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De novo transcriptome profiling of highly purified human lymphocytes primary cells.

Scientific data

Bonnal RJ, Ranzani V, Arrigoni A, Curti S, Panzeri I, Gruarin P, Abrignani S, Rossetti G, Pagani M.
PMID: 26451251
Sci Data. 2015 Sep 29;2:150051. doi: 10.1038/sdata.2015.51. eCollection 2015.

To help better understand the role of long noncoding RNAs in the human immune system, we recently generated a comprehensive RNA-seq data set using 63 RNA samples from 13 subsets of T (CD4(+) naive, CD4(+) TH1, CD4(+) TH2, CD4(+)...

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Bonnal RJ, Ranzani V, Arrigoni A, et al. De novo transcriptome profiling of highly purified human lymphocytes primary cells. Sci Data. 2015;2:150051doi: 10.1038/sdata.2015.51.
Bonnal, R. J., Ranzani, V., Arrigoni, A., Curti, S., Panzeri, I., Gruarin, P., Abrignani, S., Rossetti, G., & Pagani, M. (2015). De novo transcriptome profiling of highly purified human lymphocytes primary cells. Scientific data, 2150051. https://doi.org/10.1038/sdata.2015.51
Bonnal, Raoul J P, et al. "De novo transcriptome profiling of highly purified human lymphocytes primary cells." Scientific data vol. 2 (2015): 150051. doi: https://doi.org/10.1038/sdata.2015.51
Bonnal RJ, Ranzani V, Arrigoni A, Curti S, Panzeri I, Gruarin P, Abrignani S, Rossetti G, Pagani M. De novo transcriptome profiling of highly purified human lymphocytes primary cells. Sci Data. 2015 Sep 29;2:150051. doi: 10.1038/sdata.2015.51. eCollection 2015. PMID: 26451251; PMCID: PMC4587370.
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Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry.

GigaScience

Oetjen J, Veselkov K, Watrous J, McKenzie JS, Becker M, Hauberg-Lotte L, Kobarg JH, Strittmatter N, Mróz AK, Hoffmann F, Trede D, Palmer A, Schiffler S, Steinhorst K, Aichler M, Goldin R, Guntinas-Lichius O, von Eggeling F, Thiele H, Maedler K, Walch A, Maass P, Dorrestein PC, Takats Z, Alexandrov T.
PMID: 25941567
Gigascience. 2015 May 04;4:20. doi: 10.1186/s13742-015-0059-4. eCollection 2015.

BACKGROUND: Three-dimensional (3D) imaging mass spectrometry (MS) is an analytical chemistry technique for the 3D molecular analysis of a tissue specimen, entire organ, or microbial colonies on an agar plate. 3D-imaging MS has unique advantages over existing 3D imaging...

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Oetjen J, Veselkov K, Watrous J, et al. Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry. Gigascience. 2015;4:20doi: 10.1186/s13742-015-0059-4.
Oetjen, J., Veselkov, K., Watrous, J., McKenzie, J. S., Becker, M., Hauberg-Lotte, L., Kobarg, J. H., Strittmatter, N., Mróz, A. K., Hoffmann, F., Trede, D., Palmer, A., Schiffler, S., Steinhorst, K., Aichler, M., Goldin, R., Guntinas-Lichius, O., von Eggeling, F., Thiele, H., Maedler, K., Walch, A., Maass, P., Dorrestein, P. C., Takats, Z., & Alexandrov, T. (2015). Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry. GigaScience, 420. https://doi.org/10.1186/s13742-015-0059-4
Oetjen, Janina, et al. "Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry." GigaScience vol. 4 (2015): 20. doi: https://doi.org/10.1186/s13742-015-0059-4
Oetjen J, Veselkov K, Watrous J, McKenzie JS, Becker M, Hauberg-Lotte L, Kobarg JH, Strittmatter N, Mróz AK, Hoffmann F, Trede D, Palmer A, Schiffler S, Steinhorst K, Aichler M, Goldin R, Guntinas-Lichius O, von Eggeling F, Thiele H, Maedler K, Walch A, Maass P, Dorrestein PC, Takats Z, Alexandrov T. Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry. Gigascience. 2015 May 04;4:20. doi: 10.1186/s13742-015-0059-4. eCollection 2015. PMID: 25941567; PMCID: PMC4418095.
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CicArVarDB: SNP and InDel database for advancing genetics research and breeding applications in chickpea.

Database : the journal of biological databases and curation

Doddamani D, Khan AW, Katta MA, Agarwal G, Thudi M, Ruperao P, Edwards D, Varshney RK.
PMID: 26289427
Database (Oxford). 2015 Aug 19;2015. doi: 10.1093/database/bav078. Print 2015.

Molecular markers are valuable tools for breeders to help accelerate crop improvement. High throughput sequencing technologies facilitate the discovery of large-scale variations such as single nucleotide polymorphisms (SNPs) and simple sequence repeats (SSRs). Sequencing of chickpea genome along with...

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Doddamani D, Khan AW, Katta MA, et al. CicArVarDB: SNP and InDel database for advancing genetics research and breeding applications in chickpea. Database (Oxford). 2015;2015doi: 10.1093/database/bav078.
Doddamani, D., Khan, A. W., Katta, M. A., Agarwal, G., Thudi, M., Ruperao, P., Edwards, D., & Varshney, R. K. (2015). CicArVarDB: SNP and InDel database for advancing genetics research and breeding applications in chickpea. Database : the journal of biological databases and curation, 2015. https://doi.org/10.1093/database/bav078
Doddamani, Dadakhalandar, et al. "CicArVarDB: SNP and InDel database for advancing genetics research and breeding applications in chickpea." Database : the journal of biological databases and curation vol. 2015 (2015). doi: https://doi.org/10.1093/database/bav078
Doddamani D, Khan AW, Katta MA, Agarwal G, Thudi M, Ruperao P, Edwards D, Varshney RK. CicArVarDB: SNP and InDel database for advancing genetics research and breeding applications in chickpea. Database (Oxford). 2015 Aug 19;2015. doi: 10.1093/database/bav078. Print 2015. PMID: 26289427; PMCID: PMC4541373.
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FlyVar: a database for genetic variation in Drosophila melanogaster.

Database : the journal of biological databases and curation

Wang F, Jiang L, Chen Y, Haelterman NA, Bellen HJ, Chen R.
PMID: 26289428
Database (Oxford). 2015 Aug 19;2015. doi: 10.1093/database/bav079. Print 2015.

FlyVar is a publicly and freely available platform that addresses the increasing need of next generation sequencing data analysis in the Drosophila research community. It is composed of three parts. First, a database that contains 5.94 million DNA polymorphisms...

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Wang F, Jiang L, Chen Y, et al. FlyVar: a database for genetic variation in Drosophila melanogaster. Database (Oxford). 2015;2015doi: 10.1093/database/bav079.
Wang, F., Jiang, L., Chen, Y., Haelterman, N. A., Bellen, H. J., & Chen, R. (2015). FlyVar: a database for genetic variation in Drosophila melanogaster. Database : the journal of biological databases and curation, 2015. https://doi.org/10.1093/database/bav079
Wang, Fei, et al. "FlyVar: a database for genetic variation in Drosophila melanogaster." Database : the journal of biological databases and curation vol. 2015 (2015). doi: https://doi.org/10.1093/database/bav079
Wang F, Jiang L, Chen Y, Haelterman NA, Bellen HJ, Chen R. FlyVar: a database for genetic variation in Drosophila melanogaster. Database (Oxford). 2015 Aug 19;2015. doi: 10.1093/database/bav079. Print 2015. PMID: 26289428; PMCID: PMC4541374.
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qPrimerDB: a thermodynamics-based gene-specific qPCR primer database for 147 organisms.

Nucleic acids research

Lu K, Li T, He J, Chang W, Zhang R, Liu M, Yu M, Fan Y, Ma J, Sun W, Qu C, Liu L, Li N, Liang Y, Wang R, Qian W, Tang Z, Xu X, Lei B, Zhang K, Li J.
PMID: 28977518
Nucleic Acids Res. 2018 Jan 04;46:D1229-D1236. doi: 10.1093/nar/gkx725.

Real-time quantitative polymerase chain reaction (qPCR) is one of the most important methods for analyzing the expression patterns of target genes. However, successful qPCR experiments rely heavily on the use of high-quality primers. Various qPCR primer databases have been...

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Lu K, Li T, He J, et al. qPrimerDB: a thermodynamics-based gene-specific qPCR primer database for 147 organisms. Nucleic Acids Res. 2018;46:D1229-D1236doi: 10.1093/nar/gkx725.
Lu, K., Li, T., He, J., Chang, W., Zhang, R., Liu, M., Yu, M., Fan, Y., Ma, J., Sun, W., Qu, C., Liu, L., Li, N., Liang, Y., Wang, R., Qian, W., Tang, Z., Xu, X., Lei, B., Zhang, K., & Li, J. (2018). qPrimerDB: a thermodynamics-based gene-specific qPCR primer database for 147 organisms. Nucleic acids research, 46D1229-D1236. https://doi.org/10.1093/nar/gkx725
Lu, Kun, et al. "qPrimerDB: a thermodynamics-based gene-specific qPCR primer database for 147 organisms." Nucleic acids research vol. 46 (2018): D1229-D1236. doi: https://doi.org/10.1093/nar/gkx725
Lu K, Li T, He J, Chang W, Zhang R, Liu M, Yu M, Fan Y, Ma J, Sun W, Qu C, Liu L, Li N, Liang Y, Wang R, Qian W, Tang Z, Xu X, Lei B, Zhang K, Li J. qPrimerDB: a thermodynamics-based gene-specific qPCR primer database for 147 organisms. Nucleic Acids Res. 2018 Jan 04;46:D1229-D1236. doi: 10.1093/nar/gkx725. PMID: 28977518; PMCID: PMC5753361.
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A global database on freshwater fish species occurrence in drainage basins.

Scientific data

Tedesco PA, Beauchard O, Bigorne R, Blanchet S, Buisson L, Conti L, Cornu JF, Dias MS, Grenouillet G, Hugueny B, Jézéquel C, Leprieur F, Brosse S, Oberdorff T.
PMID: 28972575
Sci Data. 2017 Oct 03;4:170141. doi: 10.1038/sdata.2017.141.

A growing interest is devoted to global-scale approaches in ecology and evolution that examine patterns and determinants of species diversity and the threats resulting from global change. These analyses obviously require global datasets of species distribution. Freshwater systems house...

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Tedesco PA, Beauchard O, Bigorne R, et al. A global database on freshwater fish species occurrence in drainage basins. Sci Data. 2017;4:170141doi: 10.1038/sdata.2017.141.
Tedesco, P. A., Beauchard, O., Bigorne, R., Blanchet, S., Buisson, L., Conti, L., Cornu, J. F., Dias, M. S., Grenouillet, G., Hugueny, B., Jézéquel, C., Leprieur, F., Brosse, S., & Oberdorff, T. (2017). A global database on freshwater fish species occurrence in drainage basins. Scientific data, 4170141. https://doi.org/10.1038/sdata.2017.141
Tedesco, Pablo A, et al. "A global database on freshwater fish species occurrence in drainage basins." Scientific data vol. 4 (2017): 170141. doi: https://doi.org/10.1038/sdata.2017.141
Tedesco PA, Beauchard O, Bigorne R, Blanchet S, Buisson L, Conti L, Cornu JF, Dias MS, Grenouillet G, Hugueny B, Jézéquel C, Leprieur F, Brosse S, Oberdorff T. A global database on freshwater fish species occurrence in drainage basins. Sci Data. 2017 Oct 03;4:170141. doi: 10.1038/sdata.2017.141. PMID: 28972575; PMCID: PMC5625552.
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Database of host-pathogen and related species interactions, and their global distribution.

Scientific data

Wardeh M, Risley C, McIntyre MK, Setzkorn C, Baylis M.
PMID: 26401317
Sci Data. 2015 Sep 15;2:150049. doi: 10.1038/sdata.2015.49. eCollection 2015.

Interactions between species, particularly where one is likely to be a pathogen of the other, as well as the geographical distribution of species, have been systematically extracted from various web-based, free-access sources, and assembled with the accompanying evidence into...

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Wardeh M, Risley C, McIntyre MK, et al. Database of host-pathogen and related species interactions, and their global distribution. Sci Data. 2015;2:150049doi: 10.1038/sdata.2015.49.
Wardeh, M., Risley, C., McIntyre, M. K., Setzkorn, C., & Baylis, M. (2015). Database of host-pathogen and related species interactions, and their global distribution. Scientific data, 2150049. https://doi.org/10.1038/sdata.2015.49
Wardeh, Maya, et al. "Database of host-pathogen and related species interactions, and their global distribution." Scientific data vol. 2 (2015): 150049. doi: https://doi.org/10.1038/sdata.2015.49
Wardeh M, Risley C, McIntyre MK, Setzkorn C, Baylis M. Database of host-pathogen and related species interactions, and their global distribution. Sci Data. 2015 Sep 15;2:150049. doi: 10.1038/sdata.2015.49. eCollection 2015. PMID: 26401317; PMCID: PMC4570150.
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An open resource for transdiagnostic research in pediatric mental health and learning disorders.

Scientific data

Alexander LM, Escalera J, Ai L, Andreotti C, Febre K, Mangone A, Vega-Potler N, Langer N, Alexander A, Kovacs M, Litke S, O'Hagan B, Andersen J, Bronstein B, Bui A, Bushey M, Butler H, Castagna V, Camacho N, Chan E, Citera D, Clucas J, Cohen S, Dufek S, Eaves M, Fradera B, Gardner J, Grant-Villegas N, Green G, Gregory C, Hart E, Harris S, Horton M, Kahn D, Kabotyanski K, Karmel B, Kelly SP, Kleinman K, Koo B, Kramer E, Lennon E, Lord C, Mantello G, Margolis A, Merikangas KR, Milham J, Minniti G, Neuhaus R, Levine A, Osman Y, Parra LC, Pugh KR, Racanello A, Restrepo A, Saltzman T, Septimus B, Tobe R, Waltz R, Williams A, Yeo A, Castellanos FX, Klein A, Paus T, Leventhal BL, Craddock RC, Koplewicz HS, Milham MP.
PMID: 29257126
Sci Data. 2017 Dec 19;4:170181. doi: 10.1038/sdata.2017.181.

Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental...

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Alexander LM, Escalera J, Ai L, et al. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci Data. 2017;4:170181doi: 10.1038/sdata.2017.181.
Alexander, L. M., Escalera, J., Ai, L., Andreotti, C., Febre, K., Mangone, A., Vega-Potler, N., Langer, N., Alexander, A., Kovacs, M., Litke, S., O'Hagan, B., Andersen, J., Bronstein, B., Bui, A., Bushey, M., Butler, H., Castagna, V., Camacho, N., Chan, E., Citera, D., Clucas, J., Cohen, S., Dufek, S., Eaves, M., Fradera, B., Gardner, J., Grant-Villegas, N., Green, G., Gregory, C., Hart, E., Harris, S., Horton, M., Kahn, D., Kabotyanski, K., Karmel, B., Kelly, S. P., Kleinman, K., Koo, B., Kramer, E., Lennon, E., Lord, C., Mantello, G., Margolis, A., Merikangas, K. R., Milham, J., Minniti, G., Neuhaus, R., Levine, A., Osman, Y., Parra, L. C., Pugh, K. R., Racanello, A., Restrepo, A., Saltzman, T., Septimus, B., Tobe, R., Waltz, R., Williams, A., Yeo, A., Castellanos, F. X., Klein, A., Paus, T., Leventhal, B. L., Craddock, R. C., Koplewicz, H. S., & Milham, M. P. (2017). An open resource for transdiagnostic research in pediatric mental health and learning disorders. Scientific data, 4170181. https://doi.org/10.1038/sdata.2017.181
Alexander, Lindsay M, et al. "An open resource for transdiagnostic research in pediatric mental health and learning disorders." Scientific data vol. 4 (2017): 170181. doi: https://doi.org/10.1038/sdata.2017.181
Alexander LM, Escalera J, Ai L, Andreotti C, Febre K, Mangone A, Vega-Potler N, Langer N, Alexander A, Kovacs M, Litke S, O'Hagan B, Andersen J, Bronstein B, Bui A, Bushey M, Butler H, Castagna V, Camacho N, Chan E, Citera D, Clucas J, Cohen S, Dufek S, Eaves M, Fradera B, Gardner J, Grant-Villegas N, Green G, Gregory C, Hart E, Harris S, Horton M, Kahn D, Kabotyanski K, Karmel B, Kelly SP, Kleinman K, Koo B, Kramer E, Lennon E, Lord C, Mantello G, Margolis A, Merikangas KR, Milham J, Minniti G, Neuhaus R, Levine A, Osman Y, Parra LC, Pugh KR, Racanello A, Restrepo A, Saltzman T, Septimus B, Tobe R, Waltz R, Williams A, Yeo A, Castellanos FX, Klein A, Paus T, Leventhal BL, Craddock RC, Koplewicz HS, Milham MP. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci Data. 2017 Dec 19;4:170181. doi: 10.1038/sdata.2017.181. PMID: 29257126; PMCID: PMC5735921.
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Automatized image processing of bovine blastocysts produced in vitro for quantitative variable determination.

Scientific data

Rocha JC, Passalia FJ, Matos FD, Takahashi MB, Maserati MP, Alves MF, de Almeida TG, Cardoso BL, Basso AC, Nogueira MFG.
PMID: 29257125
Sci Data. 2017 Dec 19;4:170192. doi: 10.1038/sdata.2017.192.

There is currently no objective, real-time and non-invasive method for evaluating the quality of mammalian embryos. In this study, we processed images of in vitro produced bovine blastocysts to obtain a deeper comprehension of the embryonic morphological aspects that...

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Rocha JC, Passalia FJ, Matos FD, et al. Automatized image processing of bovine blastocysts produced in vitro for quantitative variable determination. Sci Data. 2017;4:170192doi: 10.1038/sdata.2017.192.
Rocha, J. C., Passalia, F. J., Matos, F. D., Takahashi, M. B., Maserati, M. P., Alves, M. F., de Almeida, T. G., Cardoso, B. L., Basso, A. C., & Nogueira, M. F. G. (2017). Automatized image processing of bovine blastocysts produced in vitro for quantitative variable determination. Scientific data, 4170192. https://doi.org/10.1038/sdata.2017.192
Rocha, José Celso, et al. "Automatized image processing of bovine blastocysts produced in vitro for quantitative variable determination." Scientific data vol. 4 (2017): 170192. doi: https://doi.org/10.1038/sdata.2017.192
Rocha JC, Passalia FJ, Matos FD, Takahashi MB, Maserati MP, Alves MF, de Almeida TG, Cardoso BL, Basso AC, Nogueira MFG. Automatized image processing of bovine blastocysts produced in vitro for quantitative variable determination. Sci Data. 2017 Dec 19;4:170192. doi: 10.1038/sdata.2017.192. PMID: 29257125; PMCID: PMC5735923.
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Assembly of an early-matured japonica (Geng) rice genome, Suijing18, based on PacBio and Illumina sequencing.

Scientific data

Nie SJ, Liu YQ, Wang CC, Gao SW, Xu TT, Liu Q, Chang HL, Chen YB, Yan PC, Peng W, Zheng TQ, Xu JL, Li ZK.
PMID: 29257136
Sci Data. 2017 Dec 19;4:170195. doi: 10.1038/sdata.2017.195.

The early-matured japonica (Geng) rice variety, Suijing18 (SJ18), carries multiple elite traits including durable blast resistance, good grain quality, and high yield. Using PacBio SMRT technology, we produced over 25 Gb of long-read sequencing raw data from SJ18 with...

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Nie SJ, Liu YQ, Wang CC, et al. Assembly of an early-matured japonica (Geng) rice genome, Suijing18, based on PacBio and Illumina sequencing. Sci Data. 2017;4:170195doi: 10.1038/sdata.2017.195.
Nie, S. J., Liu, Y. Q., Wang, C. C., Gao, S. W., Xu, T. T., Liu, Q., Chang, H. L., Chen, Y. B., Yan, P. C., Peng, W., Zheng, T. Q., Xu, J. L., & Li, Z. K. (2017). Assembly of an early-matured japonica (Geng) rice genome, Suijing18, based on PacBio and Illumina sequencing. Scientific data, 4170195. https://doi.org/10.1038/sdata.2017.195
Nie, Shou-Jun, et al. "Assembly of an early-matured japonica (Geng) rice genome, Suijing18, based on PacBio and Illumina sequencing." Scientific data vol. 4 (2017): 170195. doi: https://doi.org/10.1038/sdata.2017.195
Nie SJ, Liu YQ, Wang CC, Gao SW, Xu TT, Liu Q, Chang HL, Chen YB, Yan PC, Peng W, Zheng TQ, Xu JL, Li ZK. Assembly of an early-matured japonica (Geng) rice genome, Suijing18, based on PacBio and Illumina sequencing. Sci Data. 2017 Dec 19;4:170195. doi: 10.1038/sdata.2017.195. PMID: 29257136; PMCID: PMC5735919.
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Ash leaf metabolomes reveal differences between trees tolerant and susceptible to ash dieback disease.

Scientific data

Sambles CM, Salmon DL, Florance H, Howard TP, Smirnoff N, Nielsen LR, McKinney LV, Kjær ED, Buggs RJA, Studholme DJ, Grant M.
PMID: 29257137
Sci Data. 2017 Dec 19;4:170190. doi: 10.1038/sdata.2017.190.

European common ash, Fraxinus excelsior, is currently threatened by Ash dieback (ADB) caused by the fungus, Hymenoscyphus fraxineus. To detect and identify metabolites that may be products of pathways important in contributing to resistance against H. fraxineus, we performed...

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Sambles CM, Salmon DL, Florance H, et al. Ash leaf metabolomes reveal differences between trees tolerant and susceptible to ash dieback disease. Sci Data. 2017;4:170190doi: 10.1038/sdata.2017.190.
Sambles, C. M., Salmon, D. L., Florance, H., Howard, T. P., Smirnoff, N., Nielsen, L. R., McKinney, L. V., Kjær, E. D., Buggs, R. J. A., Studholme, D. J., & Grant, M. (2017). Ash leaf metabolomes reveal differences between trees tolerant and susceptible to ash dieback disease. Scientific data, 4170190. https://doi.org/10.1038/sdata.2017.190
Sambles, Christine M, et al. "Ash leaf metabolomes reveal differences between trees tolerant and susceptible to ash dieback disease." Scientific data vol. 4 (2017): 170190. doi: https://doi.org/10.1038/sdata.2017.190
Sambles CM, Salmon DL, Florance H, Howard TP, Smirnoff N, Nielsen LR, McKinney LV, Kjær ED, Buggs RJA, Studholme DJ, Grant M. Ash leaf metabolomes reveal differences between trees tolerant and susceptible to ash dieback disease. Sci Data. 2017 Dec 19;4:170190. doi: 10.1038/sdata.2017.190. PMID: 29257137; PMCID: PMC5735976.
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Twelve thousand recent patellogastropods from a northeastern Pacific latitudinal gradient.

Scientific data

Kahanamoku SS, Hull PM, Lindberg DR, Hsiang AY, Clites EC, Finnegan S.
PMID: 29313842
Sci Data. 2018 Jan 09;5:170197. doi: 10.1038/sdata.2017.197.

Body size distributions can vary widely among communities, with important implications for ecological dynamics, energetics, and evolutionary history. Here we present a dataset of body size and shape for 12,035 extant Patellogastropoda (true limpet) specimens from the collections of...

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Kahanamoku SS, Hull PM, Lindberg DR, et al. Twelve thousand recent patellogastropods from a northeastern Pacific latitudinal gradient. Sci Data. 2018;5:170197doi: 10.1038/sdata.2017.197.
Kahanamoku, S. S., Hull, P. M., Lindberg, D. R., Hsiang, A. Y., Clites, E. C., & Finnegan, S. (2018). Twelve thousand recent patellogastropods from a northeastern Pacific latitudinal gradient. Scientific data, 5170197. https://doi.org/10.1038/sdata.2017.197
Kahanamoku, Sara S, et al. "Twelve thousand recent patellogastropods from a northeastern Pacific latitudinal gradient." Scientific data vol. 5 (2018): 170197. doi: https://doi.org/10.1038/sdata.2017.197
Kahanamoku SS, Hull PM, Lindberg DR, Hsiang AY, Clites EC, Finnegan S. Twelve thousand recent patellogastropods from a northeastern Pacific latitudinal gradient. Sci Data. 2018 Jan 09;5:170197. doi: 10.1038/sdata.2017.197. PMID: 29313842; PMCID: PMC5759373.
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