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zenodo44/100

Southern Hemisphere Lamb Weather Types from historical GCM experiments and various reanalyses

This dataset comprises six-hourly Lamb Weather Type (LWT) time series covering the period 1979-2005 for a) historical experiments run with 61 distinct GCMs from CMIP5 and 6 (specified in "get_historical_metadata.py" published at https://doi.org/10.5281/zenodo.4555367) and b) 3 distinct reanalyses (ERA-Interim, JRA-55 and ERA5, the latter extended to 2020). The LWT time series are provided on a 2.5º regular latitude-longitude grid covering the southern hemisphere between 30ºS and 70ºS. The full LWT approach covering 27 classes is applied and the corresponding results for the Northern Hemisphere were stored in a companion dataset at https://doi.org/10.5281/zenodo.4452080. The format of the files is netCDF-4, compressed with the netCDF Kitchen Sink command "ncks -4 -L 1". The Python code used to generate this dataset is available from https://doi.org/10.5281/zenodo.4555367   Note The LWT_SH.zip file contains all relevant data. Please ignore the separate netCDF files outside this zip file. These are old files that could not be deleted during the update from version 1 to 2 due to technical issues with Zenodo.   Historial Version 2 is a major dataset update featuring the following improvements: 1. The attributes from the netCDF source files "psl...nc" obtained from ESGF were copied into the files available here. These attributes are indicated with the prefix "udata...." (for "underlying data"). 2. All non-standard calenders from the underlying netCDF files from ESGF were converted into standard using the "xarray.Dataset.convert_calendar" function. The original calendar information was stored as additional netCDF attribute. 3. The "patch" method from Python's xesmf module was used to regrid the original psl data from the native GCM grid available from ESGF to the regular lat-lon 2.5° grid common to all applied GCMs and reanalyses. contact: Swen Brands, brandssf@ifca.unican.es   Principal Research Articles, Software and Complementary Datasets Associated with this Dataset Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and 6 models for regional climate studies in the Northern Hemisphere mid-to- high latitudes. Geoscientific Model Development, 15 (4), 1375–1411. doi: https://doi.org/10.5194/gmd-15-1375-2022 Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and 6 mod- els for regional climate studies in the northern hemisphere [data set]. Zenodo. doi: https://doi.org/10.5281/zenodo.4452080 Brands, S. (2022). Common error patterns in the regional atmospheric circulation simulated by the CMIP multi-model ensemble. Geophysical Research Letters, 49 (23), e2022GL101446. doi: https://doi.org/10.1029/2022GL101446 Brands, Swen, Tatebe, Hiroaki, Danek, Christopher, Fernández, Jesús, Swart, Neil C., Volodin, Evgeny, Kim, YoungHo, Collier, Mark, Bi, Dave, & Tongwen, Wu. (2022). Python code to calculate Lamb circulation types derived from historical CMIP simulations and reanalysis data. In Geoscientific Model Development: Vols. gmd-2020-418 (Version 4). Zenodo. https://doi.org/10.5281/zenodo.6390256 Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández, J. (2023). Auxiliary online material to Brands et al. (2023): A global climate model performance atlas for the Southern Hemisphere extratrop- ics based on regional atmospheric circulation patterns. figshare. doi: https://doi.org/10.6084/m9.figshare.22193443.v1 Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández, J. (2023b). Southern Hemisphere Lamb Weather Types from historical GCM experiments and various reanalyses (1.0) [data set]. Zenodo. doi: https://doi.org/10.5281/zenodo.7612988 Brands, S., Tatebe, H., Danek, C., Fernández, J., Swart, N., Volodin, E., . . . Tong- wen, W. (2023). GCM metadata archive get historical metadata.py (v1.1). Zenodo. doi: https://doi.org/10.5281/zenodo.7715383 Fernández-Granja, J. A., Brands, S., Bedia, J., Casanueva, A., & Fernández, J. (2023). Exploring the limits of the Jenkinson–Collison weather types clas- sification scheme: a global assessment based on various reanalyses. Climate Dynamics. doi: 10.1007/s00382-022-06658-7   References of the source GCMs and Early References of the Lamb Weather Typing Method Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., . . . Kristjánsson, J. E. (2013). The Norwegian Earth System Model, NorESM1-M – part 1: Description and basic evaluation of the physical climate. Geoscientific Model Development, 6 (3), 687–720. doi: 10.5194/gmd-6-687-2013 Bi, D., Dix, M., Marsland, S., O’Farrell, S., Sullivan, A., Bodman, R., . . . Heerde- gen, A. (2020). Configuration and spin-up of ACCESS-CM2, the new gener- ation Australian Community Climate and Earth System Simulator Coupled Model. Journal of Southern Hemisphere Earth Systems Science, 70 (1), 225- 251. doi: doi:10.1071/ES19040 Bi, D., Dix, M., Marsland, S. J., O’Farrell, S., Rashid, H., Uotila, P., . . . Puri, K. (2013). The ACCESS coupled model: description, control climate and evaluation. Australian Meteorological and Oceanographic Journal , 63 , 41-64. doi: 0.22499/2.6301.004 Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., . . . Vuichard, N. (2020). Presentation and evaluation of the IPSL-CM6A- LR climate model. Journal of Advances in Modeling Earth Systems, 12 (7), e2019MS002010. doi: 10.1029/2019MS002010 Cao, J., Wang, B., Yang, Y.-M., Ma, L., Li, J., Sun, B., . . . Wu, L. (2018). The NUIST Earth System Model (NESM) version 3: description and prelimi- nary evaluation. Geoscientific Model Development, 11 (7), 2975–2993. doi: 10.5194/gmd-11-2975-2018 Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S., . . . Navarra, A. (2019). Global mean climate and main patterns of variability in the CMCC- CM2 coupled model. Journal of Advances in Modeling Earth Systems, 11 (1), 185-209. doi: 10.1029/2018MS001369 Chylek, P., Li, J., Dubey, M. K., Wang, M., & Lesins, G. (2011). Observed and model simulated 20th century arctic temperature variability: Canadian Earth System Model CanESM2. Atmospheric Chemistry and Physics Discussions, 11 , 22893–22907. doi: 10.5194/acpd-11-22893-2011 Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., . . . Woodward, S. (2011). Development and evaluation of an Earth-System model – HadGEM2. Geoscientific Model Development, 4 (4),1051–1075.doi: 10.5194/gmd-4-1051-2011 Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., . . . Vitart, F. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc., 137 (656, Part a), 553-597. doi: 10.1002/qj.828 Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arneth, A., Arsouze, T., . . . Zhang, Q. (2021). The EC-Earth3 Earth System Model for the Coupled Model Intercomparison Project 6. Geoscientific Model Development Discussions, 2021 , 1–90. doi: 10.5194/gmd-2020-446 Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont, O., . . . Vuichard, N. (2013). Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim. Dyn., 40 (9-10), 2123-2165. doi: 10.1007/s00382-012-1636-1 Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John, J. G., . . . Zhao, M. (2020). The GFDL Earth System Model version 4.1 (GFDL- ESM 4.1): Overall coupled model description and simulation characteristics. Journal of Advances in Modeling Earth Systems, 12 (11), e2019MS002015. doi: https://doi.org/10.1029/2019MS002015 Dunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W., Shevli- akova, E., . . . Zadeh, N. (2012). GFDL’s ESM2 Global Coupled Climate- Carbon Earth System Models. Part I: Physical formulation and baseline simulation characteristics.Journal of Climate, 25 (19), 6646–6665. doi: https://doi.org/10.1175/JCLI-D-11-00560.1 Griffies, S., Winton, M., Donner, L., Horowitz, L., Downes, S., Farneti, R., . . . Zadeh, N. (2011). The GFDL-CM3 coupled climate model: Characteristics of the ocean and sea ice simulations. Journal of Climate, 24 , 3520-3544. doi: 10.1175/2011JCLI3964.1 Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Abe, M., . . . Kawamiya, M. (2020). Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks. Geoscientific Model Development, 13 (5), 2197–2244. doi: 10.5194/gmd-13-2197-2020 Hazeleger, W., Wang, X., Severijns, C., Briceag, S., Bintanja, R., Sterl, A., . . . van der Wiel, K. (2011). Ec-earth v2.2: Description and validation of a new seamless earth system prediction model.Climate Dynamics, 39 , 1-19. doi: 10.1007/s00382-011-1228-5 Held, I. M., Guo, H., Adcroft, A., Dunne, J. P., Horowitz, L. W., Krasting, J., . . . Zadeh, N. (2019). Structure and performance of GFDL’s CM4.0 climate model. Journal of Advances in Modeling Earth Systems, 11 (11), 3691-3727. doi: 10.1029/2019MS001829 Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., . . . Thépaut, J.-N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146 (730), 1999-2049. doi:https://doi.org/10.1002/qj.3803 Jones, P. D., Hulme, M., & Briffa, K. R. (1993). A comparison of Lamb circulation types with an objective classification scheme. International Journal of Clima- tology, 13 (6), 655-663. doi: https://doi.org/10.1002/joc.3370130606 Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell, G. L., . . . Yao, M.-S. (2020). GISS-E2.1: Configurations and climatology. Journal of Advances in Modeling Earth Systems, 12 (8), e2019MS002025. doi: 10.1029/2019MS002025 Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., . . . Taka- hashi, K. (2015). The JRA-55 Reanalysis: General specifications and basic characteristics. Journal of the Meteorological Society of Japan. Ser. II , 93 (1), 5-48. doi: 10.2151/jmsj.2015-001 Lamb, H. (1972). British Isles weather types and a register of daily sequence of cir- culation patterns, 1861-1971. Geophysical Memoir , 116 , 85pp. (HMSO) Lee, J., Kim, J., Sun, M.-A., Kim, B.-H., Moon, H., Sung, H. M., . . . Byun, Y.- H. (2019). Evaluation of the Korea Meteorological Administration Ad- vanced Community Earth-System model (K-ACE). Asia-Pacific Journal of Atmospheric Sciences, 56 , 381–395. doi: https://doi.org/10.1007/ Lee, W.-L., Wang, Y.-C., Shiu, C.-J., Tsai, I., Tu, C.-Y., Lan, Y.-Y., . . . Hsu, H.-H. (2020). Taiwan Earth System Model version 1: description and evaluation of mean state. Geoscientific Model Development, 13 (9), 3887–3904. doi: 10.5194/gmd-13-3887-2020 Li, L., Lin, P., Yu, Y.-Q., Zhou, T., Liu, L., Liu, J., . . . Qiao, F.-L. (2013). The Flexible Global Ocean-Atmosphere-Land System Model, Grid-point ver- sion 2: FGOALS-g2. Advances in Atmospheric Sciences, 30 , 543-560.doi: 10.1007/s00376-012-2140-6 Li, L., Yu, Y., Tang, Y., Lin, P., Xie, J., Song, M., . . . Wei, J. (2020). The Flex- ible Global Ocean-Atmosphere-Land System Model Grid-point version 3 (FGOALS-g3): Description and evaluation. Journal of Advances in Model- ing Earth Systems, 12 (9), e2019MS002012. doi: https://doi.org/10.1029/ 2019MS002012 Martin, T. H. D. T. G. M., Bellouin, N., Collins, W. J., Culverwell, I. D., Halloran, P. R., Hardiman, S. C., . . . Wiltshire, A. (2011). The HadGEM2 family of Met Office Unified Model climate configurations. Geoscientific Model Development, 4 (3), 723–757. doi: 10.5194/gmd-4-723-2011 Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., . . . Roeckner, E. (2019). Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and its response to increasing CO2. Journal of Advances in Modeling Earth Systems, 11 (4), 998-1038. doi: 10.1029/2018MS001400 Pak, G., Noh, Y., Lee, M.-I., Yeh, S.-W., Kim, D., Kim, S.-Y., . . . Kim, Y. H. (2021). Korea Institute of Ocean Science and Technology Earth System Model and its simulation characteristics. Ocean Science Journal , 56 , 18-45. doi: 10.1007/s12601-021-00001-7 Park, S., Shin, J., Kim, S., Oh, E., & Kim, Y. (2019). Global climate simulated by the Seoul National University Atmosphere Model version 0 with a unified convection scheme (SAM0-UNICON). Journal of Climate, 32 (10), 2917-2949. doi: 10.1175/JCLI-D-18-0796.1 Roberts, M., Baker, A., Blockley, E., Calvert, D., Coward, A., Hewitt, H., . . . Vi- dale, P. (2019). Description of the resolution hierarchy of the global coupled HadGEM3-GC3.1 model as used in CMIP6 HighResMIP experiments. Geosci- entific Model Development Discussions, 1-47. doi: 10.5194/gmd-2019-148 Schmidt, G. A., Kelley, M., Nazarenko, L., Ruedy, R., Russell, G. L., Aleinov, I., . . . Zhang, J. (2014). Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. Journal of Advances in Modeling Earth Systems, 6 (1), 141-184. doi: 10.1002/2013MS000265 Scoccimarro, E., Gualdi, S., Bellucci, A., Sanna, A., Giuseppe Fogli, P., Manzini, E., . . . Navarra, A. (2011). Effects of tropical cyclones on ocean heat transport in a high-resolution coupled general circulation model. Journal of Climate, 24 (16), 4368-4384. doi: 10.1175/2011JCLI4104.1 Seland, Ø., Bentsen, M., Seland Graff, L., Olivié, D., Toniazzo, T., Gjermundsen, A., . . . Schulz, M. (2020). The Norwegian Earth System Model, NorESM2 – evaluation of the CMIP6 DECK and historical simulations. Geoscientific Model Development, 2020 , 1–68. doi: 10.5194/gmd-2019-378 Semmler, T., Danilov, S., Gierz, P., Goessling, H. F., Hegewald, J., Hinrichs, C., . . . Jung, T. (2020). Simulations for CMIP6 with the AWI Climate Model AWI-CM-1-1. Journal of Advances in Modeling Earth Systems, 12 (9), e2019MS002009. doi: 10.1029/2019MS002009 Swapna, P., Koll, R., Aparna, K., Kulkarni, K., Ag, P., Ashok, K., . . . Goswami, B. N. (2015). The IITM Earth System Model: Transformation of a seasonal prediction model to a long term climate model. Bulletin of the American Meteorological Society, 96 , 1351–1367. doi: 10.1175/BAMS-D-13-00276.1 Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., Colin, J., . . . Madec, G. (2019). Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role of Earth system processes in present-day and future climate. Journal of Advances in Modeling Earth Systems, 11 (12), 4182-4227. doi: 10.1029/2019MS001791 Tamura, T., Ohshima, K. I., & Nihashi, S. (2008). Mapping of sea ice production for antarctic coastal polynyas. Geophysical Research Letters, 35 (7). doi: https:// doi.org/10.1029/2007GL032903 Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., . . . Ki- moto, M. (2019). Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geoscientific Model Development, 12 (7), 2727–2765. doi: 10.5194/gmd-12-2727-2019 Tegen, I., Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Bey, I., Schutgens, N., . . . Lohmann, U. (2019). The global aerosol–climate model ECHAM6.3– HAM2.3 – part 1: Aerosol evaluation. Geoscientific Model Development, 12 (4), 1643–1677. doi: 10.5194/gmd-12-1643-2019 Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., . . . Waldman, R. (2019). Evaluation of CMIP6 DECK experiments with CNRM-CM6-1. Journal of Advances in Modeling Earth Systems, 11 (7), 2177- 2213. doi: 10.1029/2019MS001683 Voldoire, A., Sanchez-Gomez, E., Salas y Melia, D., Decharme, B., Cassou, C., Sen- esi, S., . . . Chauvin, F. (2013). The CNRM-CM5.1 global climate model: description and basic evaluation. Clim. Dyn., 40 (9-10), 2091-2121. doi: 10.1007/s00382-011-1259-y Volodin, E., Diansky, N., & Gusev, A. (2010). Simulating present-day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations. Izvestiya, Atmospheric and Oceanic Physics, 46 , 414-431. doi: https://doi.org/10.1134/S000143381004002X Volodin, E., Mortikov, E., Kostrykin, S., Galin, V., Lykossov, V., Gritsun, A., . . . Iakovlev, N. (2017). Simulation of the present-day climate with the climate model INMCM5. Climate Dynamics, 49 , 3715–3734. doi: https://doi.org/10.1007/s00382-017-3539-7 Watanabe, M., Suzuki, T., O’ishi, R., Komuro, Y., Watanabe, S., Emori, S., . . . Kimoto, M. (2010). Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. Journal of Climate, 23 , 6312-6335. doi: 10.1175/2010JCLI3679.1 Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., . . . Kawamiya, M. (2011). MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geoscientific Model Development, 4 , 845-872. doi: 10.5194/gmd-4-845-2011 Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., . . . Liu, X. (2019). The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6. Geoscientific Model Development, 12 (4), 1573–1600. doi: 10.5194/gmd-12-1573-2019 Wu, T., Song, L., Li, W., Wang, Z., Zhang, H., Xin, X., . . . Zhou, M. (2014). An overview of BCC Climate System Model development and application for climate change studies. Acta Meteorologica Sinica, 28 , 34–56. doi: 10.1007/s13351-014-3041-7 Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., . . . Ishii, M. (2019). The Meteorological Research Institute Earth System Model version 2.0, MRI-ESM2.0: Description and basic evaluation of the physical component. Journal of the Meteorological Society of Japan. Ser. II , 97 (5), 931-965. doi: 10.2151/jmsj.2019-051 Yukimoto, S., Yoshimura, H., Hosaka, M., Sakami, T., Tsujino, H., Hirabara, M., . . . Kitoh, A. (2011). Meteorological Research Institute-Earth System Model version 1 (MRI-ESM1) — model description —.Technical Reports of the Meteorological Research Institute, 64 , 1-96. Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix, M., . . . Srbinovsky, J. (2020). The Australian Earth System Model: ACCESS- ESM1.5. Journal of Southern Hemisphere Earth Systems Science, 70 , 193-214. doi: https://doi.org/10.1071/ES19035

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zenodo44/100

Northern Hemisphere Lamb Weather Types from historical GCM experiments and various reanalyses

This dataset contains 6-hourly instantaneous discrete Lamb circulation type time series (Lamb 1972) on a 2.5 degrees longitude-latitude grid covering the northern hemisphere extratropics between 30ºN and 70ºN for the period 1979-2005 or longer. These "Lamb catalogues" were calculated upon SLP data from the historical experiments run with 61 distinct GCMs participating in the Coupled Model Intercomparison Project phases 5 and 6, and also from three distinct reanalyses (ERA5 extended to 2020 from version 5 onwards, ERA-Interim and JRA-55). For 13 out of the aforementioned 61 GCMs, 72 additional runs are provided to explore the role of internal model variability. For more information, please refer to the following article: Brands, S.: A circulation-based performance atlas of the CMIP5 and 6 models for regional climate studies in the Northern Hemisphere mid-to-high latitudes, Geosci. Model Dev., 15, 1375–1411, https://doi.org/10.5194/gmd-15-1375-2022, 2022. or contact: brandssf@ifca.unican.es Reference: Lamb, H.: British Isles Weather types and a register of daily sequence of circulation patterns, 1861-1971, Geophysical Memoir, 116, 85pp., HMSO, 1972. CAUTION: When unpacked, this dataset occupies 110 GB of your local disk space. Update information: Version 2 of this archive includes the model_source_attributes.txt file containing the "source" attributes stored in the netCDF files obtained from ESGF. This attribute provides details about the individual component models within the coupled model configurations used in CMIP5 and 6. Version 3 of this archive includes 10 new GCMs, two additional runs for CNRM-CM6-1 and an updated version of model_source_attributes.txt Version 3.1 includes README.txt, which explains the content of the files located in the tar.gz file. Version 4 further includes Lamb Weather Type catalogues for the ERA5 reanalysis and 4 additional GCMs. All files have been compressed individually.  The <model_source_attributes.txt> file is depreciated and no longer updated. It is replaced by the Python function <get_historical_metadata.py> available from https://doi.org/10.5281/zenodo.4555367. This function contains an exhaustive metadata archive of the 60 GCMs considered here. Version 4.1 The LWT catalogue for CMCC-CM2-HR4 is included for consistency with the respective Southern Hemisphere dataset published at https://doi.org/10.5281/zenodo.7612987 Version 5 is a major dataset update featuring the following improvements: 1. The attributes from the netCDF source files "psl...nc" obtained from ESGF were copied into the files available here. These attributes are indicated with the prefix "udata...." (for "underlying data"). 2. All non-standard calenders from the underlying netCDF files from ESGF were converted into standard using the "xarray.Dataset.convert_calendar" function. The original calendar information was stored as additional netCDF attribute. 3. The "patch" method from Python's xesmf module was used to regrid the original psl data from the native GCM grid available from ESGF to the regular lat-lon 2.5° grid common to all applied GCMs and reanalyses. contact: Swen Brands, brandssf@ifca.unican.es   Principal Research Articles, Software and Complementary Datasets Associated with this Dataset Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and 6 models for regional climate studies in the Northern Hemisphere mid-to- high latitudes. Geoscientific Model Development, 15 (4), 1375–1411. doi: https://doi.org/10.5194/gmd-15-1375-2022 Brands, S. (2022). A circulation-based performance atlas of the CMIP5 and 6 mod- els for regional climate studies in the northern hemisphere [data set]. Zenodo. doi: https://doi.org/10.5281/zenodo.4452080 Brands, S. (2022). Common error patterns in the regional atmospheric circulation simulated by the CMIP multi-model ensemble. Geophysical Research Letters, 49 (23), e2022GL101446. doi: https://doi.org/10.1029/2022GL101446 Brands, Swen, Tatebe, Hiroaki, Danek, Christopher, Fernández, Jesús, Swart, Neil C., Volodin, Evgeny, Kim, YoungHo, Collier, Mark, Bi, Dave, & Tongwen, Wu. (2022). Python code to calculate Lamb circulation types derived from historical CMIP simulations and reanalysis data. In Geoscientific Model Development: Vols. gmd-2020-418 (Version 4). Zenodo. https://doi.org/10.5281/zenodo.6390256 Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández, J. (2023). Auxiliary online material to Brands et al. (2023): A global climate model performance atlas for the Southern Hemisphere extratrop- ics based on regional atmospheric circulation patterns. figshare. doi: https://doi.org/10.6084/m9.figshare.22193443.v1 Brands, S., Fernández-Granja, J. A., Bedia, J., Casanueva, A., & Fernández, J. (2023b). Southern Hemisphere Lamb Weather Types from historical GCM experiments and various reanalyses (1.0) [data set]. Zenodo. doi: https://doi.org/10.5281/zenodo.7612988 Brands, S., Tatebe, H., Danek, C., Fernández, J., Swart, N., Volodin, E., . . . Tong- wen, W. (2023). GCM metadata archive get historical metadata.py (v1.1). Zenodo. doi: https://doi.org/10.5281/zenodo.7715383 Fernández-Granja, J. A., Brands, S., Bedia, J., Casanueva, A., & Fernández, J. (2023). Exploring the limits of the Jenkinson–Collison weather types clas- sification scheme: a global assessment based on various reanalyses. Climate Dynamics. doi: 10.1007/s00382-022-06658-7   References of the source GCMs and Early References of the Lamb Weather Typing Method Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., . . . Kristjánsson, J. E. (2013). The Norwegian Earth System Model, NorESM1-M – part 1: Description and basic evaluation of the physical climate. Geoscientific Model Development, 6 (3), 687–720. doi: 10.5194/gmd-6-687-2013 Bi, D., Dix, M., Marsland, S., O’Farrell, S., Sullivan, A., Bodman, R., . . . Heerde- gen, A. (2020). Configuration and spin-up of ACCESS-CM2, the new gener- ation Australian Community Climate and Earth System Simulator Coupled Model. Journal of Southern Hemisphere Earth Systems Science, 70 (1), 225- 251. doi: doi:10.1071/ES19040 Bi, D., Dix, M., Marsland, S. J., O’Farrell, S., Rashid, H., Uotila, P., . . . Puri, K. (2013). The ACCESS coupled model: description, control climate and evaluation. Australian Meteorological and Oceanographic Journal , 63 , 41-64. doi: 0.22499/2.6301.004 Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y., Bastrikov, V., . . . Vuichard, N. (2020). Presentation and evaluation of the IPSL-CM6A- LR climate model. Journal of Advances in Modeling Earth Systems, 12 (7), e2019MS002010. doi: 10.1029/2019MS002010 Cao, J., Wang, B., Yang, Y.-M., Ma, L., Li, J., Sun, B., . . . Wu, L. (2018). The NUIST Earth System Model (NESM) version 3: description and prelimi- nary evaluation. Geoscientific Model Development, 11 (7), 2975–2993. doi: 10.5194/gmd-11-2975-2018 Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S., . . . Navarra, A. (2019). Global mean climate and main patterns of variability in the CMCC- CM2 coupled model. Journal of Advances in Modeling Earth Systems, 11 (1), 185-209. doi: 10.1029/2018MS001369 Chylek, P., Li, J., Dubey, M. K., Wang, M., & Lesins, G. (2011). Observed and model simulated 20th century arctic temperature variability: Canadian Earth System Model CanESM2. Atmospheric Chemistry and Physics Discussions, 11 , 22893–22907. doi: 10.5194/acpd-11-22893-2011 Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., . . . Woodward, S. (2011). Development and evaluation of an Earth-System model – HadGEM2. Geoscientific Model Development, 4 (4),1051–1075.doi: 10.5194/gmd-4-1051-2011 Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., . . . Vitart, F. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc., 137 (656, Part a), 553-597. doi: 10.1002/qj.828 Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arneth, A., Arsouze, T., . . . Zhang, Q. (2021). The EC-Earth3 Earth System Model for the Coupled Model Intercomparison Project 6. Geoscientific Model Development Discussions, 2021 , 1–90. doi: 10.5194/gmd-2020-446 Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont, O., . . . Vuichard, N. (2013). Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Clim. Dyn., 40 (9-10), 2123-2165. doi: 10.1007/s00382-012-1636-1 Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John, J. G., . . . Zhao, M. (2020). The GFDL Earth System Model version 4.1 (GFDL- ESM 4.1): Overall coupled model description and simulation characteristics. Journal of Advances in Modeling Earth Systems, 12 (11), e2019MS002015. doi: https://doi.org/10.1029/2019MS002015 Dunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W., Shevli- akova, E., . . . Zadeh, N. (2012). GFDL’s ESM2 Global Coupled Climate- Carbon Earth System Models. Part I: Physical formulation and baseline simulation characteristics.Journal of Climate, 25 (19), 6646–6665. doi: https://doi.org/10.1175/JCLI-D-11-00560.1 Griffies, S., Winton, M., Donner, L., Horowitz, L., Downes, S., Farneti, R., . . . Zadeh, N. (2011). The GFDL-CM3 coupled climate model: Characteristics of the ocean and sea ice simulations. Journal of Climate, 24 , 3520-3544. doi: 10.1175/2011JCLI3964.1 Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Abe, M., . . . Kawamiya, M. (2020). Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks. Geoscientific Model Development, 13 (5), 2197–2244. doi: 10.5194/gmd-13-2197-2020 Hazeleger, W., Wang, X., Severijns, C., Briceag, S., Bintanja, R., Sterl, A., . . . van der Wiel, K. (2011). Ec-earth v2.2: Description and validation of a new seamless earth system prediction model.Climate Dynamics, 39 , 1-19. doi: 10.1007/s00382-011-1228-5 Held, I. M., Guo, H., Adcroft, A., Dunne, J. P., Horowitz, L. W., Krasting, J., . . . Zadeh, N. (2019). Structure and performance of GFDL’s CM4.0 climate model. Journal of Advances in Modeling Earth Systems, 11 (11), 3691-3727. doi: 10.1029/2019MS001829 Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., . . . Thépaut, J.-N. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146 (730), 1999-2049. doi:https://doi.org/10.1002/qj.3803 Jones, P. D., Hulme, M., & Briffa, K. R. (1993). A comparison of Lamb circulation types with an objective classification scheme. International Journal of Clima- tology, 13 (6), 655-663. doi: https://doi.org/10.1002/joc.3370130606 Kelley, M., Schmidt, G. A., Nazarenko, L. S., Bauer, S. E., Ruedy, R., Russell, G. L., . . . Yao, M.-S. (2020). GISS-E2.1: Configurations and climatology. Journal of Advances in Modeling Earth Systems, 12 (8), e2019MS002025. doi: 10.1029/2019MS002025 Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., . . . Taka- hashi, K. (2015). The JRA-55 Reanalysis: General specifications and basic characteristics. Journal of the Meteorological Society of Japan. Ser. II , 93 (1), 5-48. doi: 10.2151/jmsj.2015-001 Lamb, H. (1972). British Isles weather types and a register of daily sequence of cir- culation patterns, 1861-1971. Geophysical Memoir , 116 , 85pp. (HMSO) Lee, J., Kim, J., Sun, M.-A., Kim, B.-H., Moon, H., Sung, H. M., . . . Byun, Y.- H. (2019). Evaluation of the Korea Meteorological Administration Ad- vanced Community Earth-System model (K-ACE). Asia-Pacific Journal of Atmospheric Sciences, 56 , 381–395. doi: https://doi.org/10.1007/ Lee, W.-L., Wang, Y.-C., Shiu, C.-J., Tsai, I., Tu, C.-Y., Lan, Y.-Y., . . . Hsu, H.-H. (2020). Taiwan Earth System Model version 1: description and evaluation of mean state. Geoscientific Model Development, 13 (9), 3887–3904. doi: 10.5194/gmd-13-3887-2020 Li, L., Lin, P., Yu, Y.-Q., Zhou, T., Liu, L., Liu, J., . . . Qiao, F.-L. (2013). The Flexible Global Ocean-Atmosphere-Land System Model, Grid-point ver- sion 2: FGOALS-g2. Advances in Atmospheric Sciences, 30 , 543-560.doi: 10.1007/s00376-012-2140-6 Li, L., Yu, Y., Tang, Y., Lin, P., Xie, J., Song, M., . . . Wei, J. (2020). The Flex- ible Global Ocean-Atmosphere-Land System Model Grid-point version 3 (FGOALS-g3): Description and evaluation. Journal of Advances in Model- ing Earth Systems, 12 (9), e2019MS002012. doi: https://doi.org/10.1029/ 2019MS002012 Martin, T. H. D. T. G. M., Bellouin, N., Collins, W. J., Culverwell, I. D., Halloran, P. R., Hardiman, S. C., . . . Wiltshire, A. (2011). The HadGEM2 family of Met Office Unified Model climate configurations. Geoscientific Model Development, 4 (3), 723–757. doi: 10.5194/gmd-4-723-2011 Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R., . . . Roeckner, E. (2019). Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and its response to increasing CO2. Journal of Advances in Modeling Earth Systems, 11 (4), 998-1038. doi: 10.1029/2018MS001400 Pak, G., Noh, Y., Lee, M.-I., Yeh, S.-W., Kim, D., Kim, S.-Y., . . . Kim, Y. H. (2021). Korea Institute of Ocean Science and Technology Earth System Model and its simulation characteristics. Ocean Science Journal , 56 , 18-45. doi: 10.1007/s12601-021-00001-7 Park, S., Shin, J., Kim, S., Oh, E., & Kim, Y. (2019). Global climate simulated by the Seoul National University Atmosphere Model version 0 with a unified convection scheme (SAM0-UNICON). Journal of Climate, 32 (10), 2917-2949. doi: 10.1175/JCLI-D-18-0796.1 Roberts, M., Baker, A., Blockley, E., Calvert, D., Coward, A., Hewitt, H., . . . Vi- dale, P. (2019). Description of the resolution hierarchy of the global coupled HadGEM3-GC3.1 model as used in CMIP6 HighResMIP experiments. Geosci- entific Model Development Discussions, 1-47. doi: 10.5194/gmd-2019-148 Schmidt, G. A., Kelley, M., Nazarenko, L., Ruedy, R., Russell, G. L., Aleinov, I., . . . Zhang, J. (2014). Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. Journal of Advances in Modeling Earth Systems, 6 (1), 141-184. doi: 10.1002/2013MS000265 Scoccimarro, E., Gualdi, S., Bellucci, A., Sanna, A., Giuseppe Fogli, P., Manzini, E., . . . Navarra, A. (2011). Effects of tropical cyclones on ocean heat transport in a high-resolution coupled general circulation model. Journal of Climate, 24 (16), 4368-4384. doi: 10.1175/2011JCLI4104.1 Seland, Ø., Bentsen, M., Seland Graff, L., Olivié, D., Toniazzo, T., Gjermundsen, A., . . . Schulz, M. (2020). The Norwegian Earth System Model, NorESM2 – evaluation of the CMIP6 DECK and historical simulations. Geoscientific Model Development, 2020 , 1–68. doi: 10.5194/gmd-2019-378 Semmler, T., Danilov, S., Gierz, P., Goessling, H. F., Hegewald, J., Hinrichs, C., . . . Jung, T. (2020). Simulations for CMIP6 with the AWI Climate Model AWI-CM-1-1. Journal of Advances in Modeling Earth Systems, 12 (9), e2019MS002009. doi: 10.1029/2019MS002009 Swapna, P., Koll, R., Aparna, K., Kulkarni, K., Ag, P., Ashok, K., . . . Goswami, B. N. (2015). The IITM Earth System Model: Transformation of a seasonal prediction model to a long term climate model. Bulletin of the American Meteorological Society, 96 , 1351–1367. doi: 10.1175/BAMS-D-13-00276.1 Séférian, R., Nabat, P., Michou, M., Saint-Martin, D., Voldoire, A., Colin, J., . . . Madec, G. (2019). Evaluation of CNRM Earth System Model, CNRM-ESM2-1: Role of Earth system processes in present-day and future climate. Journal of Advances in Modeling Earth Systems, 11 (12), 4182-4227. doi: 10.1029/2019MS001791 Tamura, T., Ohshima, K. I., & Nihashi, S. (2008). Mapping of sea ice production for antarctic coastal polynyas. Geophysical Research Letters, 35 (7). doi: https:// doi.org/10.1029/2007GL032903 Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., . . . Ki- moto, M. (2019). Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geoscientific Model Development, 12 (7), 2727–2765. doi: 10.5194/gmd-12-2727-2019 Tegen, I., Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Bey, I., Schutgens, N., . . . Lohmann, U. (2019). The global aerosol–climate model ECHAM6.3– HAM2.3 – part 1: Aerosol evaluation. Geoscientific Model Development, 12 (4), 1643–1677. doi: 10.5194/gmd-12-1643-2019 Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., . . . Waldman, R. (2019). Evaluation of CMIP6 DECK experiments with CNRM-CM6-1. Journal of Advances in Modeling Earth Systems, 11 (7), 2177- 2213. doi: 10.1029/2019MS001683 Voldoire, A., Sanchez-Gomez, E., Salas y Melia, D., Decharme, B., Cassou, C., Sen- esi, S., . . . Chauvin, F. (2013). The CNRM-CM5.1 global climate model: description and basic evaluation. Clim. Dyn., 40 (9-10), 2091-2121. doi: 10.1007/s00382-011-1259-y Volodin, E., Diansky, N., & Gusev, A. (2010). Simulating present-day climate with the INMCM4.0 coupled model of the atmospheric and oceanic general circulations. Izvestiya, Atmospheric and Oceanic Physics, 46 , 414-431. doi: https://doi.org/10.1134/S000143381004002X Volodin, E., Mortikov, E., Kostrykin, S., Galin, V., Lykossov, V., Gritsun, A., . . . Iakovlev, N. (2017). Simulation of the present-day climate with the climate model INMCM5. Climate Dynamics, 49 , 3715–3734. doi: https://doi.org/10.1007/s00382-017-3539-7 Watanabe, M., Suzuki, T., O’ishi, R., Komuro, Y., Watanabe, S., Emori, S., . . . Kimoto, M. (2010). Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. Journal of Climate, 23 , 6312-6335. doi: 10.1175/2010JCLI3679.1 Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., . . . Kawamiya, M. (2011). MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments. Geoscientific Model Development, 4 , 845-872. doi: 10.5194/gmd-4-845-2011 Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., . . . Liu, X. (2019). The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6. Geoscientific Model Development, 12 (4), 1573–1600. doi: 10.5194/gmd-12-1573-2019 Wu, T., Song, L., Li, W., Wang, Z., Zhang, H., Xin, X., . . . Zhou, M. (2014). An overview of BCC Climate System Model development and application for climate change studies. Acta Meteorologica Sinica, 28 , 34–56. doi: 10.1007/s13351-014-3041-7 Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., . . . Ishii, M. (2019). The Meteorological Research Institute Earth System Model version 2.0, MRI-ESM2.0: Description and basic evaluation of the physical component. Journal of the Meteorological Society of Japan. Ser. II , 97 (5), 931-965. doi: 10.2151/jmsj.2019-051 Yukimoto, S., Yoshimura, H., Hosaka, M., Sakami, T., Tsujino, H., Hirabara, M., . . . Kitoh, A. (2011). Meteorological Research Institute-Earth System Model version 1 (MRI-ESM1) — model description —.Technical Reports of the Meteorological Research Institute, 64 , 1-96. Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix, M., . . . Srbinovsky, J. (2020). The Australian Earth System Model: ACCESS- ESM1.5. Journal of Southern Hemisphere Earth Systems Science, 70 , 193-214. doi: https://doi.org/10.1071/ES19035

opencc-by-4.0Mar 2023View details →

edi44/100

Understanding the Influences of Forest Type, Cover Board Type and Weather on Salamanders

Salamanders are vital bioindicators that function to support a terrestrial forest ecosystem. The continuous loss of amphibian species and their habitat can have profound impacts on terrestrial systems. In terrestrial environments, salamanders use natural cover for refuge, foraging, and maintaining moisture; however, artificial cover has commonly been used to survey and conserve these species. The objective of this study was to assess terrestrial salamander preference for natural versus artificial coverboards in relation to forest stands in two successional stages located within the James H. Barrow Biological Field Station (Hiram, Ohio). Ten artificial (particle board, 30 x 33 cm) and ten natural (white ash, 30 x 30 cm) coverboards were placed in two 50 m parallel transects arranged 2 m apart within transitional and mature forests. Surveys were conducted weekly between the second week of September and the second week of November from 2018 to 2021. Average weakly precipitation and max temperature were recorded. Both abundance and species richness were significantly higher under natural coverboards and in the transitional forest. There were also correlation between species richness and abundance with daily max temperature and weakly precipitation. 678 individuals across five species were found: Eastern Red-Backed Salamander, Spotted Salamander, Four-Toed Salamander, Red-Spotted Newt, and Northern Two-Lined Salamander. Eastern Red-Backed Salamanders were the most abundant species within both mature and transitional forests. Natural coverboards may be a better method to survey terrestrial salamanders because artificial coverboards are comprised of wood chippings, wax and adhesives which may alter soil permeability for less favorable conditions.

openCC0Jul 2022View details →

zenodo40/100

Figure 3 in Investigating the influence of habitat type and weather conditions on the population dynamics of land snails Vertigo angustior Jeffreys, 1830 and Vertigo moulinsiana (Dupuy, 1849). A case study from western Poland

Figure 3. Diagram of one-way analysis of covariance test comparing a logarithmized number of individuals of Vertigo angustior (F = 92.16; p <0.01) and Vertigo moulinsiana (F = 8.165; p <0.01) in the Ilanka and Pliszka sites in 2009. Middle line: mean; box range: standard error; whiskers: standard deviation.

opencc-by-4.0Feb 2016View details →

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Figure 2 in Investigating the influence of habitat type and weather conditions on the population dynamics of land snails Vertigo angustior Jeffreys, 1830 and Vertigo moulinsiana (Dupuy, 1849). A case study from western Poland

Figure 2. Precipitation in the studied sites in consecutive months of 2009; dashed bars – sampling months. (B) and (C) Abundance of individuals: juveniles (white bars) and adults (black bars) of Vertigo angustior (B) and Vertigo moulinsiana (C) in each sampling event in the Ilanka and Pliszka sites in 2009.

opencc-by-4.0Feb 2016View details →

zenodo36/100

Data for GMD article: "Towards an improved treatment of cloud-radiation interaction in weather and climate models: exploring the potential of the Tripleclouds method for various cloud types using libRadtran 2.0.4"

Dataset for the publication by Nina Črnivec and Bernhard Mayer: "Towards an improved treatment of cloud-radiation interaction in weather and climate models: exploring the potential of the Tripleclouds method for various cloud types using libRadtran 2.0.4" submitted to Geoscientific Model Development in 2020. The repository contains data for stratocumulus, cirrus and cumulonimbus cloud case studies. It also contains MYSTIC benchmark radiation data (including atmosphering heating rate and net surface flux) for the aforementioned cloud cases. See README for additional information and description of data files.

opencc-by-4.0Dec 2020View details →

zenodo32/100

Dataset for the study "Deep Learning-Based Precipitation Simulation for Different Types of Weather Events in Eastern China"

This dataset contains the data and codes used in the study "Deep Learning-Based Precipitation Simulation for Different Types of  Weather Events in Eastern China". The resources will be updated continously.

opencc-by-4.0Jan 2024View details →

dryad32/100

Data from: Butterfly community ecology: the influences of habitat type, weather patterns, and dominant species in a temperate ecosystem

Open the record for dataset details and reuse information.

publicOct 2012View details →

dryad32/100

Data from: Free-ranging bats alter thermoregulatory behavior in response to reproductive stage, roost type, and weather

Open the record for dataset details and reuse information.

publicMay 2021View details →

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Figure 1 in Investigating the influence of habitat type and weather conditions on the population dynamics of land snails Vertigo angustior Jeffreys, 1830 and Vertigo moulinsiana (Dupuy, 1849). A case study from western Poland

Figure 1. (A) Precipitation in the studied sites in consecutive months of 2008; dashed bars – sampling months. (B) and (C) Abundance of individuals: juveniles (white bars) and adults (black bars) of Vertigo angustior (B) and Vertigo moulinsiana (C) in each sampling event in the Ilanka and Pliszka sites in 2008.

opencc-by-4.0Feb 2016View details →

zenodo28/100

UKCP data on daily precipitation, jet diagnostics and weather types

Open the record for dataset details and reuse information.

opencc-by-4.0Oct 2023View details →

ScienceDex guides

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record