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1. The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)

Author

Poldrack, R, Markiewicz, C, Appelhoff, S, Ashar, Y, Auer, T, Baillet, S, Bansal, S, Beltrachini, L, Benar, C, Bertazzoli, G, Bhogawar, S, Blair, R, Bortoletto, M, Boudreau, M, Brooks, T, Calhoun, V, Castelli, F, Clement, P, Cohen, A, Cohen-Adad, J, D'Ambrosio, S, de Hollander, G, de la Iglesia-Vayá, M, de la Vega, A, Delorme, A, Devinsky, O, Draschkow, D, Duff, E, Dupre, E, Earl, E, Esteban, O, Feingold, F, Flandin, G, Galassi, A, Gallitto, G, Ganz, M, Gau, R, Gholam, J, Ghosh, S, Giacomel, A, Gillman, A, Gleeson, P, Gramfort, A, Guay, S, Guidali, G, Halchenko, Y, Handwerker, D, Hardcastle, N, Herholz, P, Hermes, D, Honey, C, Innis, R, Ioanas, H, Jahn, A, Karakuzu, A, Keator, D, Kiar, G, Kincses, B, Laird, A, Lau, J, Lazari, A, Legarreta, J, Li, A, Li, X, Love, B, Lu, H, Marcantoni, E, Maumet, C, Mazzamuto, G, Meisler, S, Mikkelsen, M, Mutsaerts, H, Nichols, T, Nikolaidis, A, Nilsonne, G, Niso, G, Norgaard, M, Okell, T, Oostenveld, R, Ort, E, Park, P, Pawlik, M, Pernet, C, Pestilli, F, Petr, J, Phillips, C, Poline, J, Pollonini, L, Raamana, P, Ritter, P, Rizzo, G, Robbins, K, Rockhill, A, Rogers, C, Rokem, A, Rorden, C, Routier, A, Saborit-Torres, J, Salo, T, Schirner, M, Smith, R, Spisak, T, Sprenger, J, Swann, N, Szinte, M, Takerkart, S, Thirion, B, Thomas, A, Torabian, S, Varoquaux, G, Voytek, B, Welzel, J, Wilson, M, Yarkoni, T, Gorgolewski, K, Poldrack, Russell A., Markiewicz, Christopher J., Appelhoff, Stefan, Ashar, Yoni K., Auer, Tibor, Baillet, Sylvain, Bansal, Shashank, Beltrachini, Leandro, Benar, Christian G., Bertazzoli, Giacomo, Bhogawar, Suyash, Blair, Ross W., Bortoletto, Marta, Boudreau, Mathieu, Brooks, Teon L., Calhoun, Vince D., Castelli, Filippo Maria, Clement, Patricia, Cohen, Alexander L., Cohen-Adad, Julien, D'Ambrosio, Sasha, de Hollander, Gilles, de la Iglesia-Vayá, María, de la Vega, Alejandro, Delorme, Arnaud, Devinsky, Orrin, Draschkow, Dejan, Duff, Eugene Paul, DuPre, Elizabeth, Earl, Eric, Esteban, Oscar, Feingold, Franklin W., Flandin, Guillaume, Galassi, Anthony, Gallitto, Giuseppe, Ganz, Melanie, Gau, Rémi, Gholam, James, Ghosh, Satrajit S., Giacomel, Alessio, Gillman, Ashley G., Gleeson, Padraig, Gramfort, Alexandre, Guay, Samuel, Guidali, Giacomo, Halchenko, Yaroslav O., Handwerker, Daniel A., Hardcastle, Nell, Herholz, Peer, Hermes, Dora, Honey, Christopher J., Innis, Robert B., Ioanas, Horea-Ioan, Jahn, Andrew, Karakuzu, Agah, Keator, David B., Kiar, Gregory, Kincses, Balint, Laird, Angela R., Lau, Jonathan C., Lazari, Alberto, Legarreta, Jon Haitz, Li, Adam, Li, Xiangrui, Love, Bradley C., Lu, Hanzhang, Marcantoni, Eleonora, Maumet, Camille, Mazzamuto, Giacomo, Meisler, Steven L., Mikkelsen, Mark, Mutsaerts, Henk, Nichols, Thomas E., Nikolaidis, Aki, Nilsonne, Gustav, Niso, Guiomar, Norgaard, Martin, Okell, Thomas W., Oostenveld, Robert, Ort, Eduard, Park, Patrick J., Pawlik, Mateusz, Pernet, Cyril R., Pestilli, Franco, Petr, Jan, Phillips, Christophe, Poline, Jean-Baptiste, Pollonini, Luca, Raamana, Pradeep Reddy, Ritter, Petra, Rizzo, Gaia, Robbins, Kay A., Rockhill, Alexander P., Rogers, Christine, Rokem, Ariel, Rorden, Chris, Routier, Alexandre, Saborit-Torres, Jose Manuel, Salo, Taylor, Schirner, Michael, Smith, Robert E., Spisak, Tamas, Sprenger, Julia, Swann, Nicole C., Szinte, Martin, Takerkart, Sylvain, Thirion, Bertrand, Thomas, Adam G., Torabian, Sajjad, Varoquaux, Gael, Voytek, Bradley, Welzel, Julius, Wilson, Martin, Yarkoni, Tal, Gorgolewski, Krzysztof J., Poldrack, R, Markiewicz, C, Appelhoff, S, Ashar, Y, Auer, T, Baillet, S, Bansal, S, Beltrachini, L, Benar, C, Bertazzoli, G, Bhogawar, S, Blair, R, Bortoletto, M, Boudreau, M, Brooks, T, Calhoun, V, Castelli, F, Clement, P, Cohen, A, Cohen-Adad, J, D'Ambrosio, S, de Hollander, G, de la Iglesia-Vayá, M, de la Vega, A, Delorme, A, Devinsky, O, Draschkow, D, Duff, E, Dupre, E, Earl, E, Esteban, O, Feingold, F, Flandin, G, Galassi, A, Gallitto, G, Ganz, M, Gau, R, Gholam, J, Ghosh, S, Giacomel, A, Gillman, A, Gleeson, P, Gramfort, A, Guay, S, Guidali, G, Halchenko, Y, Handwerker, D, Hardcastle, N, Herholz, P, Hermes, D, Honey, C, Innis, R, Ioanas, H, Jahn, A, Karakuzu, A, Keator, D, Kiar, G, Kincses, B, Laird, A, Lau, J, Lazari, A, Legarreta, J, Li, A, Li, X, Love, B, Lu, H, Marcantoni, E, Maumet, C, Mazzamuto, G, Meisler, S, Mikkelsen, M, Mutsaerts, H, Nichols, T, Nikolaidis, A, Nilsonne, G, Niso, G, Norgaard, M, Okell, T, Oostenveld, R, Ort, E, Park, P, Pawlik, M, Pernet, C, Pestilli, F, Petr, J, Phillips, C, Poline, J, Pollonini, L, Raamana, P, Ritter, P, Rizzo, G, Robbins, K, Rockhill, A, Rogers, C, Rokem, A, Rorden, C, Routier, A, Saborit-Torres, J, Salo, T, Schirner, M, Smith, R, Spisak, T, Sprenger, J, Swann, N, Szinte, M, Takerkart, S, Thirion, B, Thomas, A, Torabian, S, Varoquaux, G, Voytek, B, Welzel, J, Wilson, M, Yarkoni, T, Gorgolewski, K, Poldrack, Russell A., Markiewicz, Christopher J., Appelhoff, Stefan, Ashar, Yoni K., Auer, Tibor, Baillet, Sylvain, Bansal, Shashank, Beltrachini, Leandro, Benar, Christian G., Bertazzoli, Giacomo, Bhogawar, Suyash, Blair, Ross W., Bortoletto, Marta, Boudreau, Mathieu, Brooks, Teon L., Calhoun, Vince D., Castelli, Filippo Maria, Clement, Patricia, Cohen, Alexander L., Cohen-Adad, Julien, D'Ambrosio, Sasha, de Hollander, Gilles, de la Iglesia-Vayá, María, de la Vega, Alejandro, Delorme, Arnaud, Devinsky, Orrin, Draschkow, Dejan, Duff, Eugene Paul, DuPre, Elizabeth, Earl, Eric, Esteban, Oscar, Feingold, Franklin W., Flandin, Guillaume, Galassi, Anthony, Gallitto, Giuseppe, Ganz, Melanie, Gau, Rémi, Gholam, James, Ghosh, Satrajit S., Giacomel, Alessio, Gillman, Ashley G., Gleeson, Padraig, Gramfort, Alexandre, Guay, Samuel, Guidali, Giacomo, Halchenko, Yaroslav O., Handwerker, Daniel A., Hardcastle, Nell, Herholz, Peer, Hermes, Dora, Honey, Christopher J., Innis, Robert B., Ioanas, Horea-Ioan, Jahn, Andrew, Karakuzu, Agah, Keator, David B., Kiar, Gregory, Kincses, Balint, Laird, Angela R., Lau, Jonathan C., Lazari, Alberto, Legarreta, Jon Haitz, Li, Adam, Li, Xiangrui, Love, Bradley C., Lu, Hanzhang, Marcantoni, Eleonora, Maumet, Camille, Mazzamuto, Giacomo, Meisler, Steven L., Mikkelsen, Mark, Mutsaerts, Henk, Nichols, Thomas E., Nikolaidis, Aki, Nilsonne, Gustav, Niso, Guiomar, Norgaard, Martin, Okell, Thomas W., Oostenveld, Robert, Ort, Eduard, Park, Patrick J., Pawlik, Mateusz, Pernet, Cyril R., Pestilli, Franco, Petr, Jan, Phillips, Christophe, Poline, Jean-Baptiste, Pollonini, Luca, Raamana, Pradeep Reddy, Ritter, Petra, Rizzo, Gaia, Robbins, Kay A., Rockhill, Alexander P., Rogers, Christine, Rokem, Ariel, Rorden, Chris, Routier, Alexandre, Saborit-Torres, Jose Manuel, Salo, Taylor, Schirner, Michael, Smith, Robert E., Spisak, Tamas, Sprenger, Julia, Swann, Nicole C., Szinte, Martin, Takerkart, Sylvain, Thirion, Bertrand, Thomas, Adam G., Torabian, Sajjad, Varoquaux, Gael, Voytek, Bradley, Welzel, Julius, Wilson, Martin, Yarkoni, Tal, and Gorgolewski, Krzysztof J.

Abstract

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

Published
2024

2. The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)

Author

Poldrack, R. A., Markiewicz, C. J., Appelhoff, S., Ashar, Y. K., Auer, T., Baillet, S., Bansal, S., Beltrachini, L., Bertazzoli, G., Bhogawar, S., Blair, R. W., Bortoletto, M., Boudreau, M., Brooks, T. L., Bénar, C. G., Calhoun, V. D., Castelli, F. M., Clement, P., Cohen, A. L., Cohen-Adad, J., Dambrosio, S., Delorme, A., Devinsky, O., Draschkow, D., Duff, E. P., Dupre, E., Earl, E., Esteban, O., Feingold, F. W., Flandin, G., Galassi, A., Gallitto, G., Ganz, M., Gholam, J., Ghosh, S. S., Giacomel, A., Gillman, A. G., Gleeson, P., Gramfort, A., Guay, S., Guidali, G., Halchenko, Y. O., Handwerker, D. A., Hardcastle, N., Herholz, P., Hermes, D., Honey, C. J., Innis, R. B., Ioanas, H.-I., Jahn, A., Karakuzu, A., Keator, D. B., Kiar, G., Kincses, B., Laird, A. R., Lau, J. C., Lazari, A., Legarreta, J. H., Li, A., Li, X., Love, B. C., Lu, H., Maumet, C., Mazzamuto, G., Meisler, S. L., Mikkelsen, M., Mutsaerts, H., Nichols, T. E., Nikolaidis, A., Nilsonne, G., Niso, G., Norgaard, M., Okell, T. W., Oostenveld, R., Ort, E., Park, P. J., Pawlik, M., Pernet, C. R., Pestilli, F., (0000-0002-3201-6002) Petr, J., Phillips, C., Poline, J.-B., Pollonini, L., Raamana, P. R., Ritter, P., Rizzo, G., Robbins, K. A., Rockhill, A. P., Rogers, C., Rokem, A., Rorden, C., Routier, A., Saborit-Torres, J. M., Salo, T., Schirner, M., Smith, R. E., Spisak, T., Sprenger, J., Swann, N. C., Szinte, M., Takerkart, S., Thirion, B., Thomas, A. G., Torabian, S., Varoquaux, G., Vaya, M. D. L. I., Voytek, B., Welzel, J., Wilson, M., Hollander, G., Vega, A., Gorgolewski, K. J., Poldrack, R. A., Markiewicz, C. J., Appelhoff, S., Ashar, Y. K., Auer, T., Baillet, S., Bansal, S., Beltrachini, L., Bertazzoli, G., Bhogawar, S., Blair, R. W., Bortoletto, M., Boudreau, M., Brooks, T. L., Bénar, C. G., Calhoun, V. D., Castelli, F. M., Clement, P., Cohen, A. L., Cohen-Adad, J., Dambrosio, S., Delorme, A., Devinsky, O., Draschkow, D., Duff, E. P., Dupre, E., Earl, E., Esteban, O., Feingold, F. W., Flandin, G., Galassi, A., Gallitto, G., Ganz, M., Gholam, J., Ghosh, S. S., Giacomel, A., Gillman, A. G., Gleeson, P., Gramfort, A., Guay, S., Guidali, G., Halchenko, Y. O., Handwerker, D. A., Hardcastle, N., Herholz, P., Hermes, D., Honey, C. J., Innis, R. B., Ioanas, H.-I., Jahn, A., Karakuzu, A., Keator, D. B., Kiar, G., Kincses, B., Laird, A. R., Lau, J. C., Lazari, A., Legarreta, J. H., Li, A., Li, X., Love, B. C., Lu, H., Maumet, C., Mazzamuto, G., Meisler, S. L., Mikkelsen, M., Mutsaerts, H., Nichols, T. E., Nikolaidis, A., Nilsonne, G., Niso, G., Norgaard, M., Okell, T. W., Oostenveld, R., Ort, E., Park, P. J., Pawlik, M., Pernet, C. R., Pestilli, F., (0000-0002-3201-6002) Petr, J., Phillips, C., Poline, J.-B., Pollonini, L., Raamana, P. R., Ritter, P., Rizzo, G., Robbins, K. A., Rockhill, A. P., Rogers, C., Rokem, A., Rorden, C., Routier, A., Saborit-Torres, J. M., Salo, T., Schirner, M., Smith, R. E., Spisak, T., Sprenger, J., Swann, N. C., Szinte, M., Takerkart, S., Thirion, B., Thomas, A. G., Torabian, S., Varoquaux, G., Vaya, M. D. L. I., Voytek, B., Welzel, J., Wilson, M., Hollander, G., Vega, A., and Gorgolewski, K. J.

Abstract

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, and the mechanisms by which it has been extended. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

Published
2024

3. Centering inclusivity in the design of online conferences-An OHBM-Open Science perspective

Author

Levitis, E, van Praag, CDG, Gau, R, Heunis, S, DuPre, E, Kiar, G, Bottenhorn, KL, Glatard, T, Nikolaidis, A, Whitaker, KJ, Mancini, M, Niso, G, Afyouni, S, Alonso-Ortiz, E, Appelhoff, S, Arnatkeviciute, A, Atay, SM, Auer, T, Baracchini, G, Bayer, JMM, Beauvais, MJS, Bijsterbosch, JD, Bilgin, IP, Bollmann, S, Botvinik-Nezer, R, Bright, MG, Calhoun, VD, Chen, X, Chopra, S, Hu, C-P, Close, TG, Cookson, SL, Craddock, RC, De la Vega, A, De Leener, B, Demeter, D, Di Maio, P, Dickie, EW, Eickhoff, SB, Esteban, O, Finc, K, Frigo, M, Ganesan, S, Ganz, M, Garner, KG, Garza-Villarreal, EA, Gonzalez-Escamilla, G, Goswami, R, Griffiths, JD, Grootswagers, T, Guay, S, Guest, O, Handwerker, DA, Herholz, P, Heuer, K, Huijser, DC, Iacovella, V, Joseph, MJE, Karakuzu, A, Keator, DB, Kobeleva, X, Kumar, M, Laird, AR, Larson-Prior, LJ, Lautarescu, A, Lazari, A, Legarreta, JH, Li, X-Y, Lv, J, Mansour, SL, Meunier, D, Moraczewski, D, Nandi, T, Nastase, SA, Nau, M, Noble, S, Norgaard, M, Obungoloch, J, Oostenveld, R, Orchard, ER, Poldrack, RA, Qiu, A, Raamana, PR, Rokem, A, Rutherford, S, Sharan, M, Shaw, TB, Syeda, WT, Testerman, MM, Toro, R, Valk, SL, Van den Bossche, S, Varoquaux, G, Vasa, F, Veldsman, M, Vohryzek, J, Wagner, AS, Walsh, RJ, White, T, Wong, F-T, Xie, X, Yan, C-G, Yang, Y-F, Yee, Y, Zanitti, GE, Van Gulick, AE, Duff, E, Maumet, C, Levitis, E, van Praag, CDG, Gau, R, Heunis, S, DuPre, E, Kiar, G, Bottenhorn, KL, Glatard, T, Nikolaidis, A, Whitaker, KJ, Mancini, M, Niso, G, Afyouni, S, Alonso-Ortiz, E, Appelhoff, S, Arnatkeviciute, A, Atay, SM, Auer, T, Baracchini, G, Bayer, JMM, Beauvais, MJS, Bijsterbosch, JD, Bilgin, IP, Bollmann, S, Botvinik-Nezer, R, Bright, MG, Calhoun, VD, Chen, X, Chopra, S, Hu, C-P, Close, TG, Cookson, SL, Craddock, RC, De la Vega, A, De Leener, B, Demeter, D, Di Maio, P, Dickie, EW, Eickhoff, SB, Esteban, O, Finc, K, Frigo, M, Ganesan, S, Ganz, M, Garner, KG, Garza-Villarreal, EA, Gonzalez-Escamilla, G, Goswami, R, Griffiths, JD, Grootswagers, T, Guay, S, Guest, O, Handwerker, DA, Herholz, P, Heuer, K, Huijser, DC, Iacovella, V, Joseph, MJE, Karakuzu, A, Keator, DB, Kobeleva, X, Kumar, M, Laird, AR, Larson-Prior, LJ, Lautarescu, A, Lazari, A, Legarreta, JH, Li, X-Y, Lv, J, Mansour, SL, Meunier, D, Moraczewski, D, Nandi, T, Nastase, SA, Nau, M, Noble, S, Norgaard, M, Obungoloch, J, Oostenveld, R, Orchard, ER, Poldrack, RA, Qiu, A, Raamana, PR, Rokem, A, Rutherford, S, Sharan, M, Shaw, TB, Syeda, WT, Testerman, MM, Toro, R, Valk, SL, Van den Bossche, S, Varoquaux, G, Vasa, F, Veldsman, M, Vohryzek, J, Wagner, AS, Walsh, RJ, White, T, Wong, F-T, Xie, X, Yan, C-G, Yang, Y-F, Yee, Y, Zanitti, GE, Van Gulick, AE, Duff, E, and Maumet, C

Abstract

As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.

Published
2021

6. Optimizing the conservation of migratory species over their full annual cycle

Author

Schuster, R. (Richard), Wilson, S. (Scott), Rodewald, A.D. (Amanda D.), Arcese, P. (Peter), Fink, D. (Daniel), Auer, T. (Tom), Bennett, J.R. (Joseph R.), Schuster, R. (Richard), Wilson, S. (Scott), Rodewald, A.D. (Amanda D.), Arcese, P. (Peter), Fink, D. (Daniel), Auer, T. (Tom), and Bennett, J.R. (Joseph R.)

Abstract

Limited knowledge of the distribution, abundance, and habitat associations of migratory species hinders effective conservation actions. We use Neotropical migratory birds as a model group to compare approaches to prioritize land conservation needed to support ≥30% of the global abundances of 117 species. Specifically, we compare scenarios from spatial optimization models to achieve conservation targets by: 1) area requirements for conserving >30% abundance of each species for each week of the year independently vs. combined; 2) including vs. ignoring spatial clustering of species abundance; and 3) incorporating vs. avoiding human-dominated landscapes. Solutions integrating information across the year require 56% less area than those integrating weekly abundances, with additional reductions when shared-use landscapes are included. Although incorporating spatial population structure requires more area, geographical representation among priority sites improves substantially. These findings illustrate that globally-sourced citizen science data can elucidate key trade-offs among opportunity costs and spatiotemporal representation of conservation efforts.

Published
2019
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8. 25 Years of cooling research in office buildings: Review for the integration of cooling strategies into the building façade (1990–2014)

Author

Prieto Hoces, A.I. (author), Knaack, U. (author), Klein, T. (author), Auer, T (author), Prieto Hoces, A.I. (author), Knaack, U. (author), Klein, T. (author), and Auer, T (author)

Abstract

This paper seeks to present a panorama of cooling related research in office buildings, categorising reported research experiences from the past 25 years in order to identify knowledge gaps and define current paths and trends for further exploration. The general goal behind this research is to support the design of sustainable office buildings in warm climates through examination of past experiences, thus the paper focuses on strategies at building level and specially related with façade design. Peer reviewed journal articles were selected as the source for the study, given the reliability of the information published under peer-review processes. Several queries were carried out throughout three online journal article databases (Web of Science, SCOPUS and ScienceDirect), considering published papers from 1990 onwards. The resulting article database was then explored trough descriptive analysis and in-depth review of some articles to expand on specific topics in order to thoroughly visualise scientific interest and tendencies within the field of study for the last 25 years. As results of the review it is possible to state the high current relevance of cooling research, having experienced an increase of publications under different climate contexts and varied topics ranging from passive to solar cooling, which is seen as a research field on its own. Also, in terms of research methods, software simulations seem to be the primary tool for cooling research, which makes sense for performance driven developments. On the other hand, the main knowledge gaps identified are the need for specific research regarding possibilities for application and architectural integration of cooling systems; the lack of articles addressing some specific cooling strategies, such as the use of evaporative and ground cooling; and the need for more information about the operation of cooling systems, especially taking users’ perception and their behaviour into account., Design of Constrution

Published
2017
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9. BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

Author

Schneidman, D, Gorgolewski, KJ, Alfaro-Almagro, F, Auer, T, Bellec, P, Capota, M, Chakravarty, MM, Churchill, NW, Cohen, AL, Craddock, RC, Devenyi, GA, Eklund, A, Esteban, O, Flandin, G, Ghosh, SS, Guntupalli, JS, Jenkinson, M, Keshavan, A, Kiar, G, Liem, F, Raamana, PR, Raffelt, D, Steele, CJ, Quirion, P-O, Smith, RE, Strother, SC, Varoquaux, G, Wang, Y, Yarkoni, T, Poldrack, RA, Schneidman, D, Gorgolewski, KJ, Alfaro-Almagro, F, Auer, T, Bellec, P, Capota, M, Chakravarty, MM, Churchill, NW, Cohen, AL, Craddock, RC, Devenyi, GA, Eklund, A, Esteban, O, Flandin, G, Ghosh, SS, Guntupalli, JS, Jenkinson, M, Keshavan, A, Kiar, G, Liem, F, Raamana, PR, Raffelt, D, Steele, CJ, Quirion, P-O, Smith, RE, Strother, SC, Varoquaux, G, Wang, Y, Yarkoni, T, and Poldrack, RA

Abstract

The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.

Published
2017

11. 25 Years of cooling research in office buildings: Review for the integration of cooling strategies into the building façade (1990–2014)

Author

Prieto Hoces, A.I. (author), Knaack, U. (author), Klein, T. (author), Auer, T (author), Prieto Hoces, A.I. (author), Knaack, U. (author), Klein, T. (author), and Auer, T (author)

Abstract

This paper seeks to present a panorama of cooling related research in office buildings, categorising reported research experiences from the past 25 years in order to identify knowledge gaps and define current paths and trends for further exploration. The general goal behind this research is to support the design of sustainable office buildings in warm climates through examination of past experiences, thus the paper focuses on strategies at building level and specially related with façade design. Peer reviewed journal articles were selected as the source for the study, given the reliability of the information published under peer-review processes. Several queries were carried out throughout three online journal article databases (Web of Science, SCOPUS and ScienceDirect), considering published papers from 1990 onwards. The resulting article database was then explored trough descriptive analysis and in-depth review of some articles to expand on specific topics in order to thoroughly visualise scientific interest and tendencies within the field of study for the last 25 years. As results of the review it is possible to state the high current relevance of cooling research, having experienced an increase of publications under different climate contexts and varied topics ranging from passive to solar cooling, which is seen as a research field on its own. Also, in terms of research methods, software simulations seem to be the primary tool for cooling research, which makes sense for performance driven developments. On the other hand, the main knowledge gaps identified are the need for specific research regarding possibilities for application and architectural integration of cooling systems; the lack of articles addressing some specific cooling strategies, such as the use of evaporative and ground cooling; and the need for more information about the operation of cooling systems, especially taking users’ perception and their behaviour into account., Design of Constrution

Published
2017
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12. 'Birds in the Clouds': Adventures in Data Engineering

Author

Cherel, N., Reesman, J., Sahuguet, A., Auer, T., Fink, D., Cherel, N., Reesman, J., Sahuguet, A., Auer, T., and Fink, D.

Abstract

Leveraging their eBird crowdsourcing project, the Cornell Lab of Ornithology generates sophisticated Spatio-Temporal Exploratory Model (STEM) maps of bird migrations. Such maps are highly relevant for both scientific and educational purposes, but creating them requires advanced modeling techniques that rely on long and potentially expensive computations. In this paper, we share our experience porting the eBird STEM data pipeline from a physical cluster to the cloud, providing a seamless deployment at a lower cost. Using open source tools and cloud "marketplaces", we managed to divide the operating costs by a factor of 6, making it possible to scale our pipeline on a research budget., Comment: Presented at the Data For Good Exchange 2017

Published
2017

13. Sharing brain mapping statistical results with the neuroimaging data model.

Author

Maumet, C, Auer, T, Bowring, A, Chen, G, Das, S, Flandin, G, Ghosh, S, Glatard, T, Gorgolewski, KJ, Helmer, KG, Jenkinson, M, Keator, DB, Nichols, BN, Poline, J-B, Reynolds, R, Sochat, V, Turner, J, Nichols, TE, Maumet, C, Auer, T, Bowring, A, Chen, G, Das, S, Flandin, G, Ghosh, S, Glatard, T, Gorgolewski, KJ, Helmer, KG, Jenkinson, M, Keator, DB, Nichols, BN, Poline, J-B, Reynolds, R, Sochat, V, Turner, J, and Nichols, TE

Abstract

Only a tiny fraction of the data and metadata produced by an fMRI study is finally conveyed to the community. This lack of transparency not only hinders the reproducibility of neuroimaging results but also impairs future meta-analyses. In this work we introduce NIDM-Results, a format specification providing a machine-readable description of neuroimaging statistical results along with key image data summarising the experiment. NIDM-Results provides a unified representation of mass univariate analyses including a level of detail consistent with available best practices. This standardized representation allows authors to relay methods and results in a platform-independent regularized format that is not tied to a particular neuroimaging software package. Tools are available to export NIDM-Result graphs and associated files from the widely used SPM and FSL software packages, and the NeuroVault repository can import NIDM-Results archives. The specification is publically available at: http://nidm.nidash.org/specs/nidm-results.html.

Published
2016

20. Prediction model to estimate presence of coronary artery disease: Retrospective pooled analysis of existing cohorts

Author

Genders, T.S.S. (Tessa), Steyerberg, E.W. (Ewout), Hunink, M.G.M. (Myriam), Nieman, K. (Koen), Galema, T.W. (Tjebbe), Mollet, N.R.A. (Nico), Feyter, P.J. (Pim) de, Krestin, G.P. (Gabriel), Alkadhi, H. (Hatem), Leschka, S. (Sebastian), Desbiolles, L. (Lotus), Meijs, M.F.L. (Matthijs), Cramer, M.-J. (Maarten-Jan), Knuuti, J. (Juhani), Kajander, S. (Sami), Bogaert, J. (Jan), Goetschalckx, K. (Kaatje), Cademartiri, F. (Filippo), Maffei, E. (Erica), Martini, C. (Chiara), Seitun, S. (Sara), Aldrovandi, A. (Annachiara), Wildermuth, S. (Simon), Stinn, B. (Björn), Fornaro, J. (Jürgen), Feuchtner, G.M. (Gudrun), Zordo, T. (Tobias) de, Auer, T. (Thomas), Plank, F. (Fabian), Friedrich, G. (Guy), Pugliese, F. (Francesca), Petersen, S.E. (Steffen), Davies, L.C. (L. Ceri), Schoepf, U.J. (Joseph), Rowe, G.W. (Garrett W.), Mieghem, C.A.G. (Carlos) van, Driessche, L. (Luc) van, Sinitsyn, V. (Valentin), Gopalan, D. (Deepa), Nikolaou, K. (Konstantin), Bamberg, F. (Fabian), Cury, R.C. (Ricardo), Battle, J. (Juan), Maurovich-Horvat, P. (Pal), Bartykowszki, A. (Andrea), Merkely, B. (Bela), Becker, D. (Dávid), Hadamitzky, M. (Martin), Hausleiter, J. (Jörg), Dewey, T.M. (Todd M.), Zimmermann, E. (Elke), Laule, M. (Michael), Genders, T.S.S. (Tessa), Steyerberg, E.W. (Ewout), Hunink, M.G.M. (Myriam), Nieman, K. (Koen), Galema, T.W. (Tjebbe), Mollet, N.R.A. (Nico), Feyter, P.J. (Pim) de, Krestin, G.P. (Gabriel), Alkadhi, H. (Hatem), Leschka, S. (Sebastian), Desbiolles, L. (Lotus), Meijs, M.F.L. (Matthijs), Cramer, M.-J. (Maarten-Jan), Knuuti, J. (Juhani), Kajander, S. (Sami), Bogaert, J. (Jan), Goetschalckx, K. (Kaatje), Cademartiri, F. (Filippo), Maffei, E. (Erica), Martini, C. (Chiara), Seitun, S. (Sara), Aldrovandi, A. (Annachiara), Wildermuth, S. (Simon), Stinn, B. (Björn), Fornaro, J. (Jürgen), Feuchtner, G.M. (Gudrun), Zordo, T. (Tobias) de, Auer, T. (Thomas), Plank, F. (Fabian), Friedrich, G. (Guy), Pugliese, F. (Francesca), Petersen, S.E. (Steffen), Davies, L.C. (L. Ceri), Schoepf, U.J. (Joseph), Rowe, G.W. (Garrett W.), Mieghem, C.A.G. (Carlos) van, Driessche, L. (Luc) van, Sinitsyn, V. (Valentin), Gopalan, D. (Deepa), Nikolaou, K. (Konstantin), Bamberg, F. (Fabian), Cury, R.C. (Ricardo), Battle, J. (Juan), Maurovich-Horvat, P. (Pal), Bartykowszki, A. (Andrea), Merkely, B. (Bela), Becker, D. (Dávid), Hadamitzky, M. (Martin), Hausleiter, J. (Jörg), Dewey, T.M. (Todd M.), Zimmermann, E. (Elke), and Laule, M. (Michael)

Published
2012
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21. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts

Author

Genders, Tessa, Steyerberg, Ewout, Nieman, Koen, Galema, Tjebbe, Mollet, Nico, Feijter, Pim, Krestin, Gabriel, Alkadhi, H, Leschka, S, Desbiolles, L, Meijs, MFL, Cramer, MJ, Knuuti, J, Kajander, S, Bogaert, J, Goetschalckx, K, Cademartiri, F., Maffei, E, Martini, C, Seitun, S, Aldrovandi, A, Wildermuth, S, Stinn, B, Fornaro, J, Feuchtner, G, De Zordo, T, Auer, T, Plank, F, Friedrich, G, Pugliese, F, Petersen, SE, Davies, LC, Schoepf, UJ, Rowe, GW, van Mieghem, CAG, van Driessche, L, Sinitsyn, V, Gopalan, D (Deepa), Nikolaou, K, Bamberg, F, Cury, RC, Battle, J, Maurovich-Horvat, P, Bartykowszki, A, Merkely, B, Becker, D, Hadamitzky, M, Hausleiter, J, Dewey, M, Zimmermann, E, Laule, M, Hunink, Myriam, Genders, Tessa, Steyerberg, Ewout, Nieman, Koen, Galema, Tjebbe, Mollet, Nico, Feijter, Pim, Krestin, Gabriel, Alkadhi, H, Leschka, S, Desbiolles, L, Meijs, MFL, Cramer, MJ, Knuuti, J, Kajander, S, Bogaert, J, Goetschalckx, K, Cademartiri, F., Maffei, E, Martini, C, Seitun, S, Aldrovandi, A, Wildermuth, S, Stinn, B, Fornaro, J, Feuchtner, G, De Zordo, T, Auer, T, Plank, F, Friedrich, G, Pugliese, F, Petersen, SE, Davies, LC, Schoepf, UJ, Rowe, GW, van Mieghem, CAG, van Driessche, L, Sinitsyn, V, Gopalan, D (Deepa), Nikolaou, K, Bamberg, F, Cury, RC, Battle, J, Maurovich-Horvat, P, Bartykowszki, A, Merkely, B, Becker, D, Hadamitzky, M, Hausleiter, J, Dewey, M, Zimmermann, E, Laule, M, and Hunink, Myriam

Abstract

Objectives To develop prediction models that better estimate the pretest probability of coronary artery disease in low prevalence populations. Design Retrospective pooled analysis of individual patient data. Setting 18 hospitals in Europe and the United States. Participants Patients with stable chest pain without evidence for previous coronary artery disease, if they were referred for computed tomography (CT) based coronary angiography or catheter based coronary angiography (indicated as low and high prevalence settings, respectively). Main outcome measures Obstructive coronary artery disease (>= 50% diameter stenosis in at least one vessel found on catheter based coronary angiography). Multiple imputation accounted for missing predictors and outcomes, exploiting strong correlation between the two angiography procedures. Predictive models included a basic model (age, sex, symptoms, and setting), clinical model (basic model factors and diabetes, hypertension, dyslipidaemia, and smoking), and extended model (clinical model facto Results We included 5677 patients (3283 men, 2394 women), of whom 1634 had obstructive coronary artery disease found on catheter based coronary angiography. All potential predictors were significantly associated with the presence of disease in univariable and multivariable analyses. The clinical model improved the prediction, compared with the basic model (cross validated c statistic improvement from 0.77 to 0.79, net reclassification improvement 35%); the coronary calcium score in the extended Conclusions Updated prediction models including age, sex, symptoms, and cardiovascular risk factors allow for accurate estimation of the pretest probability of coronary artery disease in low prevalence populations. Addition of coronary calcium scores to the prediction models improves the estimates.

Published
2012

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