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Corrigendum: High-throughput RNAi screen for essential genes and drug synergistic combinations in colorectal cancer.

Scientific data

Williams SP, Barthorpe AS, Lightfoot H, Garnett MJ, McDermott U.
PMID: 30299440
Sci Data. 2018 Oct 09;5:180215. doi: 10.1038/sdata.2018.215.

This corrects the article DOI: 10.1038/sdata.2017.139.

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Williams SP, Barthorpe AS, Lightfoot H, et al. Corrigendum: High-throughput RNAi screen for essential genes and drug synergistic combinations in colorectal cancer. Sci Data. 2018;5:180215doi: 10.1038/sdata.2018.215.
Williams, S. P., Barthorpe, A. S., Lightfoot, H., Garnett, M. J., & McDermott, U. (2018). Corrigendum: High-throughput RNAi screen for essential genes and drug synergistic combinations in colorectal cancer. Scientific data, 5180215. https://doi.org/10.1038/sdata.2018.215
Williams, Steven P, et al. "Corrigendum: High-throughput RNAi screen for essential genes and drug synergistic combinations in colorectal cancer." Scientific data vol. 5 (2018): 180215. doi: https://doi.org/10.1038/sdata.2018.215
Williams SP, Barthorpe AS, Lightfoot H, Garnett MJ, McDermott U. Corrigendum: High-throughput RNAi screen for essential genes and drug synergistic combinations in colorectal cancer. Sci Data. 2018 Oct 09;5:180215. doi: 10.1038/sdata.2018.215. PMID: 30299440; PMCID: PMC6176782.
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The electronic Rothamsted Archive (e-RA), an online resource for data from the Rothamsted long-term experiments.

Scientific data

Perryman SAM, Castells-Brooke NID, Glendining MJ, Goulding KWT, Hawkesford MJ, Macdonald AJ, Ostler RJ, Poulton PR, Rawlings CJ, Scott T, Verrier PJ.
PMID: 29762552
Sci Data. 2018 May 15;5:180072. doi: 10.1038/sdata.2018.72.

The electronic Rothamsted Archive, e-RA (www.era.rothamsted.ac.uk) provides a permanent managed database to both securely store and disseminate data from Rothamsted Research's long-term field experiments (since 1843) and meteorological stations (since 1853). Both historical and contemporary data are made available...

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Perryman SAM, Castells-Brooke NID, Glendining MJ, et al. The electronic Rothamsted Archive (e-RA), an online resource for data from the Rothamsted long-term experiments. Sci Data. 2018;5:180072doi: 10.1038/sdata.2018.72.
Perryman, S. A. M., Castells-Brooke, N. I. D., Glendining, M. J., Goulding, K. W. T., Hawkesford, M. J., Macdonald, A. J., Ostler, R. J., Poulton, P. R., Rawlings, C. J., Scott, T., & Verrier, P. J. (2018). The electronic Rothamsted Archive (e-RA), an online resource for data from the Rothamsted long-term experiments. Scientific data, 5180072. https://doi.org/10.1038/sdata.2018.72
Perryman, Sarah A M, et al. "The electronic Rothamsted Archive (e-RA), an online resource for data from the Rothamsted long-term experiments." Scientific data vol. 5 (2018): 180072. doi: https://doi.org/10.1038/sdata.2018.72
Perryman SAM, Castells-Brooke NID, Glendining MJ, Goulding KWT, Hawkesford MJ, Macdonald AJ, Ostler RJ, Poulton PR, Rawlings CJ, Scott T, Verrier PJ. The electronic Rothamsted Archive (e-RA), an online resource for data from the Rothamsted long-term experiments. Sci Data. 2018 May 15;5:180072. doi: 10.1038/sdata.2018.72. PMID: 29762552; PMCID: PMC5952867.
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A global dataset of air temperature derived from satellite remote sensing and weather stations.

Scientific data

Hooker J, Duveiller G, Cescatti A.
PMID: 30398475
Sci Data. 2018 Nov 06;5:180246. doi: 10.1038/sdata.2018.246.

Air temperature at 2 m above the land surface is a key variable used to assess climate change. However, observations of air temperature are typically only available from a limited number of weather stations distributed mainly in developed countries,...

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Hooker J, Duveiller G, Cescatti A. A global dataset of air temperature derived from satellite remote sensing and weather stations. Sci Data. 2018;5:180246doi: 10.1038/sdata.2018.246.
Hooker, J., Duveiller, G., & Cescatti, A. (2018). A global dataset of air temperature derived from satellite remote sensing and weather stations. Scientific data, 5180246. https://doi.org/10.1038/sdata.2018.246
Hooker, Josh, et al. "A global dataset of air temperature derived from satellite remote sensing and weather stations." Scientific data vol. 5 (2018): 180246. doi: https://doi.org/10.1038/sdata.2018.246
Hooker J, Duveiller G, Cescatti A. A global dataset of air temperature derived from satellite remote sensing and weather stations. Sci Data. 2018 Nov 06;5:180246. doi: 10.1038/sdata.2018.246. PMID: 30398475; PMCID: PMC6219417.
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Fluctuations in anthropogenic nighttime lights from satellite imagery for five cities in Niger and Nigeria.

Scientific data

Bharti N, Tatem AJ.
PMID: 30422123
Sci Data. 2018 Nov 13;5:180256. doi: 10.1038/sdata.2018.256.

Dynamic measures of human populations are critical for global health management but are often overlooked, largely because they are difficult to quantify. Measuring human population dynamics can be prohibitively expensive in under-resourced communities. Satellite imagery can provide measurements of...

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Bharti N, Tatem AJ. Fluctuations in anthropogenic nighttime lights from satellite imagery for five cities in Niger and Nigeria. Sci Data. 2018;5:180256doi: 10.1038/sdata.2018.256.
Bharti, N., & Tatem, A. J. (2018). Fluctuations in anthropogenic nighttime lights from satellite imagery for five cities in Niger and Nigeria. Scientific data, 5180256. https://doi.org/10.1038/sdata.2018.256
Bharti, Nita, and Tatem, Andrew J. "Fluctuations in anthropogenic nighttime lights from satellite imagery for five cities in Niger and Nigeria." Scientific data vol. 5 (2018): 180256. doi: https://doi.org/10.1038/sdata.2018.256
Bharti N, Tatem AJ. Fluctuations in anthropogenic nighttime lights from satellite imagery for five cities in Niger and Nigeria. Sci Data. 2018 Nov 13;5:180256. doi: 10.1038/sdata.2018.256. PMID: 30422123; PMCID: PMC6233255.
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Collected mass spectrometry data on monoterpene indole alkaloids from natural product chemistry research.

Scientific data

Fox Ramos AE, Le Pogam P, Fox Alcover C, Otogo N'Nang E, Cauchie G, Hazni H, Awang K, Bréard D, Echavarren AM, Frédérich M, Gaslonde T, Girardot M, Grougnet R, Kirillova MS, Kritsanida M, Lémus C, Le Ray AM, Lewin G, Litaudon M, Mambu L, Michel S, Miloserdov FM, Muratore ME, Richomme-Peniguel P, Roussi F, Evanno L, Poupon E, Champy P, Beniddir MA.
PMID: 30944327
Sci Data. 2019 Apr 03;6(1):15. doi: 10.1038/s41597-019-0028-3.

This Data Descriptor announces the submission to public repositories of the monoterpene indole alkaloid database (MIADB), a cumulative collection of 172 tandem mass spectrometry (MS/MS) spectra from multiple research projects conducted in eight natural product chemistry laboratories since the...

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Fox Ramos AE, Le Pogam P, Fox Alcover C, et al. Collected mass spectrometry data on monoterpene indole alkaloids from natural product chemistry research. Sci Data. 2019;6(1):15doi: 10.1038/s41597-019-0028-3.
Fox Ramos, A. E., Le Pogam, P., Fox Alcover, C., Otogo N'Nang, E., Cauchie, G., Hazni, H., Awang, K., Bréard, D., Echavarren, A. M., Frédérich, M., Gaslonde, T., Girardot, M., Grougnet, R., Kirillova, M. S., Kritsanida, M., Lémus, C., Le Ray, A. M., Lewin, G., Litaudon, M., Mambu, L., Michel, S., Miloserdov, F. M., Muratore, M. E., Richomme-Peniguel, P., Roussi, F., Evanno, L., Poupon, E., Champy, P., & Beniddir, M. A. (2019). Collected mass spectrometry data on monoterpene indole alkaloids from natural product chemistry research. Scientific data, 6(1), 15. https://doi.org/10.1038/s41597-019-0028-3
Fox Ramos, Alexander E, et al. "Collected mass spectrometry data on monoterpene indole alkaloids from natural product chemistry research." Scientific data vol. 6,1 (2019): 15. doi: https://doi.org/10.1038/s41597-019-0028-3
Fox Ramos AE, Le Pogam P, Fox Alcover C, Otogo N'Nang E, Cauchie G, Hazni H, Awang K, Bréard D, Echavarren AM, Frédérich M, Gaslonde T, Girardot M, Grougnet R, Kirillova MS, Kritsanida M, Lémus C, Le Ray AM, Lewin G, Litaudon M, Mambu L, Michel S, Miloserdov FM, Muratore ME, Richomme-Peniguel P, Roussi F, Evanno L, Poupon E, Champy P, Beniddir MA. Collected mass spectrometry data on monoterpene indole alkaloids from natural product chemistry research. Sci Data. 2019 Apr 03;6(1):15. doi: 10.1038/s41597-019-0028-3. PMID: 30944327; PMCID: PMC6480975.
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A 204-subject multimodal neuroimaging dataset to study language processing.

Scientific data

Schoffelen JM, Oostenveld R, Lam NHL, Uddén J, Hultén A, Hagoort P.
PMID: 30944338
Sci Data. 2019 Apr 03;6(1):17. doi: 10.1038/s41597-019-0020-y.

This dataset, colloquially known as the Mother Of Unification Studies (MOUS) dataset, contains multimodal neuroimaging data that has been acquired from 204 healthy human subjects. The neuroimaging protocol consisted of magnetic resonance imaging (MRI) to derive information at high...

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Schoffelen JM, Oostenveld R, Lam NHL, et al. A 204-subject multimodal neuroimaging dataset to study language processing. Sci Data. 2019;6(1):17doi: 10.1038/s41597-019-0020-y.
Schoffelen, J. M., Oostenveld, R., Lam, N. H. L., Uddén, J., Hultén, A., & Hagoort, P. (2019). A 204-subject multimodal neuroimaging dataset to study language processing. Scientific data, 6(1), 17. https://doi.org/10.1038/s41597-019-0020-y
Schoffelen, Jan-Mathijs, et al. "A 204-subject multimodal neuroimaging dataset to study language processing." Scientific data vol. 6,1 (2019): 17. doi: https://doi.org/10.1038/s41597-019-0020-y
Schoffelen JM, Oostenveld R, Lam NHL, Uddén J, Hultén A, Hagoort P. A 204-subject multimodal neuroimaging dataset to study language processing. Sci Data. 2019 Apr 03;6(1):17. doi: 10.1038/s41597-019-0020-y. PMID: 30944338; PMCID: PMC6472396.
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City-scale car traffic and parking density maps from Uber Movement travel time data.

Scientific data

Aryandoust A, van Vliet O, Patt A.
PMID: 31434904
Sci Data. 2019 Aug 21;6(1):158. doi: 10.1038/s41597-019-0159-6.

Car parking is of central importance to congestion on roads and the urban planning process of optimizing road networks, pricing parking lots and planning land use. The efficient placement, sizing and grid connection of charging stations for electric cars...

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Aryandoust A, van Vliet O, Patt A. City-scale car traffic and parking density maps from Uber Movement travel time data. Sci Data. 2019;6(1):158doi: 10.1038/s41597-019-0159-6.
Aryandoust, A., van Vliet, O., & Patt, A. (2019). City-scale car traffic and parking density maps from Uber Movement travel time data. Scientific data, 6(1), 158. https://doi.org/10.1038/s41597-019-0159-6
Aryandoust, Arsam, et al. "City-scale car traffic and parking density maps from Uber Movement travel time data." Scientific data vol. 6,1 (2019): 158. doi: https://doi.org/10.1038/s41597-019-0159-6
Aryandoust A, van Vliet O, Patt A. City-scale car traffic and parking density maps from Uber Movement travel time data. Sci Data. 2019 Aug 21;6(1):158. doi: 10.1038/s41597-019-0159-6. PMID: 31434904; PMCID: PMC6704179.
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Author Correction: A data set of inland lake catchment boundaries for the Qiangtang Plateau.

Scientific data

Yan D, Li M, Bi W, Weng B, Qin T, Wang J, Do P.
PMID: 31492873
Sci Data. 2019 Sep 06;6(1):171. doi: 10.1038/s41597-019-0176-5.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Yan D, Li M, Bi W, et al. Author Correction: A data set of inland lake catchment boundaries for the Qiangtang Plateau. Sci Data. 2019;6(1):171doi: 10.1038/s41597-019-0176-5.
Yan, D., Li, M., Bi, W., Weng, B., Qin, T., Wang, J., & Do, P. (2019). Author Correction: A data set of inland lake catchment boundaries for the Qiangtang Plateau. Scientific data, 6(1), 171. https://doi.org/10.1038/s41597-019-0176-5
Yan, Denghua, et al. "Author Correction: A data set of inland lake catchment boundaries for the Qiangtang Plateau." Scientific data vol. 6,1 (2019): 171. doi: https://doi.org/10.1038/s41597-019-0176-5
Yan D, Li M, Bi W, Weng B, Qin T, Wang J, Do P. Author Correction: A data set of inland lake catchment boundaries for the Qiangtang Plateau. Sci Data. 2019 Sep 06;6(1):171. doi: 10.1038/s41597-019-0176-5. PMID: 31492873; PMCID: PMC6731235.
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PubMed Phrases, an open set of coherent phrases for searching biomedical literature.

Scientific data

Kim S, Yeganova L, Comeau DC, Wilbur WJ, Lu Z.
PMID: 29893755
Sci Data. 2018 Jun 12;5:180104. doi: 10.1038/sdata.2018.104.

In biomedicine, key concepts are often expressed by multiple words (e.g., 'zinc finger protein'). Previous work has shown treating a sequence of words as a meaningful unit, where applicable, is not only important for human understanding but also beneficial...

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Kim S, Yeganova L, Comeau DC, et al. PubMed Phrases, an open set of coherent phrases for searching biomedical literature. Sci Data. 2018;5:180104doi: 10.1038/sdata.2018.104.
Kim, S., Yeganova, L., Comeau, D. C., Wilbur, W. J., & Lu, Z. (2018). PubMed Phrases, an open set of coherent phrases for searching biomedical literature. Scientific data, 5180104. https://doi.org/10.1038/sdata.2018.104
Kim, Sun, et al. "PubMed Phrases, an open set of coherent phrases for searching biomedical literature." Scientific data vol. 5 (2018): 180104. doi: https://doi.org/10.1038/sdata.2018.104
Kim S, Yeganova L, Comeau DC, Wilbur WJ, Lu Z. PubMed Phrases, an open set of coherent phrases for searching biomedical literature. Sci Data. 2018 Jun 12;5:180104. doi: 10.1038/sdata.2018.104. PMID: 29893755; PMCID: PMC5996850.
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SEEG initiative estimates of Brazilian greenhouse gas emissions from 1970 to 2015.

Scientific data

de Azevedo TR, Costa Junior C, Brandão Junior A, Cremer MDS, Piatto M, Tsai DS, Barreto P, Martins H, Sales M, Galuchi T, Rodrigues A, Morgado R, Ferreira AL, Barcellos E Silva F, Viscondi GF, Dos Santos KC, Cunha KBD, Manetti A, Coluna IME, Albuquerque IR, Junior SW, Leite C, Kishinami R.
PMID: 29809176
Sci Data. 2018 May 29;5:180045. doi: 10.1038/sdata.2018.45.

This work presents the SEEG platform, a 46-year long dataset of greenhouse gas emissions (GHG) in Brazil (1970-2015) providing more than 2 million data records for the Agriculture, Energy, Industry, Waste and Land Use Change Sectors at national and...

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de Azevedo TR, Costa Junior C, Brandão Junior A, et al. SEEG initiative estimates of Brazilian greenhouse gas emissions from 1970 to 2015. Sci Data. 2018;5:180045doi: 10.1038/sdata.2018.45.
de Azevedo, T. R., Costa Junior, C., Brandão Junior, A., Cremer, M. D. S., Piatto, M., Tsai, D. S., Barreto, P., Martins, H., Sales, M., Galuchi, T., Rodrigues, A., Morgado, R., Ferreira, A. L., Barcellos E Silva, F., Viscondi, G. F., Dos Santos, K. C., Cunha, K. B. D., Manetti, A., Coluna, I. M. E., Albuquerque, I. R., Junior, S. W., Leite, C., & Kishinami, R. (2018). SEEG initiative estimates of Brazilian greenhouse gas emissions from 1970 to 2015. Scientific data, 5180045. https://doi.org/10.1038/sdata.2018.45
de Azevedo, Tasso Rezende, et al. "SEEG initiative estimates of Brazilian greenhouse gas emissions from 1970 to 2015." Scientific data vol. 5 (2018): 180045. doi: https://doi.org/10.1038/sdata.2018.45
de Azevedo TR, Costa Junior C, Brandão Junior A, Cremer MDS, Piatto M, Tsai DS, Barreto P, Martins H, Sales M, Galuchi T, Rodrigues A, Morgado R, Ferreira AL, Barcellos E Silva F, Viscondi GF, Dos Santos KC, Cunha KBD, Manetti A, Coluna IME, Albuquerque IR, Junior SW, Leite C, Kishinami R. SEEG initiative estimates of Brazilian greenhouse gas emissions from 1970 to 2015. Sci Data. 2018 May 29;5:180045. doi: 10.1038/sdata.2018.45. PMID: 29809176; PMCID: PMC5972695.
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Spinal cord injury epidemiological profile in the Sarah Network of Rehabilitation Hospitals-a Brazilian population sample.

Spinal cord series and cases

Barbetta DC, Smanioto TR, Poletto MF, Ferreira R, Lopes A, Casaro FM, Camargo A, Ogashawara TO.
PMID: 29644101
Spinal Cord Ser Cases. 2018 Apr 04;4:32. doi: 10.1038/s41394-018-0049-8. eCollection 2018.

STUDY DESIGN: Retrospective cross-sectional study.OBJECTIVE: To describe the epidemiological profile of individuals with spinal cord injury (SCI), who visited the Sarah Network in 2014.SETTING: Brazil, Brasília-Federal District.METHODS: A review of the electronic medical records of persons with SCI, aged...

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Barbetta DC, Smanioto TR, Poletto MF, et al. Spinal cord injury epidemiological profile in the Sarah Network of Rehabilitation Hospitals-a Brazilian population sample. Spinal Cord Ser Cases. 2018;4:32doi: 10.1038/s41394-018-0049-8.
Barbetta, D. C., Smanioto, T. R., Poletto, M. F., Ferreira, R., Lopes, A., Casaro, F. M., Camargo, A., & Ogashawara, T. O. (2018). Spinal cord injury epidemiological profile in the Sarah Network of Rehabilitation Hospitals-a Brazilian population sample. Spinal cord series and cases, 432. https://doi.org/10.1038/s41394-018-0049-8
Barbetta, D C, et al. "Spinal cord injury epidemiological profile in the Sarah Network of Rehabilitation Hospitals-a Brazilian population sample." Spinal cord series and cases vol. 4 (2018): 32. doi: https://doi.org/10.1038/s41394-018-0049-8
Barbetta DC, Smanioto TR, Poletto MF, Ferreira R, Lopes A, Casaro FM, Camargo A, Ogashawara TO. Spinal cord injury epidemiological profile in the Sarah Network of Rehabilitation Hospitals-a Brazilian population sample. Spinal Cord Ser Cases. 2018 Apr 04;4:32. doi: 10.1038/s41394-018-0049-8. eCollection 2018. PMID: 29644101; PMCID: PMC5884855.
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Seafloor crustal deformation data along the subduction zones around Japan obtained by GNSS-A observations.

Scientific data

Yokota Y, Ishikawa T, Watanabe SI.
PMID: 30204155
Sci Data. 2018 Sep 11;5:180182. doi: 10.1038/sdata.2018.182.

Crustal deformation data obtained by geodetic observation networks are foundations in the fields of geodesy and seismology. These data are essential for understanding plate motion and earthquake sources and for simulating earthquake and tsunami scenarios. Although relatively scarce, seafloor...

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Yokota Y, Ishikawa T, Watanabe SI. Seafloor crustal deformation data along the subduction zones around Japan obtained by GNSS-A observations. Sci Data. 2018;5:180182doi: 10.1038/sdata.2018.182.
Yokota, Y., Ishikawa, T., & Watanabe, S. I. (2018). Seafloor crustal deformation data along the subduction zones around Japan obtained by GNSS-A observations. Scientific data, 5180182. https://doi.org/10.1038/sdata.2018.182
Yokota, Yusuke, et al. "Seafloor crustal deformation data along the subduction zones around Japan obtained by GNSS-A observations." Scientific data vol. 5 (2018): 180182. doi: https://doi.org/10.1038/sdata.2018.182
Yokota Y, Ishikawa T, Watanabe SI. Seafloor crustal deformation data along the subduction zones around Japan obtained by GNSS-A observations. Sci Data. 2018 Sep 11;5:180182. doi: 10.1038/sdata.2018.182. PMID: 30204155; PMCID: PMC6132190.
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Showing 25 to 36 of 880 entries