) = 35ms. Under each simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references. Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 8 of 14

This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the eighth part of 14 parts of the full dataset (8/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms,  800ms, and echo time (TE) = 10ms. Under each simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references. Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 13 of 14

This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the thirteenth part of 14 parts of the full dataset (13/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms,  800ms, and echo time (TE) = 35ms. Under each simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references. Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>

Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 7 of 14

This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the seventh part of 14 parts of the full dataset (7/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms,  500ms, and echo time (TE) = 40ms. Under each simulated scanning sequence, there are 500 brain volumes. The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references. Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed. All parts of the whole dataset can be found at: Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a> Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a> Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a> Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a> Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a> Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a> Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a> Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a> Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a> Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a> Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a> Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a> Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a> Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a>

opencc-by-4.0Jul 2023View details →

Large SEM-BSE images of hydrated belite of ages from 14 days up to 1 year

This dataset contains 8-Bit SEM-BSE images of commercially available dicalcium silicate (C2S; belite; Vustah, Czech Republic). The belite was mixed with a water/binder ratio of 0.5. The paste was the cast in small sealed containers, which were submersed with water. The specimens were stored at 22 ± 2°C. After the desired hydration times (14, 28, 84, 365 days) the hydration was stopped by immersing the prisms in isopropanol and drying them at 60°C for 12 hours. The dried prisms were then embedded in low viscosity epoxy resin and mechanically polished using diamond paste with a grain size down to 0.25 µm. Finally, the specimens were coatet with a thin layer of carbon to avoid charging. The images were acquired at 12 kV using a CBS (concentric backscatter) detector within a Thermofischer Helios G4 UX. Table 1: Basic information like resolution, size and phase composition of the images. <table> <tbody> <tr> <td>file</td> <td>age</td> <td>size</td> <td>size</td> <td>area</td> <td>pores</td> <td>hydrates</td> <td>clinker</td> </tr> <tr> <td> </td> <td>in days</td> <td>in px</td> <td>in µm</td> <td>in mm²</td> <td>area-%</td> <td>area-%</td> <td>area-%</td> </tr> <tr> <td>C2S 14d.tif</td> <td>14</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>31.4</td> <td>43.5</td> <td>25.2</td> </tr> <tr> <td>C2S 28d.tif</td> <td>28</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>15.9</td> <td>62.6</td> <td>21.5</td> </tr> <tr> <td>C2S 84d.tif</td> <td>84</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>19.3</td> <td>65.6</td> <td>15.2</td> </tr> <tr> <td>C2S 365d.tif</td> <td>365</td> <td>36864 x 36864</td> <td>1554.0 x 1554.0</td> <td>2.41</td> <td>23.8</td> <td>64.4</td> <td>12.0</td> </tr> </tbody> </table> The proportions of pores, hydrates and unhydrated clinker shown in table 1 are the result of manual thresholding of denoised versions of these images and may therefore differ to own measurements. The scaling is backed into the file and can be read using ImageJ/Fiji. The unstitched files are provded as 7z archives. The sub-images were arranged in a 10 x 10 grid. The pixel scaling of these files is the same as in the larger files. Internal note These files are included in the following MAPS datasets: <ul> <li>2023_05_24 C32-C2S 14-84 d</li> <li>2023_06_08 C2S-C3S 28d-1year</li> </ul> Funding The research was supported by the Deutsche Forschungsgemeinschaft (DFG), grant number <a href="https://gepris.dfg.de/gepris/projekt/344069666">344069666</a>.

opencc-by-4.0Jul 2023View details →

Supplementary Data for Low-loss stable storage of 1.2 Angstrom X-ray pulses in a 14 m Bragg cavity

Supplementary Data for Margraf, R. et al. "Low-loss stable storage of 1.2 Angstrom X-ray pulses in a 14 m Bragg cavity," Nature Photonics, 2023.

openCC (other)Dec 2021View details →

Climate Change Impacts for 14 Tree Species in Southwest Colorado

Forest management traditionally has been based on expectation of a steady climate. In the face of a changing climate, management requires projections of changes in the distribution of the climatic niche of the major species and strategies for applying the projections. We prepared climatic habitat models incorporating heatload as a topographic predictor for the 14 upland tree species of southwestern Colorado, USA, an area that has already seen substantial climate impacts. Models were trained with over 800,000 points of known presence and absence. Using 11 climate scenarios for the decade around 2060, we classified and mapped change for each species. Projected impacts are extensive. Except for the low-elevation woodland species, persistent habitat is rare. Most habitat is lost or threatened and is poorly compensated by emergent habitat. Three species may be locally extirpated. Nevertheless, strategies are described that can use the projections to apply management where it is likely to be most effective, to facilitate or assist migration, to favor species likely to be suited in the future, and to identify potential climate refugia.

openCC0Aug 2021View details →

CSM01 Seasonal Summary of Numbers of Small Mammals on 14 LTER Traplines in Prairie Habitats at Konza Prairie (Reformatted to the ecocomDP Design Pattern)

This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-knz/88/8. The abstract below was extracted from the Level 0 data package and is included for context: Data set contains seasonal summaries (spring and autumn) of the number of individuals of each species of small mammal captured (relative abundance) on each grassland trapline. Each record contains year, season, trapline and number of individuals captured of each species. These live trap records are based on daily captures during two 4-day trapping periods in spring (late February to early April) and autumn (early October to mid-November) for each of 14 permanent traplines established on seven fire-grazing treatments (two traplines per treatment). These seven fire-grazing treatments include three sites that are grazed by bison (1 unburned, 1 annual burn and 1 4-year burn) and four sites that are not grazed by bison (1 unburned, 1 annual burn and 2 4-year burn).

openCustomJan 2020View details →

Temperature and discharge data for lake NE 14 Outlet near Toolik Lake, Alaska, during the 2009 summer field season.

File contains temperature and discharge data for lake NE 14 Outlet during the 2009 summer field season.