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34 results for “10-K reports”
Figure 8 from: Borremans C, Durden J, Schoening T, Curtis EJ, Adams L, Branzan Albu A, Arnaubec A, Ayata S-D, Baburaj R, Bassin C, Beck M, Bigham KT, Boschen-Rose RE, Collett C, Contini M, Correa PVF, Dominguez-Carrió C, Dreyfus G, Duncan G, Ferrera M, Foulon V, Friedman A, Gaikwad S, Game C, Gaytán-Caballero A, Girard F, Giusti M, Hanafi-Portier M, Howell K, Hulevata I, Itiowe K, Jackett C, Jansen J, Karthäuser C, Katija K, Kernec M, Kim G, Kitahara M, Langenkämper D, Langlois T, Lanteri N, Jianping Li C, Li Q-R, Liabot P-O, Lindsay D, Loulidi A, Marcon Y, Marini S, Marranzino A, Massot-Campos M, Matabos M, Menot L, Moreno B, Morrissey M, Nakath D, Nattkemper T, Neufeld M, Obst M, Olu K, Parimbelli A, Pasotti F, Pelletier D, Perhirin M, Piechaud N, Pizarro O, Purser A, Rodrigues CF, Ceballos Romero E, Schlining B, Song Y, Sosik HM, Sourisseau M, Taormina B, Taucher J, Thornton B, Van Audenhaege L, von der Meden C, Wacquet G, Williams J, Witting K, Zurowietz M (2024) Report on the Marine Imaging Workshop 2022. Research Ideas and Outcomes 10: e119782. https://doi.org/10.3897/rio.10.e119782
Figure 8 Polling results of discussion session 3 "Automation", question 2: "Is AI for underwater images fundamentally different to existing "in-air" methods?".
Supplementary material 4 from: Nilsson RH, Taylor AFS, Adams RI, Baschien C, Bengtsson-Palme J, Cangren P, Coleine C, Daniel H-M, Glassman SI, Hirooka Y, Irinyi L, Iršėnaitė R, Martin-Sanchez PM, Meyer W, Oh S-Y, Sampaio JP, Seifert KA, Sklenář F, Stubbe D, Suh S-O, Summerbell R, Svantesson S, Unterseher M, Visagie CM, Weiss M, Woudenberg JHC, Wurzbacher C, den Wyngaert SV, Yilmaz N, Yurkov A, Kõljalg U, Abarenkov K (2018) Taxonomic annotation of public fungal ITS sequences from the built environment – a report from an April 10–11, 2017 workshop (Aberdeen, UK). MycoKeys 28: 65-82. https://doi.org/10.3897/mycokeys.28.20887
The interactive Krona chart associated with Figure 2 :
Supplementary material 2 from: Nilsson RH, Taylor AFS, Adams RI, Baschien C, Bengtsson-Palme J, Cangren P, Coleine C, Daniel H-M, Glassman SI, Hirooka Y, Irinyi L, Iršėnaitė R, Martin-Sanchez PM, Meyer W, Oh S-Y, Sampaio JP, Seifert KA, Sklenář F, Stubbe D, Suh S-O, Summerbell R, Svantesson S, Unterseher M, Visagie CM, Weiss M, Woudenberg JHC, Wurzbacher C, den Wyngaert SV, Yilmaz N, Yurkov A, Kõljalg U, Abarenkov K (2018) Taxonomic annotation of public fungal ITS sequences from the built environment – a report from an April 10–11, 2017 workshop (Aberdeen, UK). MycoKeys 28: 65-82. https://doi.org/10.3897/mycokeys.28.20887
The MIxS-BE annotations implemented for the built environment sequences during the workshop :
Figure 3 from: Nilsson RH, Taylor AFS, Adams RI, Baschien C, Bengtsson-Palme J, Cangren P, Coleine C, Daniel H-M, Glassman SI, Hirooka Y, Irinyi L, Iršėnaitė R, Martin-Sanchez PM, Meyer W, Oh S-Y, Sampaio JP, Seifert KA, Sklenář F, Stubbe D, Suh S-O, Summerbell R, Svantesson S, Unterseher M, Visagie CM, Weiss M, Woudenberg JHC, Wurzbacher C, den Wyngaert SV, Yilmaz N, Yurkov A, Kõljalg U, Abarenkov K (2018) Taxonomic annotation of public fungal ITS sequences from the built environment – a report from an April 10–11, 2017 workshop (Aberdeen, UK). MycoKeys 28: 65-82. https://doi.org/10.3897/mycokeys.28.20887
Figure 3 Analysis of the MIxS-BE "building occupancy type" (type of building where the underlying sample was taken). The figure is based on Abarenkov et al. (2016) plus the data added during the workshop, such that it indicates the scientific state of ITS-based Sanger-derived sequencing of the built mycobiome as of spring 2017.
Figure 2 from: Nilsson RH, Taylor AFS, Adams RI, Baschien C, Bengtsson-Palme J, Cangren P, Coleine C, Daniel H-M, Glassman SI, Hirooka Y, Irinyi L, Iršėnaitė R, Martin-Sanchez PM, Meyer W, Oh S-Y, Sampaio JP, Seifert KA, Sklenář F, Stubbe D, Suh S-O, Summerbell R, Svantesson S, Unterseher M, Visagie CM, Weiss M, Woudenberg JHC, Wurzbacher C, den Wyngaert SV, Yilmaz N, Yurkov A, Kõljalg U, Abarenkov K (2018) Taxonomic annotation of public fungal ITS sequences from the built environment – a report from an April 10–11, 2017 workshop (Aberdeen, UK). MycoKeys 28: 65-82. https://doi.org/10.3897/mycokeys.28.20887
Figure 2 Krona chart of the taxonomic affiliation of the built environment sequences down to order level. The Krona chart lists all annotated built environment sequences except those classified as Fungi sp. (32%) and those of non-fungal origin (1%). An interactive version of the Krona chart is provided as Supplementary material 4. The figure is based on Abarenkov et al. (2016) plus the data added during the workshop, such that it indicates the scientific state of ITS-based Sanger-derived sequencing of the built mycobiome as of spring 2017.
Figure 1 from: Nilsson RH, Taylor AFS, Adams RI, Baschien C, Bengtsson-Palme J, Cangren P, Coleine C, Daniel H-M, Glassman SI, Hirooka Y, Irinyi L, Iršėnaitė R, Martin-Sanchez PM, Meyer W, Oh S-Y, Sampaio JP, Seifert KA, Sklenář F, Stubbe D, Suh S-O, Summerbell R, Svantesson S, Unterseher M, Visagie CM, Weiss M, Woudenberg JHC, Wurzbacher C, den Wyngaert SV, Yilmaz N, Yurkov A, Kõljalg U, Abarenkov K (2018) Taxonomic annotation of public fungal ITS sequences from the built environment – a report from an April 10–11, 2017 workshop (Aberdeen, UK). MycoKeys 28: 65-82. https://doi.org/10.3897/mycokeys.28.20887
Figure 1 Analysis of the built environment sequences for country of collection. Country centroids based on the geographical centres of contiguous country land masses are marked with bubbles of different size on the global map to indicate the number of built environment sequences originating from these countries as stated explicitly in the underlying INSDC records or as restored during the present effort and in Abarenkov et al. (2016) (57 distinct countries, sequence count ranging from 1 to 3,091). The figure is based on Abarenkov et al. (2016) plus the data added during the workshop, such that it indicates the scientific state of ITS-based Sanger-derived sequencing of the built mycobiome as of spring 2017.
Figure 6 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 6 Simulating inflation, between 2024 and 2040 – Basic number: 2% inflation per year.
Figure 8 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 8 Simulation of inflation, Model B.
Figure 7 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 7 Simulation of inflation, Model A.
Figure 9 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 9 Simulation of inflation, Model C.
Figure 1 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 1 DiSSCo timeline.
Figure 11 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 11 DiSSCo national contribution models.
Figure 2 from: Landel S, Lymer G, Pasterk M, Guiraud M, Worley K (2024) A report on recommendations for the most suitable financial contribution model for the Distributed System of Scientific Collections Research Infrastructure (DiSSCo-RI). Research Ideas and Outcomes 10: e117217. https://doi.org/10.3897/rio.10.e117217
Figure 2 DiSSCo general membership fee calculation model.
Data Set from Holbein CE, Peugh J, Veldtman GR, Apers S, Luyckx K, Kovacs AH, Thomet C, Budts W, Enomoto J, Sluman MA, Lu CW, Jackson JL, Khairy P, Cook SC, Chidambarathanu S, Alday L, Eriksen K, Dellborg M, Berghammer M, Johansson B, Mackie AS, Menahem S, Caruana M, Soufi A, Fernandes SM, White K, Callus E, Kutty S, Moons P; APPROACH-IS consortium and the International Society for Adult Congenital Heart Disease (ISACHD). Health behaviours reported by adults with congenital heart disease across 15 countries. Eur J Prev Cardiol. 2020 Jul;27(10):1077-1087. doi: 10.1177/2047487319876231. Epub 2019 Sep 17. PMID: 31529991.
<p>Data Set from Holbein CE, Peugh J, Veldtman GR, Apers S, Luyckx K, Kovacs AH, Thomet C, Budts W, Enomoto J, Sluman MA, Lu CW, Jackson JL, Khairy P, Cook SC, Chidambarathanu S, Alday L, Eriksen K, Dellborg M, Berghammer M, Johansson B, Mackie AS, Menahem S, Caruana M, Soufi A, Fernandes SM, White K, Callus E, Kutty S, Moons P; APPROACH-IS consortium and the International Society for Adult Congenital Heart Disease (ISACHD). Health behaviours reported by adults with congenital heart disease across 15 countries. Eur J Prev Cardiol. 2020 Jul;27(10):1077-1087. doi: 10.1177/2047487319876231. Epub 2019 Sep 17. PMID: 31529991.</p> <p> </p> <p>This is the abstract:</p> <p><strong>Background: </strong>Health behaviours are essential to maintain optimal health and reduce the risk of cardiovascular complications in adults with congenital heart disease. This study aimed to describe health behaviours in adults with congenital heart disease in 15 countries and to identify patient characteristics associated with optimal health behaviours in the international sample.</p> <p><strong>Design: </strong>This was a cross-sectional observational study.</p> <p><strong>Methods: </strong>Adults with congenital heart disease (<em>n</em> = 4028, median age = 32 years, interquartile range 25-42 years) completed self-report measures as part of the Assessment of Patterns of Patient-Reported Outcomes in Adults with Congenital Heart disease - International Study (APPROACH-IS). Participants reported on seven health behaviours using the Health Behaviors Scale-Congenital Heart Disease. Demographic and medical characteristics were assessed via medical chart review and self-report. Multivariate path analyses with inverse sampling weights were used to investigate study aims.</p> <p><strong>Results: </strong>Health behaviour rates for the full sample were 10% binge drinking, 12% cigarette smoking, 6% recreational drug use, 72% annual dental visit, 69% twice daily tooth brushing, 27% daily dental flossing and 43% sport participation. Pairwise comparisons indicated that rates differed between countries. Rates of substance use behaviours were higher in younger, male participants. Optimal dental health behaviours were more common among older, female participants with higher educational attainment while sports participation was more frequent among participants who were younger, male, married, employed/students, with higher educational attainment, less complex anatomical defects and better functional status.</p> <p><strong>Conclusions: </strong>Health behaviour rates vary by country. Predictors of health behaviours may reflect larger geographic trends. Our findings have implications for the development and implementation of programmes for the assessment and promotion of optimal health behaviours in adults with congenital heart disease.</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.