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Next generation sequencing data of a defined microbial mock community.

Scientific data

Singer E, Andreopoulos B, Bowers RM, Lee J, Deshpande S, Chiniquy J, Ciobanu D, Klenk HP, Zane M, Daum C, Clum A, Cheng JF, Copeland A, Woyke T.
PMID: 27673566
Sci Data. 2016 Sep 27;3:160081. doi: 10.1038/sdata.2016.81.

Generating sequence data of a defined community composed of organisms with complete reference genomes is indispensable for the benchmarking of new genome sequence analysis methods, including assembly and binning tools. Moreover the validation of new sequencing library protocols and...

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Singer E, Andreopoulos B, Bowers RM, et al. Next generation sequencing data of a defined microbial mock community. Sci Data. 2016;3:160081doi: 10.1038/sdata.2016.81.
Singer, E., Andreopoulos, B., Bowers, R. M., Lee, J., Deshpande, S., Chiniquy, J., Ciobanu, D., Klenk, H. P., Zane, M., Daum, C., Clum, A., Cheng, J. F., Copeland, A., & Woyke, T. (2016). Next generation sequencing data of a defined microbial mock community. Scientific data, 3160081. https://doi.org/10.1038/sdata.2016.81
Singer, Esther, et al. "Next generation sequencing data of a defined microbial mock community." Scientific data vol. 3 (2016): 160081. doi: https://doi.org/10.1038/sdata.2016.81
Singer E, Andreopoulos B, Bowers RM, Lee J, Deshpande S, Chiniquy J, Ciobanu D, Klenk HP, Zane M, Daum C, Clum A, Cheng JF, Copeland A, Woyke T. Next generation sequencing data of a defined microbial mock community. Sci Data. 2016 Sep 27;3:160081. doi: 10.1038/sdata.2016.81. PMID: 27673566; PMCID: PMC5037974.
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De novo transcriptome assembly databases for the butterfly orchid Phalaenopsis equestris.

Scientific data

Niu SC, Xu Q, Zhang GQ, Zhang YQ, Tsai WC, Hsu JL, Liang CK, Luo YB, Liu ZJ.
PMID: 27673730
Sci Data. 2016 Sep 27;3:160083. doi: 10.1038/sdata.2016.83.

Orchids are renowned for their spectacular flowers and ecological adaptations. After the sequencing of the genome of the tropical epiphytic orchid Phalaenopsis equestris, we combined Illumina HiSeq2000 for RNA-Seq and Trinity for de novo assembly to characterize the transcriptomes...

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Niu SC, Xu Q, Zhang GQ, et al. De novo transcriptome assembly databases for the butterfly orchid Phalaenopsis equestris. Sci Data. 2016;3:160083doi: 10.1038/sdata.2016.83.
Niu, S. C., Xu, Q., Zhang, G. Q., Zhang, Y. Q., Tsai, W. C., Hsu, J. L., Liang, C. K., Luo, Y. B., & Liu, Z. J. (2016). De novo transcriptome assembly databases for the butterfly orchid Phalaenopsis equestris. Scientific data, 3160083. https://doi.org/10.1038/sdata.2016.83
Niu, Shan-Ce, et al. "De novo transcriptome assembly databases for the butterfly orchid Phalaenopsis equestris." Scientific data vol. 3 (2016): 160083. doi: https://doi.org/10.1038/sdata.2016.83
Niu SC, Xu Q, Zhang GQ, Zhang YQ, Tsai WC, Hsu JL, Liang CK, Luo YB, Liu ZJ. De novo transcriptome assembly databases for the butterfly orchid Phalaenopsis equestris. Sci Data. 2016 Sep 27;3:160083. doi: 10.1038/sdata.2016.83. PMID: 27673730; PMCID: PMC5037975.
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A global experimental dataset for assessing grain legume production.

Scientific data

Cernay C, Pelzer E, Makowski D.
PMID: 27676125
Sci Data. 2016 Sep 27;3:160084. doi: 10.1038/sdata.2016.84.

Grain legume crops are a significant component of the human diet and animal feed and have an important role in the environment, but the global diversity of agricultural legume species is currently underexploited. Experimental assessments of grain legume performances...

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Cernay C, Pelzer E, Makowski D. A global experimental dataset for assessing grain legume production. Sci Data. 2016;3:160084doi: 10.1038/sdata.2016.84.
Cernay, C., Pelzer, E., & Makowski, D. (2016). A global experimental dataset for assessing grain legume production. Scientific data, 3160084. https://doi.org/10.1038/sdata.2016.84
Cernay, Charles, et al. "A global experimental dataset for assessing grain legume production." Scientific data vol. 3 (2016): 160084. doi: https://doi.org/10.1038/sdata.2016.84
Cernay C, Pelzer E, Makowski D. A global experimental dataset for assessing grain legume production. Sci Data. 2016 Sep 27;3:160084. doi: 10.1038/sdata.2016.84. PMID: 27676125; PMCID: PMC5037976.
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A database of georeferenced nutrient chemistry data for mountain lakes of the Western United States.

Scientific data

Williams J, Labou SG.
PMID: 28509907
Sci Data. 2017 May 16;4:170069. doi: 10.1038/sdata.2017.69.

Human activities have increased atmospheric nitrogen and phosphorus deposition rates relative to pre-industrial background. In the Western U.S., anthropogenic nutrient deposition has increased nutrient concentrations and stimulated algal growth in at least some remote mountain lakes. The Georeferenced Lake...

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Williams J, Labou SG. A database of georeferenced nutrient chemistry data for mountain lakes of the Western United States. Sci Data. 2017;4:170069doi: 10.1038/sdata.2017.69.
Williams, J., & Labou, S. G. (2017). A database of georeferenced nutrient chemistry data for mountain lakes of the Western United States. Scientific data, 4170069. https://doi.org/10.1038/sdata.2017.69
Williams, Jason, and Labou, Stephanie G. "A database of georeferenced nutrient chemistry data for mountain lakes of the Western United States." Scientific data vol. 4 (2017): 170069. doi: https://doi.org/10.1038/sdata.2017.69
Williams J, Labou SG. A database of georeferenced nutrient chemistry data for mountain lakes of the Western United States. Sci Data. 2017 May 16;4:170069. doi: 10.1038/sdata.2017.69. PMID: 28509907; PMCID: PMC5479644.
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A hybrid organic-inorganic perovskite dataset.

Scientific data

Kim C, Huan TD, Krishnan S, Ramprasad R.
PMID: 28485719
Sci Data. 2017 May 09;4:170057. doi: 10.1038/sdata.2017.57.

Hybrid organic-inorganic perovskites (HOIPs) have been attracting a great deal of attention due to their versatility of electronic properties and fabrication methods. We prepare a dataset of 1,346 HOIPs, which features 16 organic cations, 3 group-IV cations and 4...

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Kim C, Huan TD, Krishnan S, et al. A hybrid organic-inorganic perovskite dataset. Sci Data. 2017;4:170057doi: 10.1038/sdata.2017.57.
Kim, C., Huan, T. D., Krishnan, S., & Ramprasad, R. (2017). A hybrid organic-inorganic perovskite dataset. Scientific data, 4170057. https://doi.org/10.1038/sdata.2017.57
Kim, Chiho, et al. "A hybrid organic-inorganic perovskite dataset." Scientific data vol. 4 (2017): 170057. doi: https://doi.org/10.1038/sdata.2017.57
Kim C, Huan TD, Krishnan S, Ramprasad R. A hybrid organic-inorganic perovskite dataset. Sci Data. 2017 May 09;4:170057. doi: 10.1038/sdata.2017.57. PMID: 28485719; PMCID: PMC5423391.
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Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error.

Journal of chemical theory and computation

Faber FA, Hutchison L, Huang B, Gilmer J, Schoenholz SS, Dahl GE, Vinyals O, Kearnes S, Riley PF, von Lilienfeld OA.
PMID: 28926232
J Chem Theory Comput. 2017 Nov 14;13(11):5255-5264. doi: 10.1021/acs.jctc.7b00577. Epub 2017 Oct 10.

We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of 13 electronic ground-state properties of organic molecules. The performance of each regressor/representation/property combination is assessed using learning curves which...

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Faber FA, Hutchison L, Huang B, et al. Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. J Chem Theory Comput. 2017;13(11):5255-5264doi: 10.1021/acs.jctc.7b00577.
Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A. (2017). Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. Journal of chemical theory and computation, 13(11), 5255-5264. https://doi.org/10.1021/acs.jctc.7b00577
Faber, Felix A, et al. "Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error." Journal of chemical theory and computation vol. 13,11 (2017): 5255-5264. doi: https://doi.org/10.1021/acs.jctc.7b00577
Faber FA, Hutchison L, Huang B, Gilmer J, Schoenholz SS, Dahl GE, Vinyals O, Kearnes S, Riley PF, von Lilienfeld OA. Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. J Chem Theory Comput. 2017 Nov 14;13(11):5255-5264. doi: 10.1021/acs.jctc.7b00577. Epub 2017 Oct 10. PMID: 28926232.
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Long-term data set of small mammals from owl pellets in the Atlantic-Mediterranean transition area.

Scientific data

Escribano N, Galicia D, Ariño AH, Escala C.
PMID: 27676217
Sci Data. 2016 Sep 27;3:160085. doi: 10.1038/sdata.2016.85.

We describe the pellet sampling data set from the Vertebrate Collection of the Museum of Zoology of the University of Navarra. This data set compiles all information about small mammals obtained from the analysis of owl pellets. The collection...

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Escribano N, Galicia D, Ariño AH, et al. Long-term data set of small mammals from owl pellets in the Atlantic-Mediterranean transition area. Sci Data. 2016;3:160085doi: 10.1038/sdata.2016.85.
Escribano, N., Galicia, D., Ariño, A. H., & Escala, C. (2016). Long-term data set of small mammals from owl pellets in the Atlantic-Mediterranean transition area. Scientific data, 3160085. https://doi.org/10.1038/sdata.2016.85
Escribano, Nora, et al. "Long-term data set of small mammals from owl pellets in the Atlantic-Mediterranean transition area." Scientific data vol. 3 (2016): 160085. doi: https://doi.org/10.1038/sdata.2016.85
Escribano N, Galicia D, Ariño AH, Escala C. Long-term data set of small mammals from owl pellets in the Atlantic-Mediterranean transition area. Sci Data. 2016 Sep 27;3:160085. doi: 10.1038/sdata.2016.85. PMID: 27676217; PMCID: PMC5037973.
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The Harvard organic photovoltaic dataset.

Scientific data

Lopez SA, Pyzer-Knapp EO, Simm GN, Lutzow T, Li K, Seress LR, Hachmann J, Aspuru-Guzik A.
PMID: 27676312
Sci Data. 2016 Sep 27;3:160086. doi: 10.1038/sdata.2016.86.

The Harvard Organic Photovoltaic Dataset (HOPV15) presented in this work is a collation of experimental photovoltaic data from the literature, and corresponding quantum-chemical calculations performed over a range of conformers, each with quantum chemical results using a variety of...

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Lopez SA, Pyzer-Knapp EO, Simm GN, et al. The Harvard organic photovoltaic dataset. Sci Data. 2016;3:160086doi: 10.1038/sdata.2016.86.
Lopez, S. A., Pyzer-Knapp, E. O., Simm, G. N., Lutzow, T., Li, K., Seress, L. R., Hachmann, J., & Aspuru-Guzik, A. (2016). The Harvard organic photovoltaic dataset. Scientific data, 3160086. https://doi.org/10.1038/sdata.2016.86
Lopez, Steven A, et al. "The Harvard organic photovoltaic dataset." Scientific data vol. 3 (2016): 160086. doi: https://doi.org/10.1038/sdata.2016.86
Lopez SA, Pyzer-Knapp EO, Simm GN, Lutzow T, Li K, Seress LR, Hachmann J, Aspuru-Guzik A. The Harvard organic photovoltaic dataset. Sci Data. 2016 Sep 27;3:160086. doi: 10.1038/sdata.2016.86. PMID: 27676312; PMCID: PMC5037972.
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The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.

Scientific data

Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, Ghosh SS, Glatard T, Halchenko YO, Handwerker DA, Hanke M, Keator D, Li X, Michael Z, Maumet C, Nichols BN, Nichols TE, Pellman J, Poline JB, Rokem A, Schaefer G, Sochat V, Triplett W, Turner JA, Varoquaux G, Poldrack RA.
PMID: 27326542
Sci Data. 2016 Jun 21;3:160044. doi: 10.1038/sdata.2016.44.

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain....

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Gorgolewski KJ, Auer T, Calhoun VD, et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 2016;3:160044doi: 10.1038/sdata.2016.44.
Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., Handwerker, D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N., Nichols, T. E., Pellman, J., Poline, J. B., Rokem, A., Schaefer, G., Sochat, V., Triplett, W., Turner, J. A., Varoquaux, G., & Poldrack, R. A. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific data, 3160044. https://doi.org/10.1038/sdata.2016.44
Gorgolewski, Krzysztof J, et al. "The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments." Scientific data vol. 3 (2016): 160044. doi: https://doi.org/10.1038/sdata.2016.44
Gorgolewski KJ, Auer T, Calhoun VD, Craddock RC, Das S, Duff EP, Flandin G, Ghosh SS, Glatard T, Halchenko YO, Handwerker DA, Hanke M, Keator D, Li X, Michael Z, Maumet C, Nichols BN, Nichols TE, Pellman J, Poline JB, Rokem A, Schaefer G, Sochat V, Triplett W, Turner JA, Varoquaux G, Poldrack RA. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 2016 Jun 21;3:160044. doi: 10.1038/sdata.2016.44. PMID: 27326542; PMCID: PMC4978148.
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High-resolution data on the impact of warming on soil CO.

Scientific data

Liang N, Teramoto M, Takagi M, Zeng J.
PMID: 28291228
Sci Data. 2017 Mar 14;4:170026. doi: 10.1038/sdata.2017.26.

This paper describes a project for evaluation of global warming's impacts on soil carbon dynamics in Japanese forest ecosystems. We started a soil warming experiment in late 2008 in a 55-year-old evergreen broad-leaved forest at the boundary between the...

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Liang N, Teramoto M, Takagi M, et al. High-resolution data on the impact of warming on soil CO. Sci Data. 2017;4:170026doi: 10.1038/sdata.2017.26.
Liang, N., Teramoto, M., Takagi, M., & Zeng, J. (2017). High-resolution data on the impact of warming on soil CO. Scientific data, 4170026. https://doi.org/10.1038/sdata.2017.26
Liang, Naishen, et al. "High-resolution data on the impact of warming on soil CO." Scientific data vol. 4 (2017): 170026. doi: https://doi.org/10.1038/sdata.2017.26
Liang N, Teramoto M, Takagi M, Zeng J. High-resolution data on the impact of warming on soil CO. Sci Data. 2017 Mar 14;4:170026. doi: 10.1038/sdata.2017.26. PMID: 28291228; PMCID: PMC5386236.
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Unique identifiers for small molecules enable rigorous labeling of their atoms.

Scientific data

Dashti H, Westler WM, Markley JL, Eghbalnia HR.
PMID: 28534867
Sci Data. 2017 May 23;4:170073. doi: 10.1038/sdata.2017.73.

Rigorous characterization of small organic molecules in terms of their structural and biological properties is vital to biomedical research. The three-dimensional structure of a molecule, its 'photo ID', is inefficient for searching and matching tasks. Instead, identifiers play a...

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Dashti H, Westler WM, Markley JL, et al. Unique identifiers for small molecules enable rigorous labeling of their atoms. Sci Data. 2017;4:170073doi: 10.1038/sdata.2017.73.
Dashti, H., Westler, W. M., Markley, J. L., & Eghbalnia, H. R. (2017). Unique identifiers for small molecules enable rigorous labeling of their atoms. Scientific data, 4170073. https://doi.org/10.1038/sdata.2017.73
Dashti, Hesam, et al. "Unique identifiers for small molecules enable rigorous labeling of their atoms." Scientific data vol. 4 (2017): 170073. doi: https://doi.org/10.1038/sdata.2017.73
Dashti H, Westler WM, Markley JL, Eghbalnia HR. Unique identifiers for small molecules enable rigorous labeling of their atoms. Sci Data. 2017 May 23;4:170073. doi: 10.1038/sdata.2017.73. PMID: 28534867; PMCID: PMC5441290.
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Global terrestrial Human Footprint maps for 1993 and 2009.

Scientific data

Venter O, Sanderson EW, Magrach A, Allan JR, Beher J, Jones KR, Possingham HP, Laurance WF, Wood P, Fekete BM, Levy MA, Watson JE.
PMID: 27552448
Sci Data. 2016 Aug 23;3:160067. doi: 10.1038/sdata.2016.67.

Remotely-sensed and bottom-up survey information were compiled on eight variables measuring the direct and indirect human pressures on the environment globally in 1993 and 2009. This represents not only the most current information of its type, but also the...

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Venter O, Sanderson EW, Magrach A, et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci Data. 2016;3:160067doi: 10.1038/sdata.2016.67.
Venter, O., Sanderson, E. W., Magrach, A., Allan, J. R., Beher, J., Jones, K. R., Possingham, H. P., Laurance, W. F., Wood, P., Fekete, B. M., Levy, M. A., & Watson, J. E. (2016). Global terrestrial Human Footprint maps for 1993 and 2009. Scientific data, 3160067. https://doi.org/10.1038/sdata.2016.67
Venter, Oscar, et al. "Global terrestrial Human Footprint maps for 1993 and 2009." Scientific data vol. 3 (2016): 160067. doi: https://doi.org/10.1038/sdata.2016.67
Venter O, Sanderson EW, Magrach A, Allan JR, Beher J, Jones KR, Possingham HP, Laurance WF, Wood P, Fekete BM, Levy MA, Watson JE. Global terrestrial Human Footprint maps for 1993 and 2009. Sci Data. 2016 Aug 23;3:160067. doi: 10.1038/sdata.2016.67. PMID: 27552448; PMCID: PMC5127486.
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Showing 1 to 12 of 880 entries