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A repository of assays to quantify 10,000 human proteins by SWATH-MS.

Scientific data

Rosenberger G, Koh CC, Guo T, Röst HL, Kouvonen P, Collins BC, Heusel M, Liu Y, Caron E, Vichalkovski A, Faini M, Schubert OT, Faridi P, Ebhardt HA, Matondo M, Lam H, Bader SL, Campbell DS, Deutsch EW, Moritz RL, Tate S, Aebersold R.
PMID: 25977788
Sci Data. 2014 Sep 16;1:140031. doi: 10.1038/sdata.2014.31. eCollection 2014.

Mass spectrometry is the method of choice for deep and reliable exploration of the (human) proteome. Targeted mass spectrometry reliably detects and quantifies pre-determined sets of proteins in a complex biological matrix and is used in studies that rely...

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Rosenberger G, Koh CC, Guo T, et al. A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci Data. 2014;1:140031doi: 10.1038/sdata.2014.31.
Rosenberger, G., Koh, C. C., Guo, T., Röst, H. L., Kouvonen, P., Collins, B. C., Heusel, M., Liu, Y., Caron, E., Vichalkovski, A., Faini, M., Schubert, O. T., Faridi, P., Ebhardt, H. A., Matondo, M., Lam, H., Bader, S. L., Campbell, D. S., Deutsch, E. W., Moritz, R. L., Tate, S., & Aebersold, R. (2014). A repository of assays to quantify 10,000 human proteins by SWATH-MS. Scientific data, 1140031. https://doi.org/10.1038/sdata.2014.31
Rosenberger, George, et al. "A repository of assays to quantify 10,000 human proteins by SWATH-MS." Scientific data vol. 1 (2014): 140031. doi: https://doi.org/10.1038/sdata.2014.31
Rosenberger G, Koh CC, Guo T, Röst HL, Kouvonen P, Collins BC, Heusel M, Liu Y, Caron E, Vichalkovski A, Faini M, Schubert OT, Faridi P, Ebhardt HA, Matondo M, Lam H, Bader SL, Campbell DS, Deutsch EW, Moritz RL, Tate S, Aebersold R. A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci Data. 2014 Sep 16;1:140031. doi: 10.1038/sdata.2014.31. eCollection 2014. PMID: 25977788; PMCID: PMC4322573.
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kpath: integration of metabolic pathway linked data.

Database : the journal of biological databases and curation

Navas-Delgado I, García-Godoy MJ, López-Camacho E, Rybinski M, Reyes-Palomares A, Medina MÁ, Aldana-Montes JF.
PMID: 26055101
Database (Oxford). 2015 Jun 08;2015:bav053. doi: 10.1093/database/bav053. Print 2015.

In the last few years, the Life Sciences domain has experienced a rapid growth in the amount of available biological databases. The heterogeneity of these databases makes data integration a challenging issue. Some integration challenges are locating resources, relationships,...

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Navas-Delgado I, García-Godoy MJ, López-Camacho E, et al. kpath: integration of metabolic pathway linked data. Database (Oxford). 2015;2015:bav053doi: 10.1093/database/bav053.
Navas-Delgado, I., García-Godoy, M. J., López-Camacho, E., Rybinski, M., Reyes-Palomares, A., Medina, M. �. �., & Aldana-Montes, J. F. (2015). kpath: integration of metabolic pathway linked data. Database : the journal of biological databases and curation, 2015bav053. https://doi.org/10.1093/database/bav053
Navas-Delgado, Ismael, et al. "kpath: integration of metabolic pathway linked data." Database : the journal of biological databases and curation vol. 2015 (2015): bav053. doi: https://doi.org/10.1093/database/bav053
Navas-Delgado I, García-Godoy MJ, López-Camacho E, Rybinski M, Reyes-Palomares A, Medina MÁ, Aldana-Montes JF. kpath: integration of metabolic pathway linked data. Database (Oxford). 2015 Jun 08;2015:bav053. doi: 10.1093/database/bav053. Print 2015. PMID: 26055101; PMCID: PMC4460419.
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Genome-wide association analysis of type 2 diabetes in the EPIC-InterAct study.

Scientific data

Cai L, Wheeler E, Kerrison ND, Luan J, Deloukas P, Franks PW, Amiano P, Ardanaz E, Bonet C, Fagherazzi G, Groop LC, Kaaks R, Huerta JM, Masala G, Nilsson PM, Overvad K, Pala V, Panico S, Rodriguez-Barranco M, Rolandsson O, Sacerdote C, Schulze MB, Spijkerman AMW, Tjonneland A, Tumino R, van der Schouw YT, Sharp SJ, Forouhi NG, Riboli E, McCarthy MI, Barroso I, Langenberg C, Wareham NJ.
PMID: 33188205
Sci Data. 2020 Nov 13;7(1):393. doi: 10.1038/s41597-020-00716-7.

Type 2 diabetes (T2D) is a global public health challenge. Whilst the advent of genome-wide association studies has identified >400 genetic variants associated with T2D, our understanding of its biological mechanisms and translational insights is still limited. The EPIC-InterAct...

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Cai L, Wheeler E, Kerrison ND, et al. Genome-wide association analysis of type 2 diabetes in the EPIC-InterAct study. Sci Data. 2020;7(1):393doi: 10.1038/s41597-020-00716-7.
Cai, L., Wheeler, E., Kerrison, N. D., Luan, J., Deloukas, P., Franks, P. W., Amiano, P., Ardanaz, E., Bonet, C., Fagherazzi, G., Groop, L. C., Kaaks, R., Huerta, J. M., Masala, G., Nilsson, P. M., Overvad, K., Pala, V., Panico, S., Rodriguez-Barranco, M., Rolandsson, O., Sacerdote, C., Schulze, M. B., Spijkerman, A. M. W., Tjonneland, A., Tumino, R., van der Schouw, Y. T., Sharp, S. J., Forouhi, N. G., Riboli, E., McCarthy, M. I., Barroso, I., Langenberg, C., & Wareham, N. J. (2020). Genome-wide association analysis of type 2 diabetes in the EPIC-InterAct study. Scientific data, 7(1), 393. https://doi.org/10.1038/s41597-020-00716-7
Cai, Lina, et al. "Genome-wide association analysis of type 2 diabetes in the EPIC-InterAct study." Scientific data vol. 7,1 (2020): 393. doi: https://doi.org/10.1038/s41597-020-00716-7
Cai L, Wheeler E, Kerrison ND, Luan J, Deloukas P, Franks PW, Amiano P, Ardanaz E, Bonet C, Fagherazzi G, Groop LC, Kaaks R, Huerta JM, Masala G, Nilsson PM, Overvad K, Pala V, Panico S, Rodriguez-Barranco M, Rolandsson O, Sacerdote C, Schulze MB, Spijkerman AMW, Tjonneland A, Tumino R, van der Schouw YT, Sharp SJ, Forouhi NG, Riboli E, McCarthy MI, Barroso I, Langenberg C, Wareham NJ. Genome-wide association analysis of type 2 diabetes in the EPIC-InterAct study. Sci Data. 2020 Nov 13;7(1):393. doi: 10.1038/s41597-020-00716-7. PMID: 33188205; PMCID: PMC7666191.
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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...

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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.
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Smart Distance Lab's art fair, experimental data on social distancing during the COVID-19 pandemic.

Scientific data

Tanis CC, Leach NM, Geiger SJ, Nauta FH, Dablander F, van Harreveld F, de Wit S, Kanters G, Knoppers J, Markus DAW, Bouten RRM, Oostvogel QH, Boersma MJ, van der Steenhoven MV, Borsboom D, Blanken TF.
PMID: 34267219
Sci Data. 2021 Jul 15;8(1):179. doi: 10.1038/s41597-021-00971-2.

In the absence of a vaccine, social distancing behaviour is pivotal to mitigate COVID-19 virus spread. In this large-scale behavioural experiment, we gathered data during Smart Distance Lab: The Art Fair (n = 839) between August 28 and 30,...

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Tanis CC, Leach NM, Geiger SJ, et al. Smart Distance Lab's art fair, experimental data on social distancing during the COVID-19 pandemic. Sci Data. 2021;8(1):179doi: 10.1038/s41597-021-00971-2.
Tanis, C. C., Leach, N. M., Geiger, S. J., Nauta, F. H., Dablander, F., van Harreveld, F., de Wit, S., Kanters, G., Knoppers, J., Markus, D. A. W., Bouten, R. R. M., Oostvogel, Q. H., Boersma, M. J., van der Steenhoven, M. V., Borsboom, D., & Blanken, T. F. (2021). Smart Distance Lab's art fair, experimental data on social distancing during the COVID-19 pandemic. Scientific data, 8(1), 179. https://doi.org/10.1038/s41597-021-00971-2
Tanis, Charlotte C, et al. "Smart Distance Lab's art fair, experimental data on social distancing during the COVID-19 pandemic." Scientific data vol. 8,1 (2021): 179. doi: https://doi.org/10.1038/s41597-021-00971-2
Tanis CC, Leach NM, Geiger SJ, Nauta FH, Dablander F, van Harreveld F, de Wit S, Kanters G, Knoppers J, Markus DAW, Bouten RRM, Oostvogel QH, Boersma MJ, van der Steenhoven MV, Borsboom D, Blanken TF. Smart Distance Lab's art fair, experimental data on social distancing during the COVID-19 pandemic. Sci Data. 2021 Jul 15;8(1):179. doi: 10.1038/s41597-021-00971-2. PMID: 34267219; PMCID: PMC8282783.
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Community-curated and standardised metadata of published ancient metagenomic samples with AncientMetagenomeDir.

Scientific data

Fellows Yates JA, Andrades Valtueña A, Vågene ÅJ, Cribdon B, Velsko IM, Borry M, Bravo-Lopez MJ, Fernandez-Guerra A, Green EJ, Ramachandran SL, Heintzman PD, Spyrou MA, Hübner A, Gancz AS, Hider J, Allshouse AF, Zaro V, Warinner C.
PMID: 33500403
Sci Data. 2021 Jan 26;8(1):31. doi: 10.1038/s41597-021-00816-y.

Ancient DNA and RNA are valuable data sources for a wide range of disciplines. Within the field of ancient metagenomics, the number of published genetic datasets has risen dramatically in recent years, and tracking this data for reuse is...

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Fellows Yates JA, Andrades Valtueña A, Vågene ÅJ, et al. Community-curated and standardised metadata of published ancient metagenomic samples with AncientMetagenomeDir. Sci Data. 2021;8(1):31doi: 10.1038/s41597-021-00816-y.
Fellows Yates, J. A., Andrades Valtueña, A., Vågene, �. �. J., Cribdon, B., Velsko, I. M., Borry, M., Bravo-Lopez, M. J., Fernandez-Guerra, A., Green, E. J., Ramachandran, S. L., Heintzman, P. D., Spyrou, M. A., Hübner, A., Gancz, A. S., Hider, J., Allshouse, A. F., Zaro, V., & Warinner, C. (2021). Community-curated and standardised metadata of published ancient metagenomic samples with AncientMetagenomeDir. Scientific data, 8(1), 31. https://doi.org/10.1038/s41597-021-00816-y
Fellows Yates, James A, et al. "Community-curated and standardised metadata of published ancient metagenomic samples with AncientMetagenomeDir." Scientific data vol. 8,1 (2021): 31. doi: https://doi.org/10.1038/s41597-021-00816-y
Fellows Yates JA, Andrades Valtueña A, Vågene ÅJ, Cribdon B, Velsko IM, Borry M, Bravo-Lopez MJ, Fernandez-Guerra A, Green EJ, Ramachandran SL, Heintzman PD, Spyrou MA, Hübner A, Gancz AS, Hider J, Allshouse AF, Zaro V, Warinner C. Community-curated and standardised metadata of published ancient metagenomic samples with AncientMetagenomeDir. Sci Data. 2021 Jan 26;8(1):31. doi: 10.1038/s41597-021-00816-y. PMID: 33500403; PMCID: PMC7838265.
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Onshore industrial wind turbine locations for the United States up to March 2014.

Scientific data

Diffendorfer JE, Kramer LA, Ancona ZH, Garrity CP.
PMID: 26601687
Sci Data. 2015 Nov 24;2:150060. doi: 10.1038/sdata.2015.60.

Wind energy is a rapidly growing form of renewable energy in the United States. While summary information on the total amounts of installed capacity are available by state, a free, centralized, national, turbine-level, geospatial dataset useful for scientific research,...

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Diffendorfer JE, Kramer LA, Ancona ZH, et al. Onshore industrial wind turbine locations for the United States up to March 2014. Sci Data. 2015;2:150060doi: 10.1038/sdata.2015.60.
Diffendorfer, J. E., Kramer, L. A., Ancona, Z. H., & Garrity, C. P. (2015). Onshore industrial wind turbine locations for the United States up to March 2014. Scientific data, 2150060. https://doi.org/10.1038/sdata.2015.60
Diffendorfer, Jay E, et al. "Onshore industrial wind turbine locations for the United States up to March 2014." Scientific data vol. 2 (2015): 150060. doi: https://doi.org/10.1038/sdata.2015.60
Diffendorfer JE, Kramer LA, Ancona ZH, Garrity CP. Onshore industrial wind turbine locations for the United States up to March 2014. Sci Data. 2015 Nov 24;2:150060. doi: 10.1038/sdata.2015.60. PMID: 26601687; PMCID: PMC4658569.
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An extended Arctic proxy temperature database for the past 2,000 years.

Scientific data

McKay NP, Kaufman DS.
PMID: 25977783
Sci Data. 2014 Aug 19;1:140026. doi: 10.1038/sdata.2014.26. eCollection 2014.

Robust climate reconstructions of the most recent centuries and millennia are invaluable for placing modern warming in the context of natural variability. Here we present an extended and revised database (version 1.1) of proxy temperature records recently used to...

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McKay NP, Kaufman DS. An extended Arctic proxy temperature database for the past 2,000 years. Sci Data. 2014;1:140026doi: 10.1038/sdata.2014.26.
McKay, N. P., & Kaufman, D. S. (2014). An extended Arctic proxy temperature database for the past 2,000 years. Scientific data, 1140026. https://doi.org/10.1038/sdata.2014.26
McKay, Nicholas P, and Kaufman, Darrell S. "An extended Arctic proxy temperature database for the past 2,000 years." Scientific data vol. 1 (2014): 140026. doi: https://doi.org/10.1038/sdata.2014.26
McKay NP, Kaufman DS. An extended Arctic proxy temperature database for the past 2,000 years. Sci Data. 2014 Aug 19;1:140026. doi: 10.1038/sdata.2014.26. eCollection 2014. PMID: 25977783; PMCID: PMC4322576.
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A dataset of human decision-making in teamwork management.

Scientific data

Yu H, Shen Z, Miao C, Leung C, Chen Y, Fauvel S, Lin J, Cui L, Pan Z, Yang Q.
PMID: 28094787
Sci Data. 2017 Jan 17;4:160127. doi: 10.1038/sdata.2016.127.

Today, most endeavours require teamwork by people with diverse skills and characteristics. In managing teamwork, decisions are often made under uncertainty and resource constraints. The strategies and the effectiveness of the strategies different people adopt to manage teamwork under...

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Yu H, Shen Z, Miao C, et al. A dataset of human decision-making in teamwork management. Sci Data. 2017;4:160127doi: 10.1038/sdata.2016.127.
Yu, H., Shen, Z., Miao, C., Leung, C., Chen, Y., Fauvel, S., Lin, J., Cui, L., Pan, Z., & Yang, Q. (2017). A dataset of human decision-making in teamwork management. Scientific data, 4160127. https://doi.org/10.1038/sdata.2016.127
Yu, Han, et al. "A dataset of human decision-making in teamwork management." Scientific data vol. 4 (2017): 160127. doi: https://doi.org/10.1038/sdata.2016.127
Yu H, Shen Z, Miao C, Leung C, Chen Y, Fauvel S, Lin J, Cui L, Pan Z, Yang Q. A dataset of human decision-making in teamwork management. Sci Data. 2017 Jan 17;4:160127. doi: 10.1038/sdata.2016.127. PMID: 28094787; PMCID: PMC5240621.
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Bilateral international migration flow estimates for 200 countries.

Scientific data

Abel GJ, Cohen JE.
PMID: 31209218
Sci Data. 2019 Jun 17;6(1):82. doi: 10.1038/s41597-019-0089-3.

Data on stocks and flows of international migration are necessary to understand migrant patterns and trends and to monitor and evaluate migration-relevant international development agendas. Many countries do not publish data on bilateral migration flows. At least six methods...

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Abel GJ, Cohen JE. Bilateral international migration flow estimates for 200 countries. Sci Data. 2019;6(1):82doi: 10.1038/s41597-019-0089-3.
Abel, G. J., & Cohen, J. E. (2019). Bilateral international migration flow estimates for 200 countries. Scientific data, 6(1), 82. https://doi.org/10.1038/s41597-019-0089-3
Abel, Guy J, and Cohen, Joel E. "Bilateral international migration flow estimates for 200 countries." Scientific data vol. 6,1 (2019): 82. doi: https://doi.org/10.1038/s41597-019-0089-3
Abel GJ, Cohen JE. Bilateral international migration flow estimates for 200 countries. Sci Data. 2019 Jun 17;6(1):82. doi: 10.1038/s41597-019-0089-3. PMID: 31209218; PMCID: PMC6572777.
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A database to enable discovery and design of piezoelectric materials.

Scientific data

de Jong M, Chen W, Geerlings H, Asta M, Persson KA.
PMID: 26451252
Sci Data. 2015 Sep 29;2:150053. doi: 10.1038/sdata.2015.53. eCollection 2015.

Piezoelectric materials are used in numerous applications requiring a coupling between electrical fields and mechanical strain. Despite the technological importance of this class of materials, for only a small fraction of all inorganic compounds which display compatible crystallographic symmetry,...

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de Jong M, Chen W, Geerlings H, et al. A database to enable discovery and design of piezoelectric materials. Sci Data. 2015;2:150053doi: 10.1038/sdata.2015.53.
de Jong, M., Chen, W., Geerlings, H., Asta, M., & Persson, K. A. (2015). A database to enable discovery and design of piezoelectric materials. Scientific data, 2150053. https://doi.org/10.1038/sdata.2015.53
de Jong, Maarten, et al. "A database to enable discovery and design of piezoelectric materials." Scientific data vol. 2 (2015): 150053. doi: https://doi.org/10.1038/sdata.2015.53
de Jong M, Chen W, Geerlings H, Asta M, Persson KA. A database to enable discovery and design of piezoelectric materials. Sci Data. 2015 Sep 29;2:150053. doi: 10.1038/sdata.2015.53. eCollection 2015. PMID: 26451252; PMCID: PMC4587372.
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Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities.

Scientific data

Zaman S, Wesley A, Silva DRDC, Buddharaju P, Akbar F, Gao G, Mark G, Gutierrez-Osuna R, Pavlidis I.
PMID: 31704939
Sci Data. 2019 Nov 08;6(1):264. doi: 10.1038/s41597-019-0249-5.

We describe a controlled experiment, aiming to study productivity and stress effects of email interruptions and activity interactions in the modern office. The measurement set includes multimodal data for n = 63 knowledge workers who volunteered for this experiment...

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Zaman S, Wesley A, Silva DRDC, et al. Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities. Sci Data. 2019;6(1):264doi: 10.1038/s41597-019-0249-5.
Zaman, S., Wesley, A., Silva, D. R. D. C., Buddharaju, P., Akbar, F., Gao, G., Mark, G., Gutierrez-Osuna, R., & Pavlidis, I. (2019). Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities. Scientific data, 6(1), 264. https://doi.org/10.1038/s41597-019-0249-5
Zaman, Shaila, et al. "Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities." Scientific data vol. 6,1 (2019): 264. doi: https://doi.org/10.1038/s41597-019-0249-5
Zaman S, Wesley A, Silva DRDC, Buddharaju P, Akbar F, Gao G, Mark G, Gutierrez-Osuna R, Pavlidis I. Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities. Sci Data. 2019 Nov 08;6(1):264. doi: 10.1038/s41597-019-0249-5. PMID: 31704939; PMCID: PMC6841929.
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Showing 13 to 24 of 50 entries