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A global database of lake surface temperatures collected by in situ and satellite methods from 1985-2009.

Scientific data

Sharma S, Gray DK, Read JS, O'Reilly CM, Schneider P, Qudrat A, Gries C, Stefanoff S, Hampton SE, Hook S, Lenters JD, Livingstone DM, McIntyre PB, Adrian R, Allan MG, Anneville O, Arvola L, Austin J, Bailey J, Baron JS, Brookes J, Chen Y, Daly R, Dokulil M, Dong B, Ewing K, de Eyto E, Hamilton D, Havens K, Haydon S, Hetzenauer H, Heneberry J, Hetherington AL, Higgins SN, Hixson E, Izmest'eva LR, Jones BM, Kangur K, Kasprzak P, Köster O, Kraemer BM, Kumagai M, Kuusisto E, Leshkevich G, May L, MacIntyre S, Müller-Navarra D, Naumenko M, Noges P, Noges T, Niederhauser P, North RP, Paterson AM, Plisnier PD, Rigosi A, Rimmer A, Rogora M, Rudstam L, Rusak JA, Salmaso N, Samal NR, Schindler DE, Schladow G, Schmidt SR, Schultz T, Silow EA, Straile D, Teubner K, Verburg P, Voutilainen A, Watkinson A, Weyhenmeyer GA, Williamson CE, Woo KH.
PMID: 25977814
Sci Data. 2015 Mar 17;2:150008. doi: 10.1038/sdata.2015.8. eCollection 2015.

Global environmental change has influenced lake surface temperatures, a key driver of ecosystem structure and function. Recent studies have suggested significant warming of water temperatures in individual lakes across many different regions around the world. However, the spatial and...

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Sharma S, Gray DK, Read JS, et al. A global database of lake surface temperatures collected by in situ and satellite methods from 1985-2009. Sci Data. 2015;2:150008doi: 10.1038/sdata.2015.8.
Sharma, S., Gray, D. K., Read, J. S., O'Reilly, C. M., Schneider, P., Qudrat, A., Gries, C., Stefanoff, S., Hampton, S. E., Hook, S., Lenters, J. D., Livingstone, D. M., McIntyre, P. B., Adrian, R., Allan, M. G., Anneville, O., Arvola, L., Austin, J., Bailey, J., Baron, J. S., Brookes, J., Chen, Y., Daly, R., Dokulil, M., Dong, B., Ewing, K., de Eyto, E., Hamilton, D., Havens, K., Haydon, S., Hetzenauer, H., Heneberry, J., Hetherington, A. L., Higgins, S. N., Hixson, E., Izmest'eva, L. R., Jones, B. M., Kangur, K., Kasprzak, P., Köster, O., Kraemer, B. M., Kumagai, M., Kuusisto, E., Leshkevich, G., May, L., MacIntyre, S., Müller-Navarra, D., Naumenko, M., Noges, P., Noges, T., Niederhauser, P., North, R. P., Paterson, A. M., Plisnier, P. D., Rigosi, A., Rimmer, A., Rogora, M., Rudstam, L., Rusak, J. A., Salmaso, N., Samal, N. R., Schindler, D. E., Schladow, G., Schmidt, S. R., Schultz, T., Silow, E. A., Straile, D., Teubner, K., Verburg, P., Voutilainen, A., Watkinson, A., Weyhenmeyer, G. A., Williamson, C. E., & Woo, K. H. (2015). A global database of lake surface temperatures collected by in situ and satellite methods from 1985-2009. Scientific data, 2150008. https://doi.org/10.1038/sdata.2015.8
Sharma, Sapna, et al. "A global database of lake surface temperatures collected by in situ and satellite methods from 1985-2009." Scientific data vol. 2 (2015): 150008. doi: https://doi.org/10.1038/sdata.2015.8
Sharma S, Gray DK, Read JS, O'Reilly CM, Schneider P, Qudrat A, Gries C, Stefanoff S, Hampton SE, Hook S, Lenters JD, Livingstone DM, McIntyre PB, Adrian R, Allan MG, Anneville O, Arvola L, Austin J, Bailey J, Baron JS, Brookes J, Chen Y, Daly R, Dokulil M, Dong B, Ewing K, de Eyto E, Hamilton D, Havens K, Haydon S, Hetzenauer H, Heneberry J, Hetherington AL, Higgins SN, Hixson E, Izmest'eva LR, Jones BM, Kangur K, Kasprzak P, Köster O, Kraemer BM, Kumagai M, Kuusisto E, Leshkevich G, May L, MacIntyre S, Müller-Navarra D, Naumenko M, Noges P, Noges T, Niederhauser P, North RP, Paterson AM, Plisnier PD, Rigosi A, Rimmer A, Rogora M, Rudstam L, Rusak JA, Salmaso N, Samal NR, Schindler DE, Schladow G, Schmidt SR, Schultz T, Silow EA, Straile D, Teubner K, Verburg P, Voutilainen A, Watkinson A, Weyhenmeyer GA, Williamson CE, Woo KH. A global database of lake surface temperatures collected by in situ and satellite methods from 1985-2009. Sci Data. 2015 Mar 17;2:150008. doi: 10.1038/sdata.2015.8. eCollection 2015. PMID: 25977814; PMCID: PMC4423389.
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A user-friendly database of coastal flooding in the United Kingdom from 1915-2014.

Scientific data

Haigh ID, Wadey MP, Gallop SL, Loehr H, Nicholls RJ, Horsburgh K, Brown JM, Bradshaw E.
PMID: 25984352
Sci Data. 2015 May 12;2:150021. doi: 10.1038/sdata.2015.21. eCollection 2015.

Coastal flooding caused by extreme sea levels can be devastating, with long-lasting and diverse consequences. Historically, the UK has suffered major flooding events, and at present 2.5 million properties and £150 billion of assets are potentially exposed to coastal...

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Haigh ID, Wadey MP, Gallop SL, et al. A user-friendly database of coastal flooding in the United Kingdom from 1915-2014. Sci Data. 2015;2:150021doi: 10.1038/sdata.2015.21.
Haigh, I. D., Wadey, M. P., Gallop, S. L., Loehr, H., Nicholls, R. J., Horsburgh, K., Brown, J. M., & Bradshaw, E. (2015). A user-friendly database of coastal flooding in the United Kingdom from 1915-2014. Scientific data, 2150021. https://doi.org/10.1038/sdata.2015.21
Haigh, Ivan D, et al. "A user-friendly database of coastal flooding in the United Kingdom from 1915-2014." Scientific data vol. 2 (2015): 150021. doi: https://doi.org/10.1038/sdata.2015.21
Haigh ID, Wadey MP, Gallop SL, Loehr H, Nicholls RJ, Horsburgh K, Brown JM, Bradshaw E. A user-friendly database of coastal flooding in the United Kingdom from 1915-2014. Sci Data. 2015 May 12;2:150021. doi: 10.1038/sdata.2015.21. eCollection 2015. PMID: 25984352; PMCID: PMC4431528.
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Blended sea level anomaly fields with enhanced coastal coverage along the U.S. West Coast.

Scientific data

Risien CM, Strub PT.
PMID: 26927667
Sci Data. 2016 Mar 01;3:160013. doi: 10.1038/sdata.2016.13.

We form a new 'blended' data set of sea level anomaly (SLA) fields by combining gridded daily fields derived from altimeter data with coastal tide gauge data. Within approximately 55-70 km of the coast, the altimeter data are discarded...

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Risien CM, Strub PT. Blended sea level anomaly fields with enhanced coastal coverage along the U.S. West Coast. Sci Data. 2016;3:160013doi: 10.1038/sdata.2016.13.
Risien, C. M., & Strub, P. T. (2016). Blended sea level anomaly fields with enhanced coastal coverage along the U.S. West Coast. Scientific data, 3160013. https://doi.org/10.1038/sdata.2016.13
Risien, C M, and Strub, P T. "Blended sea level anomaly fields with enhanced coastal coverage along the U.S. West Coast." Scientific data vol. 3 (2016): 160013. doi: https://doi.org/10.1038/sdata.2016.13
Risien CM, Strub PT. Blended sea level anomaly fields with enhanced coastal coverage along the U.S. West Coast. Sci Data. 2016 Mar 01;3:160013. doi: 10.1038/sdata.2016.13. PMID: 26927667; PMCID: PMC4772653.
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Lilac and honeysuckle phenology data 1956-2014.

Scientific data

Rosemartin AH, Denny EG, Weltzin JF, Lee Marsh R, Wilson BE, Mehdipoor H, Zurita-Milla R, Schwartz MD.
PMID: 26306204
Sci Data. 2015 Jul 21;2:150038. doi: 10.1038/sdata.2015.38. eCollection 2015.

The dataset is comprised of leafing and flowering data collected across the continental United States from 1956 to 2014 for purple common lilac (Syringa vulgaris), a cloned lilac cultivar (S. x chinensis 'Red Rothomagensis') and two cloned honeysuckle cultivars...

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Rosemartin AH, Denny EG, Weltzin JF, et al. Lilac and honeysuckle phenology data 1956-2014. Sci Data. 2015;2:150038doi: 10.1038/sdata.2015.38.
Rosemartin, A. H., Denny, E. G., Weltzin, J. F., Lee Marsh, R., Wilson, B. E., Mehdipoor, H., Zurita-Milla, R., & Schwartz, M. D. (2015). Lilac and honeysuckle phenology data 1956-2014. Scientific data, 2150038. https://doi.org/10.1038/sdata.2015.38
Rosemartin, Alyssa H, et al. "Lilac and honeysuckle phenology data 1956-2014." Scientific data vol. 2 (2015): 150038. doi: https://doi.org/10.1038/sdata.2015.38
Rosemartin AH, Denny EG, Weltzin JF, Lee Marsh R, Wilson BE, Mehdipoor H, Zurita-Milla R, Schwartz MD. Lilac and honeysuckle phenology data 1956-2014. Sci Data. 2015 Jul 21;2:150038. doi: 10.1038/sdata.2015.38. eCollection 2015. PMID: 26306204; PMCID: PMC4520215.
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COVID-19 European regional tracker.

Scientific data

Naqvi A.
PMID: 34267216
Sci Data. 2021 Jul 15;8(1):181. doi: 10.1038/s41597-021-00950-7.

This Tracker presents data on daily COVID-19 cases at the sub-national level for 26 European countries from January 2020 till present. Country-level data sources are identified and processed to form a homogenized panel at the NUTS 3 or NUTS...

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Naqvi A. COVID-19 European regional tracker. Sci Data. 2021;8(1):181doi: 10.1038/s41597-021-00950-7.
Naqvi, A. (2021). COVID-19 European regional tracker. Scientific data, 8(1), 181. https://doi.org/10.1038/s41597-021-00950-7
Naqvi, Asjad. "COVID-19 European regional tracker." Scientific data vol. 8,1 (2021): 181. doi: https://doi.org/10.1038/s41597-021-00950-7
Naqvi A. COVID-19 European regional tracker. Sci Data. 2021 Jul 15;8(1):181. doi: 10.1038/s41597-021-00950-7. PMID: 34267216; PMCID: PMC8282658.
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A database for using machine learning and data mining techniques for coronary artery disease diagnosis.

Scientific data

Alizadehsani R, Roshanzamir M, Abdar M, Beykikhoshk A, Khosravi A, Panahiazar M, Koohestani A, Khozeimeh F, Nahavandi S, Sarrafzadegan N.
PMID: 31645559
Sci Data. 2019 Oct 23;6(1):227. doi: 10.1038/s41597-019-0206-3.

We present the coronary artery disease (CAD) database, a comprehensive resource, comprising 126 papers and 68 datasets relevant to CAD diagnosis, extracted from the scientific literature from 1992 and 2018. These data were collected to help advance research on...

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Alizadehsani R, Roshanzamir M, Abdar M, et al. A database for using machine learning and data mining techniques for coronary artery disease diagnosis. Sci Data. 2019;6(1):227doi: 10.1038/s41597-019-0206-3.
Alizadehsani, R., Roshanzamir, M., Abdar, M., Beykikhoshk, A., Khosravi, A., Panahiazar, M., Koohestani, A., Khozeimeh, F., Nahavandi, S., & Sarrafzadegan, N. (2019). A database for using machine learning and data mining techniques for coronary artery disease diagnosis. Scientific data, 6(1), 227. https://doi.org/10.1038/s41597-019-0206-3
Alizadehsani, R, et al. "A database for using machine learning and data mining techniques for coronary artery disease diagnosis." Scientific data vol. 6,1 (2019): 227. doi: https://doi.org/10.1038/s41597-019-0206-3
Alizadehsani R, Roshanzamir M, Abdar M, Beykikhoshk A, Khosravi A, Panahiazar M, Koohestani A, Khozeimeh F, Nahavandi S, Sarrafzadegan N. A database for using machine learning and data mining techniques for coronary artery disease diagnosis. Sci Data. 2019 Oct 23;6(1):227. doi: 10.1038/s41597-019-0206-3. PMID: 31645559; PMCID: PMC6811630.
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Real-world longitudinal data collected from the SleepHealth mobile app study.

Scientific data

Deering S, Pratap A, Suver C, Borelli AJ, Amdur A, Headapohl W, Stepnowsky CJ.
PMID: 33247114
Sci Data. 2020 Nov 27;7(1):418. doi: 10.1038/s41597-020-00753-2.

Conducting biomedical research using smartphones is a novel approach to studying health and disease that is only beginning to be meaningfully explored. Gathering large-scale, real-world data to track disease manifestation and long-term trajectory in this manner is quite practical...

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Deering S, Pratap A, Suver C, et al. Real-world longitudinal data collected from the SleepHealth mobile app study. Sci Data. 2020;7(1):418doi: 10.1038/s41597-020-00753-2.
Deering, S., Pratap, A., Suver, C., Borelli, A. J., Amdur, A., Headapohl, W., & Stepnowsky, C. J. (2020). Real-world longitudinal data collected from the SleepHealth mobile app study. Scientific data, 7(1), 418. https://doi.org/10.1038/s41597-020-00753-2
Deering, Sean, et al. "Real-world longitudinal data collected from the SleepHealth mobile app study." Scientific data vol. 7,1 (2020): 418. doi: https://doi.org/10.1038/s41597-020-00753-2
Deering S, Pratap A, Suver C, Borelli AJ, Amdur A, Headapohl W, Stepnowsky CJ. Real-world longitudinal data collected from the SleepHealth mobile app study. Sci Data. 2020 Nov 27;7(1):418. doi: 10.1038/s41597-020-00753-2. PMID: 33247114; PMCID: PMC7695828.
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European primary forest database v2.0.

Scientific data

Sabatini FM, Bluhm H, Kun Z, Aksenov D, Atauri JA, Buchwald E, Burrascano S, Cateau E, Diku A, Duarte IM, Fernández López ÁB, Garbarino M, Grigoriadis N, Horváth F, Keren S, Kitenberga M, Kiš A, Kraut A, Ibisch PL, Larrieu L, Lombardi F, Matovic B, Melu RN, Meyer P, Midteng R, Mikac S, Mikoláš M, Mozgeris G, Panayotov M, Pisek R, Nunes L, Ruete A, Schickhofer M, Simovski B, Stillhard J, Stojanovic D, Szwagrzyk J, Tikkanen OP, Toromani E, Volosyanchuk R, Vrška T, Waldherr M, Yermokhin M, Zlatanov T, Zagidullina A, Kuemmerle T.
PMID: 34404811
Sci Data. 2021 Aug 17;8(1):220. doi: 10.1038/s41597-021-00988-7.

Primary forests, defined here as forests where the signs of human impacts, if any, are strongly blurred due to decades without forest management, are scarce in Europe and continue to disappear. Despite these losses, we know little about where...

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Sabatini FM, Bluhm H, Kun Z, et al. European primary forest database v2.0. Sci Data. 2021;8(1):220doi: 10.1038/s41597-021-00988-7.
Sabatini, F. M., Bluhm, H., Kun, Z., Aksenov, D., Atauri, J. A., Buchwald, E., Burrascano, S., Cateau, E., Diku, A., Duarte, I. M., Fernández López, �. �. B., Garbarino, M., Grigoriadis, N., Horváth, F., Keren, S., Kitenberga, M., Kiš, A., Kraut, A., Ibisch, P. L., Larrieu, L., Lombardi, F., Matovic, B., Melu, R. N., Meyer, P., Midteng, R., Mikac, S., Mikoláš, M., Mozgeris, G., Panayotov, M., Pisek, R., Nunes, L., Ruete, A., Schickhofer, M., Simovski, B., Stillhard, J., Stojanovic, D., Szwagrzyk, J., Tikkanen, O. P., Toromani, E., Volosyanchuk, R., Vrška, T., Waldherr, M., Yermokhin, M., Zlatanov, T., Zagidullina, A., & Kuemmerle, T. (2021). European primary forest database v2.0. Scientific data, 8(1), 220. https://doi.org/10.1038/s41597-021-00988-7
Sabatini, Francesco Maria, et al. "European primary forest database v2.0." Scientific data vol. 8,1 (2021): 220. doi: https://doi.org/10.1038/s41597-021-00988-7
Sabatini FM, Bluhm H, Kun Z, Aksenov D, Atauri JA, Buchwald E, Burrascano S, Cateau E, Diku A, Duarte IM, Fernández López ÁB, Garbarino M, Grigoriadis N, Horváth F, Keren S, Kitenberga M, Kiš A, Kraut A, Ibisch PL, Larrieu L, Lombardi F, Matovic B, Melu RN, Meyer P, Midteng R, Mikac S, Mikoláš M, Mozgeris G, Panayotov M, Pisek R, Nunes L, Ruete A, Schickhofer M, Simovski B, Stillhard J, Stojanovic D, Szwagrzyk J, Tikkanen OP, Toromani E, Volosyanchuk R, Vrška T, Waldherr M, Yermokhin M, Zlatanov T, Zagidullina A, Kuemmerle T. European primary forest database v2.0. Sci Data. 2021 Aug 17;8(1):220. doi: 10.1038/s41597-021-00988-7. PMID: 34404811; PMCID: PMC8371020.
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A resource to explore the discovery of rare diseases and their causative genes.

Scientific data

Ehrhart F, Willighagen EL, Kutmon M, van Hoften M, Curfs LMG, Evelo CT.
PMID: 33947870
Sci Data. 2021 May 04;8(1):124. doi: 10.1038/s41597-021-00905-y.

Here, we describe a dataset with information about monogenic, rare diseases with a known genetic background, supplemented with manually extracted provenance for the disease itself and the discovery of the underlying genetic cause. We assembled a collection of 4166...

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Ehrhart F, Willighagen EL, Kutmon M, et al. A resource to explore the discovery of rare diseases and their causative genes. Sci Data. 2021;8(1):124doi: 10.1038/s41597-021-00905-y.
Ehrhart, F., Willighagen, E. L., Kutmon, M., van Hoften, M., Curfs, L. M. G., & Evelo, C. T. (2021). A resource to explore the discovery of rare diseases and their causative genes. Scientific data, 8(1), 124. https://doi.org/10.1038/s41597-021-00905-y
Ehrhart, Friederike, et al. "A resource to explore the discovery of rare diseases and their causative genes." Scientific data vol. 8,1 (2021): 124. doi: https://doi.org/10.1038/s41597-021-00905-y
Ehrhart F, Willighagen EL, Kutmon M, van Hoften M, Curfs LMG, Evelo CT. A resource to explore the discovery of rare diseases and their causative genes. Sci Data. 2021 May 04;8(1):124. doi: 10.1038/s41597-021-00905-y. PMID: 33947870; PMCID: PMC8096966.
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The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes.

Scientific data

Kelly J, Knottenbelt W.
PMID: 25984347
Sci Data. 2015 Mar 31;2:150007. doi: 10.1038/sdata.2015.7. eCollection 2015.

Many countries are rolling out smart electricity meters. These measure a home's total power demand. However, research into consumer behaviour suggests that consumers are best able to improve their energy efficiency when provided with itemised, appliance-by-appliance consumption information. Energy...

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Kelly J, Knottenbelt W. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci Data. 2015;2:150007doi: 10.1038/sdata.2015.7.
Kelly, J., & Knottenbelt, W. (2015). The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Scientific data, 2150007. https://doi.org/10.1038/sdata.2015.7
Kelly, Jack, and Knottenbelt, William. "The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes." Scientific data vol. 2 (2015): 150007. doi: https://doi.org/10.1038/sdata.2015.7
Kelly J, Knottenbelt W. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci Data. 2015 Mar 31;2:150007. doi: 10.1038/sdata.2015.7. eCollection 2015. PMID: 25984347; PMCID: PMC4432654.
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Event-related potential datasets based on a three-stimulus paradigm.

GigaScience

Vareka L, Bruha P, Moucek R.
PMID: 25671095
Gigascience. 2014 Dec 12;3(1):35. doi: 10.1186/2047-217X-3-35. eCollection 2014.

BACKGROUND: The event-related potentials technique is widely used in cognitive neuroscience research. The P300 waveform has been explored in many research articles because of its wide applications, such as lie detection or brain-computer interfaces (BCI). However, very few datasets...

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Vareka L, Bruha P, Moucek R. Event-related potential datasets based on a three-stimulus paradigm. Gigascience. 2014;3(1):35doi: 10.1186/2047-217X-3-35.
Vareka, L., Bruha, P., & Moucek, R. (2014). Event-related potential datasets based on a three-stimulus paradigm. GigaScience, 3(1), 35. https://doi.org/10.1186/2047-217X-3-35
Vareka, Lukas, et al. "Event-related potential datasets based on a three-stimulus paradigm." GigaScience vol. 3,1 (2014): 35. doi: https://doi.org/10.1186/2047-217X-3-35
Vareka L, Bruha P, Moucek R. Event-related potential datasets based on a three-stimulus paradigm. Gigascience. 2014 Dec 12;3(1):35. doi: 10.1186/2047-217X-3-35. eCollection 2014. PMID: 25671095; PMCID: PMC4322487.
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Spatiotemporal dataset on Chinese population distribution and its driving factors from 1949 to 2013.

Scientific data

Wang L, Chen L.
PMID: 27377410
Sci Data. 2016 Jul 05;3:160047. doi: 10.1038/sdata.2016.47.

Spatio-temporal data on human population and its driving factors is critical to understanding and responding to population problems. Unfortunately, such spatio-temporal data on a large scale and over the long term are often difficult to obtain. Here, we present...

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Wang L, Chen L. Spatiotemporal dataset on Chinese population distribution and its driving factors from 1949 to 2013. Sci Data. 2016;3:160047doi: 10.1038/sdata.2016.47.
Wang, L., & Chen, L. (2016). Spatiotemporal dataset on Chinese population distribution and its driving factors from 1949 to 2013. Scientific data, 3160047. https://doi.org/10.1038/sdata.2016.47
Wang, Lizhe, and Chen, Lajiao. "Spatiotemporal dataset on Chinese population distribution and its driving factors from 1949 to 2013." Scientific data vol. 3 (2016): 160047. doi: https://doi.org/10.1038/sdata.2016.47
Wang L, Chen L. Spatiotemporal dataset on Chinese population distribution and its driving factors from 1949 to 2013. Sci Data. 2016 Jul 05;3:160047. doi: 10.1038/sdata.2016.47. PMID: 27377410; PMCID: PMC4932880.
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Showing 1 to 12 of 50 entries