Advanced Search
Display options
Filter resources
Text Availability
Article type
Publication date
Species
Language
Sex
Age
Showing 1369 to 1376 of 1376 entries
Sorted by: Best Match Show Resources per page
Filters applied: Dataset
309 metagenome assembled microbial genomes from deep sediment samples in the Gulfs of Kathiawar Peninsula.

Scientific data

Nathani NM, Dave KJ, Vatsa PP, Mahajan MS, Sharma P, Mootapally C.
PMID: 34321485
Sci Data. 2021 Jul 28;8(1):194. doi: 10.1038/s41597-021-00957-0.

Prokaryoplankton genomes from the deep marine sediments are less explored compared to shallow shore sediments. The Gulfs of Kathiawar peninsula experience varied currents and inputs from different on-shore activities. Any perturbations would directly influence the microbiome and their normal...

Cite
Nathani NM, Dave KJ, Vatsa PP, et al. 309 metagenome assembled microbial genomes from deep sediment samples in the Gulfs of Kathiawar Peninsula. Sci Data. 2021;8(1):194doi: 10.1038/s41597-021-00957-0.
Nathani, N. M., Dave, K. J., Vatsa, P. P., Mahajan, M. S., Sharma, P., & Mootapally, C. (2021). 309 metagenome assembled microbial genomes from deep sediment samples in the Gulfs of Kathiawar Peninsula. Scientific data, 8(1), 194. https://doi.org/10.1038/s41597-021-00957-0
Nathani, Neelam M, et al. "309 metagenome assembled microbial genomes from deep sediment samples in the Gulfs of Kathiawar Peninsula." Scientific data vol. 8,1 (2021): 194. doi: https://doi.org/10.1038/s41597-021-00957-0
Nathani NM, Dave KJ, Vatsa PP, Mahajan MS, Sharma P, Mootapally C. 309 metagenome assembled microbial genomes from deep sediment samples in the Gulfs of Kathiawar Peninsula. Sci Data. 2021 Jul 28;8(1):194. doi: 10.1038/s41597-021-00957-0. PMID: 34321485; PMCID: PMC8319310.
Copy Download .nbib Format: NLM
An updated map of Trypanosoma cruzi histone post-translational modifications.

Scientific data

de Almeida RF, Fernandes M, de Godoy LMF.
PMID: 33767201
Sci Data. 2021 Mar 25;8(1):93. doi: 10.1038/s41597-021-00818-w.

In humans and other eukaryotes, histone post-translational modifications (hPTMs) play an essential role in the epigenetic control of gene expression. In trypanosomatid parasites, conversely, gene regulation occurs mainly at the post-transcriptional level. However, our group has recently shown that...

Cite
de Almeida RF, Fernandes M, de Godoy LMF. An updated map of Trypanosoma cruzi histone post-translational modifications. Sci Data. 2021;8(1):93doi: 10.1038/s41597-021-00818-w.
de Almeida, R. F., Fernandes, M., & de Godoy, L. M. F. (2021). An updated map of Trypanosoma cruzi histone post-translational modifications. Scientific data, 8(1), 93. https://doi.org/10.1038/s41597-021-00818-w
de Almeida, Rafael Fogaça, et al. "An updated map of Trypanosoma cruzi histone post-translational modifications." Scientific data vol. 8,1 (2021): 93. doi: https://doi.org/10.1038/s41597-021-00818-w
de Almeida RF, Fernandes M, de Godoy LMF. An updated map of Trypanosoma cruzi histone post-translational modifications. Sci Data. 2021 Mar 25;8(1):93. doi: 10.1038/s41597-021-00818-w. PMID: 33767201; PMCID: PMC7994815.
Copy Download .nbib Format: NLM
AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19.

Scientific data

Suryanarayanan P, Tsou CH, Poddar A, Mahajan D, Dandala B, Madan P, Agrawal A, Wachira C, Samuel OM, Bar-Shira O, Kipchirchir C, Okwako S, Ogallo W, Otieno F, Nyota T, Matu F, Barros VR, Shats D, Kagan O, Remy S, Bent O, Guhan P, Mahatma S, Walcott-Bryant A, Pathak D, Rosen-Zvi M.
PMID: 33767205
Sci Data. 2021 Mar 25;8(1):94. doi: 10.1038/s41597-021-00878-y.

The Coronavirus disease 2019 (COVID-19) global pandemic has transformed almost every facet of human society throughout the world. Against an emerging, highly transmissible disease, governments worldwide have implemented non-pharmaceutical interventions (NPIs) to slow the spread of the virus. Examples...

Cite
Suryanarayanan P, Tsou CH, Poddar A, et al. AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19. Sci Data. 2021;8(1):94doi: 10.1038/s41597-021-00878-y.
Suryanarayanan, P., Tsou, C. H., Poddar, A., Mahajan, D., Dandala, B., Madan, P., Agrawal, A., Wachira, C., Samuel, O. M., Bar-Shira, O., Kipchirchir, C., Okwako, S., Ogallo, W., Otieno, F., Nyota, T., Matu, F., Barros, V. R., Shats, D., Kagan, O., Remy, S., Bent, O., Guhan, P., Mahatma, S., Walcott-Bryant, A., Pathak, D., & Rosen-Zvi, M. (2021). AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19. Scientific data, 8(1), 94. https://doi.org/10.1038/s41597-021-00878-y
Suryanarayanan, Parthasarathy, et al. "AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19." Scientific data vol. 8,1 (2021): 94. doi: https://doi.org/10.1038/s41597-021-00878-y
Suryanarayanan P, Tsou CH, Poddar A, Mahajan D, Dandala B, Madan P, Agrawal A, Wachira C, Samuel OM, Bar-Shira O, Kipchirchir C, Okwako S, Ogallo W, Otieno F, Nyota T, Matu F, Barros VR, Shats D, Kagan O, Remy S, Bent O, Guhan P, Mahatma S, Walcott-Bryant A, Pathak D, Rosen-Zvi M. AI-assisted tracking of worldwide non-pharmaceutical interventions for COVID-19. Sci Data. 2021 Mar 25;8(1):94. doi: 10.1038/s41597-021-00878-y. PMID: 33767205; PMCID: PMC7994304.
Copy Download .nbib Format: NLM
ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis.

Scientific data

Imbert A, Rompais M, Selloum M, Castelli F, Mouton-Barbosa E, Brandolini-Bunlon M, Chu-Van E, Joly C, Hirschler A, Roger P, Burger T, Leblanc S, Sorg T, Ouzia S, Vandenbrouck Y, Médigue C, Junot C, Ferro M, Pujos-Guillot E, de Peredo AG, Fenaille F, Carapito C, Herault Y, Thévenot EA.
PMID: 34862403
Sci Data. 2021 Dec 03;8(1):311. doi: 10.1038/s41597-021-01095-3.

Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse...

Cite
Imbert A, Rompais M, Selloum M, et al. ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis. Sci Data. 2021;8(1):311doi: 10.1038/s41597-021-01095-3.
Imbert, A., Rompais, M., Selloum, M., Castelli, F., Mouton-Barbosa, E., Brandolini-Bunlon, M., Chu-Van, E., Joly, C., Hirschler, A., Roger, P., Burger, T., Leblanc, S., Sorg, T., Ouzia, S., Vandenbrouck, Y., Médigue, C., Junot, C., Ferro, M., Pujos-Guillot, E., de Peredo, A. G., Fenaille, F., Carapito, C., Herault, Y., & Thévenot, E. A. (2021). ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis. Scientific data, 8(1), 311. https://doi.org/10.1038/s41597-021-01095-3
Imbert, Alyssa, et al. "ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis." Scientific data vol. 8,1 (2021): 311. doi: https://doi.org/10.1038/s41597-021-01095-3
Imbert A, Rompais M, Selloum M, Castelli F, Mouton-Barbosa E, Brandolini-Bunlon M, Chu-Van E, Joly C, Hirschler A, Roger P, Burger T, Leblanc S, Sorg T, Ouzia S, Vandenbrouck Y, Médigue C, Junot C, Ferro M, Pujos-Guillot E, de Peredo AG, Fenaille F, Carapito C, Herault Y, Thévenot EA. ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis. Sci Data. 2021 Dec 03;8(1):311. doi: 10.1038/s41597-021-01095-3. PMID: 34862403.
Copy Download .nbib Format: NLM
Chromosome-scale assembly and high-density genetic map of the yellow drum, Nibea albiflora.

Scientific data

Xu D, Zhang W, Chen R, Song H, Tian L, Tan P, Wang L, Zhu Q, Wu B, Lou B, Min J, Zhou J.
PMID: 34654820
Sci Data. 2021 Oct 15;8(1):268. doi: 10.1038/s41597-021-01045-z.

The yellow drum (Nibea albiflora) is an economically important sciaenid fish in East Asian countries. In this study, we sequenced and assembled a near-complete gynogenetic yellow drum genome. We generated 45.63 Gb of Illumina short-reads and 80.27 Gb of...

Cite
Xu D, Zhang W, Chen R, et al. Chromosome-scale assembly and high-density genetic map of the yellow drum, Nibea albiflora. Sci Data. 2021;8(1):268doi: 10.1038/s41597-021-01045-z.
Xu, D., Zhang, W., Chen, R., Song, H., Tian, L., Tan, P., Wang, L., Zhu, Q., Wu, B., Lou, B., Min, J., & Zhou, J. (2021). Chromosome-scale assembly and high-density genetic map of the yellow drum, Nibea albiflora. Scientific data, 8(1), 268. https://doi.org/10.1038/s41597-021-01045-z
Xu, Dongdong, et al. "Chromosome-scale assembly and high-density genetic map of the yellow drum, Nibea albiflora." Scientific data vol. 8,1 (2021): 268. doi: https://doi.org/10.1038/s41597-021-01045-z
Xu D, Zhang W, Chen R, Song H, Tian L, Tan P, Wang L, Zhu Q, Wu B, Lou B, Min J, Zhou J. Chromosome-scale assembly and high-density genetic map of the yellow drum, Nibea albiflora. Sci Data. 2021 Oct 15;8(1):268. doi: 10.1038/s41597-021-01045-z. PMID: 34654820; PMCID: PMC8521588.
Copy Download .nbib Format: NLM
An open science resource for establishing reliability and reproducibility in functional connectomics.

Scientific data

Zuo XN, Anderson JS, Bellec P, Birn RM, Biswal BB, Blautzik J, Breitner JC, Buckner RL, Calhoun VD, Castellanos FX, Chen A, Chen B, Chen J, Chen X, Colcombe SJ, Courtney W, Craddock RC, Di Martino A, Dong HM, Fu X, Gong Q, Gorgolewski KJ, Han Y, He Y, He Y, Ho E, Holmes A, Hou XH, Huckins J, Jiang T, Jiang Y, Kelley W, Kelly C, King M, LaConte SM, Lainhart JE, Lei X, Li HJ, Li K, Li K, Lin Q, Liu D, Liu J, Liu X, Liu Y, Lu G, Lu J, Luna B, Luo J, Lurie D, Mao Y, Margulies DS, Mayer AR, Meindl T, Meyerand ME, Nan W, Nielsen JA, O'Connor D, Paulsen D, Prabhakaran V, Qi Z, Qiu J, Shao C, Shehzad Z, Tang W, Villringer A, Wang H, Wang K, Wei D, Wei GX, Weng XC, Wu X, Xu T, Yang N, Yang Z, Zang YF, Zhang L, Zhang Q, Zhang Z, Zhang Z, Zhao K, Zhen Z, Zhou Y, Zhu XT, Milham MP.
PMID: 25977800
Sci Data. 2014 Dec 09;1:140049. doi: 10.1038/sdata.2014.49. eCollection 2014.

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability...

Cite
Zuo XN, Anderson JS, Bellec P, et al. An open science resource for establishing reliability and reproducibility in functional connectomics. Sci Data. 2014;1:140049doi: 10.1038/sdata.2014.49.
Zuo, X. N., Anderson, J. S., Bellec, P., Birn, R. M., Biswal, B. B., Blautzik, J., Breitner, J. C., Buckner, R. L., Calhoun, V. D., Castellanos, F. X., Chen, A., Chen, B., Chen, J., Chen, X., Colcombe, S. J., Courtney, W., Craddock, R. C., Di Martino, A., Dong, H. M., Fu, X., Gong, Q., Gorgolewski, K. J., Han, Y., He, Y., He, Y., Ho, E., Holmes, A., Hou, X. H., Huckins, J., Jiang, T., Jiang, Y., Kelley, W., Kelly, C., King, M., LaConte, S. M., Lainhart, J. E., Lei, X., Li, H. J., Li, K., Li, K., Lin, Q., Liu, D., Liu, J., Liu, X., Liu, Y., Lu, G., Lu, J., Luna, B., Luo, J., Lurie, D., Mao, Y., Margulies, D. S., Mayer, A. R., Meindl, T., Meyerand, M. E., Nan, W., Nielsen, J. A., O'Connor, D., Paulsen, D., Prabhakaran, V., Qi, Z., Qiu, J., Shao, C., Shehzad, Z., Tang, W., Villringer, A., Wang, H., Wang, K., Wei, D., Wei, G. X., Weng, X. C., Wu, X., Xu, T., Yang, N., Yang, Z., Zang, Y. F., Zhang, L., Zhang, Q., Zhang, Z., Zhang, Z., Zhao, K., Zhen, Z., Zhou, Y., Zhu, X. T., & Milham, M. P. (2014). An open science resource for establishing reliability and reproducibility in functional connectomics. Scientific data, 1140049. https://doi.org/10.1038/sdata.2014.49
Zuo, Xi-Nian, et al. "An open science resource for establishing reliability and reproducibility in functional connectomics." Scientific data vol. 1 (2014): 140049. doi: https://doi.org/10.1038/sdata.2014.49
Zuo XN, Anderson JS, Bellec P, Birn RM, Biswal BB, Blautzik J, Breitner JC, Buckner RL, Calhoun VD, Castellanos FX, Chen A, Chen B, Chen J, Chen X, Colcombe SJ, Courtney W, Craddock RC, Di Martino A, Dong HM, Fu X, Gong Q, Gorgolewski KJ, Han Y, He Y, He Y, Ho E, Holmes A, Hou XH, Huckins J, Jiang T, Jiang Y, Kelley W, Kelly C, King M, LaConte SM, Lainhart JE, Lei X, Li HJ, Li K, Li K, Lin Q, Liu D, Liu J, Liu X, Liu Y, Lu G, Lu J, Luna B, Luo J, Lurie D, Mao Y, Margulies DS, Mayer AR, Meindl T, Meyerand ME, Nan W, Nielsen JA, O'Connor D, Paulsen D, Prabhakaran V, Qi Z, Qiu J, Shao C, Shehzad Z, Tang W, Villringer A, Wang H, Wang K, Wei D, Wei GX, Weng XC, Wu X, Xu T, Yang N, Yang Z, Zang YF, Zhang L, Zhang Q, Zhang Z, Zhang Z, Zhao K, Zhen Z, Zhou Y, Zhu XT, Milham MP. An open science resource for establishing reliability and reproducibility in functional connectomics. Sci Data. 2014 Dec 09;1:140049. doi: 10.1038/sdata.2014.49. eCollection 2014. PMID: 25977800; PMCID: PMC4421932.
Copy Download .nbib Format: NLM
Whole-exome and whole-transcriptome sequencing of canine mammary gland tumors.

Scientific data

Kim KK, Seung BJ, Kim D, Park HM, Lee S, Song DW, Lee G, Cheong JH, Nam H, Sur JH, Kim S.
PMID: 31413331
Sci Data. 2019 Aug 14;6(1):147. doi: 10.1038/s41597-019-0149-8.

Studies of naturally occurring cancers in dogs, which share many genetic and environmental factors with humans, provide valuable information as a comparative model for studying the mechanisms of human cancer pathogenesis. While individual and small-scale studies of canine cancers...

Cite
Kim KK, Seung BJ, Kim D, et al. Whole-exome and whole-transcriptome sequencing of canine mammary gland tumors. Sci Data. 2019;6(1):147doi: 10.1038/s41597-019-0149-8.
Kim, K. K., Seung, B. J., Kim, D., Park, H. M., Lee, S., Song, D. W., Lee, G., Cheong, J. H., Nam, H., Sur, J. H., & Kim, S. (2019). Whole-exome and whole-transcriptome sequencing of canine mammary gland tumors. Scientific data, 6(1), 147. https://doi.org/10.1038/s41597-019-0149-8
Kim, Ka-Kyung, et al. "Whole-exome and whole-transcriptome sequencing of canine mammary gland tumors." Scientific data vol. 6,1 (2019): 147. doi: https://doi.org/10.1038/s41597-019-0149-8
Kim KK, Seung BJ, Kim D, Park HM, Lee S, Song DW, Lee G, Cheong JH, Nam H, Sur JH, Kim S. Whole-exome and whole-transcriptome sequencing of canine mammary gland tumors. Sci Data. 2019 Aug 14;6(1):147. doi: 10.1038/s41597-019-0149-8. PMID: 31413331; PMCID: PMC6694171.
Copy Download .nbib Format: NLM
SCSPOD14, a South China Sea physical oceanographic dataset derived from in situ measurements during 1919-2014.

Scientific data

Zeng L, Wang D, Chen J, Wang W, Chen R.
PMID: 27116565
Sci Data. 2016 Apr 26;3:160029. doi: 10.1038/sdata.2016.29.

In addition to the oceanographic data available for the South China Sea (SCS) from the World Ocean Database (WOD) and Array for Real-time Geostrophic Oceanography (Argo) floats, a suite of observations has been made by the South China Sea...

Cite
Zeng L, Wang D, Chen J, et al. SCSPOD14, a South China Sea physical oceanographic dataset derived from in situ measurements during 1919-2014. Sci Data. 2016;3:160029doi: 10.1038/sdata.2016.29.
Zeng, L., Wang, D., Chen, J., Wang, W., & Chen, R. (2016). SCSPOD14, a South China Sea physical oceanographic dataset derived from in situ measurements during 1919-2014. Scientific data, 3160029. https://doi.org/10.1038/sdata.2016.29
Zeng, Lili, et al. "SCSPOD14, a South China Sea physical oceanographic dataset derived from in situ measurements during 1919-2014." Scientific data vol. 3 (2016): 160029. doi: https://doi.org/10.1038/sdata.2016.29
Zeng L, Wang D, Chen J, Wang W, Chen R. SCSPOD14, a South China Sea physical oceanographic dataset derived from in situ measurements during 1919-2014. Sci Data. 2016 Apr 26;3:160029. doi: 10.1038/sdata.2016.29. PMID: 27116565; PMCID: PMC4845715.
Copy Download .nbib Format: NLM
Showing 1369 to 1376 of 1376 entries