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Dataset Graph 2: Publication Type and Venue of Heiner Müller's Writings

Dataset for Graph 2. Data collected based on the Heiner Müller Werkausgabe. Graph 2 shows the different publication types and publication venues that Heiner Müller used to publish his writings (essays, articles, speeches, etc.) from 1968 until 1998.

opencc-by-4.0Nov 2022View details →

New woody plant functional types and parameters for the SAVANNA ecosystem model

New woody plant functional types (PFTs) are defined and parameterised for use in the SAVANNA ecosystem model (Coughenour, 1992, 1993). Supplementary material used in creating the PFTs and parameters are included. Details of the methods are available from the authors on request. The new woody PFTS are defined in terms of growth form, leaf size and defences in relation to large mammal herbivores. 1. shrub types are <4 m (Zizka et al., 2014), 2. fine-leaf types have bipinnate leaves with leptophyllous- or nanophyllous-sized leaflets (<225 mm2) according to Raunkaier’s leaf size classes (Fuller and Bakke, 1918) given that leaflets of compound leaves are separate morphological units analogous to simple leaves (Milla, 2012; Mo et al., 2022), 3. high chemical defence investment (CDI) types have either nitrogen:acid detergent fibre (N:ADF) <0.10 (Wallis et al., 2012) or condensed tannin (CT) >5% (Cooper and Owen-Smith, 1985) when expressed in sorghum tannin or leucocyanidin equivalents as determined by the acid-butanol assay, 4. all types, except fine_highcdi and fine_lowcdi, have the square-root of Charles-Dominique et al.'s (2017) "investment in structural defence" (ISD) < 13.

Centripetal migration in Drosophila ovary I: wild type timelapse & milestones pt1

Timelapse imaging data tracking inward migration of follicle cells during centripetal migration in Drosophila ovary.  Part of data supporting Figs 2, S1 of “Two phases for centripetal migration of Drosophila melanogaster follicle cells: initial ingression followed by epithelial migration” DOI: 10.1242/dev.200492      •Timelapse imaging data of wild type samples      •Quantitative analysis of specific milestone morphologies      •Analysis of distances between leader FC tips from opposite sides of egg chamber prior to Milestone VII in relevant samples

SSVEP database elicited by four visual stimuli types

<ul> <li>The database consists of 108 electroencephalographic files from 27 participants performing a 5-target selection task. </li> <li>Each participant performed one experimental session.</li> <li>All datasets were collected on channels PO7, PO3, POz, PO4, PO8, O1, Oz, and O2, according to the 10–20 EEG electrode placement standard.</li> <li>For the visual stimuli, we consider the On-Off and Checkerboard patterns with luminance modulated by rectangular and sinusoidal functions, resulting in a total of four types of visual stimuli: Checkerboard pattern with the rectangular modulated signal (Sxx-C.txt); Checkerboard pattern with sinusoidal modulated signal (Sxx-mC.txt); On-Off pattern with sinusoidal modulated signal (Sxx-mOO.txt) and On-Off pattern with rectangular modulated signal  (Sxx-OO.txt), where "Sxx" represents the subject number and 01 <= xx <= 27.</li> <li>In each file columns 1 to 8 correspond to EEG data and column 9 corresponds to the marks channel.</li> <li>Each phase of the experiment block is identified with a marker.</li> <li>The phases of one experiment trial are Fixation(201), Target Presentation(202), Preparation(203), Stimulation(101-105), and Rest(200).</li> <li>Marker numbers 101, 102, 103, 104, and 105, encodes de target frequency applied during the "Stimulation" stage in a trial. They are associated with the stimulation frequencies as follows: 101 - 24 Hz; 102 - 20 Hz; 103 - 15 Hz; 104 - 10.909 Hz and, 105 - 8.57 Hz</li> <li>Files can be easily accessible with EEG-dedicated MATLAB toolboxes, such as Fieldtrip and EEGLAB.</li> </ul>

opencc-by-4.0Sep 2022View details →

New Data Types in Data Management and Archiving [Webinar recording]

New Data Types in Data Management and Archiving workshop focused on the management, archiving and access to new types of data (NDTs), i.e. administrative, transactional and social media data. The program consisted of four presentations tackling various issues related to handling the NDTs in data repositories and sharing these data in the community of social researchers. Martin Vávra (CSDA) was speaking about current capacities among CESSDA SPs for handling NDTs, Brian Kleiner (FORS) was talking about the coordinated approach to handling NDTs CESSDA SPs. Yevhen Voronin (GESIS) gave a presentation about social media data sharing in social research and Pascal Jurgens (Johannes Gutenberg University Mainz) was speaking about Social Science in the Embattled Digital Age: Adversarial Creation, Use and Sharing of New Data Types. The speakers’ presentations were followed by the panel discussion, where audience members were encouraged to participate and brought in their own experiences of archivists, data managers and researchers. The event was a part of the CESSDA training activities. The video is available on the <a href="https://www.youtube.com/watch?v=j13GsqwDO2Q">CESSDA Training YouTube channel.</a>

Taxonomy, distribution and classification of ecosystem-types, integrating the recent IUCN function-based typology and local conceptualizations

1. Introduction: This dataset is a work in progress. It compiles data gathered on ecosystem-types and their distribution based on a series of field studies led by the author, in Seychelles and West and Central Africa (Senterre 2014, Senterre & Wagner 2014, Senterre 2016, Senterre et al. 2017, 2019, 2020, 2021a, 2022). The aims of this dataset are: a. To share in an explicit and transparent way data on proposed taxonomies of ecosystems, i.e. conceptualizations of ecosystem-types, including explicit ecosystem names and management of synonymies. b. To develop ecosystem red listing based on transparent and falsifiable distribution raw data, combining distribution modeling (maps) and in situ observation of individual stand occurrences. c. To illustrate in detail how to deal with ecosystem data following the approach described in Senterre et al. (2021b) (i.e. "ecosystemology" approach). d. To integrate the above approach with the newly developed function-based typology of ecosystems (Keith et al. 2022), therefore contributing to bridging the persistent gap between the global and the local scales in ecosystem descriptions and classifications.   2. Context and versions: This dataset was initially planned for publication on GBIF (Global Biodiversity Information Facility), as part of a project developed for the review of Key Biodiversity Areas in Seychelles: "Mainstreaming recent species and ecosystem distribution data into Key Biodiversity Areas assessments in Seychelles" (<a href="https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf">https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf</a>). In the first version of the GBIF dataset (<a href="https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf">https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf</a>), we proposed an analysis of the potential 'core' and 'extension' files available in GBIF for a publication of ecosystem-type names (and synonymies) and their corresponding occurrences recorded from field observations. This is an original analysis of taxonomic principles managed entirely at the scale of local observable objects, and their history of identifications or interpretations. Toward the end of the above-mentioned GBIF project, considering the limitations and gaps currently present in GBIF, it was decided to restrict the GBIF dataset to a simple 'metadata' entry and to publish the complete version of this dataset in Zenodo. This allows to include all tables needed, as well as all required fields without having to accommodate them within the limited GBIF structure (see metadata description on GBIF for more details). The fields of the tables published here are described in the GBIF metadata entry and in the ecosystemology paper (Senterre et al. 2021b).   3. New development on typology aspects: In addition, considering that the new IUCN global typology of ecosystems is now published (Keith et al. 2022), we have reviewed in detail the possibility of integration of ecosystems conceptualized using our ecosystemology approach within the new IUCN typology. The result of this analysis is being considered for a publication, and this Zenodo dataset would then be published in full (i.e. including all typology aspects) as supplementary materials. In the meantime, I would be happy to discuss any of these aspects with whoever is interested.   4. Access to ecosystem data for conservation actors: Finally, the actual data (published here) on ecosystem-types, their names, synonymies, classification, distribution, and red list status are compiled into a format that we designed to be useful to conservation actors in the form of interactive webpages (produced with R as shiny apps). This development is based on very limited resources, and the author is still quite new to R, so any help or feedback on ways to improve the scripts would be very much welcomed. The interactive page is available here (currently filtered to Seychelles' data only, although the dataset contains data beyond the Seychelles): https://shiny.bio.gov.sc/bioeco/ The R scripts are available on Github: https://github.com/bsenterre/ecosystemology   5. Tables contained in this dataset: a. Ecosystem taxonomy tables: ecoSpecies: Contains the list of all ecosystem-type names with their unique identifier. ecoOccurrences: Contains the list of individual stand occurrences, including ecosystem characters as standardized in Senterre et al. (2021b; i.e. virtual ecosystem specimen). ecoSpeciesProfiles: Contains basic metadata on ecosystem-types, such as their Red List evaluations. ecoIdentifications: Contains all the different interpretations/identifications (referring to the table ecoSpecies or to higher levels of classification, see below) made on the stands observed in the ecoOccurrences table.   b. Ecosystem typology tables (TO BE ADDED LATER): IUCNL3: This is just a transcription, as is, of the IUCN global typology version 2.1. IUCNL3BIOCrossover: This table defines and comments correspondences between BIOL2 (the level 2 of the typology used by us) and the IUCN typology L3 (level 3). BIOL2: This is a variation based on the IUCN typology, here our level 2. BIOL3: This is a variation based on the IUCN typology, here our level 3. BIOL4: This is a variation based on the IUCN typology, here our level 4. ecoGenus: This is a general type of stand (thus excluding any regional ecosystem connotation), defined at a local scale and never combined with any geographic connotation (see ecosystemology paper: Senterre et al. 2021b). ecoFamily: This is a generalized version of the ecoGenus (i.e. still excluding any regional, sub-regional or geographic aspect). ecoOrder: This is a further generalized version of the ecoGenus (see also Senterre et al. 2020). lifeZone: This is a basic and incomplete list of life zones as defined following the Holdridge (1967) approach, with some additional elements proposed in Senterre et al. (2021b).   6. Literature cited: Holdridge, L. R. 1967. Life zone ecology. Tropical Science Center, San Jose, Costa Rica. Keith, D. A., J. R. Ferrer-Paris, E. Nicholson, M. J. Bishop, B. A. Polidoro, E. Ramirez-Llodra, M. G. Tozer, J. L. Nel, R. Mac Nally, E. J. Gregr, K. E. Watermeyer, F. Essl, D. Faber-Langendoen, J. Franklin, C. E. R. Lehmann, A. Etter, D. J. Roux, J. S. Stark, J. A. Rowland, N. A. Brummitt, U. C. Fernandez-Arcaya, I. M. Suthers, S. K. Wiser, I. Donohue, L. J. Jackson, R. T. Pennington, T. M. Iliffe, V. Gerovasileiou, P. Giller, B. J. Robson, N. Pettorelli, A. Andrade, A. Lindgaard, T. Tahvanainen, A. Terauds, M. A. Chadwick, N. J. Murray, J. Moat, P. Pliscoff, I. Zager, and R. T. Kingsford. 2022. A function-based typology for Earth’s ecosystems. . Nature 610:513–518. doi:10.1038/s41586-022-05318-4. Senterre, B. 2014. Mapping habitat-types within the Hummingbird site at Dugbe (Liberia, West Africa). Consultancy Report, Missouri Botanical Garden. P. 56. https://doi.org/10.13140/RG.2.2.32628.48003. Senterre, B. 2016. Habitat-type ground-truthing and assessment of ecosystem conservation value in the Bel Air Alufer mining site (Guinea, West Africa), with recommendations for improving the draft map of land cover types. Consultancy Report, Missouri Botanical Garden, A study conducted for Alufer Mining Limited. P. 54. Senterre, B., E. Bidault, and T. Stévart. 2019. Identification et évaluation des écosystèmes menacés du Mont Nimba. Rapport de consultance, Missouri Botanical Garden (MBG), Africa and Madagascar Department. P. 106. https://doi.org/10.13140/RG.2.2.13242.93129. Senterre, B., E. Bidault, T. Stévart, and P. P. Lowry II. 2020. Assessment of Key Biodiversity Areas in the Lofa-Gola-Mano & Nimba complexes (West Africa) using ecosystem criteria. Final Report, Missouri Botanical Garden. P. 146. 10.13140/RG.2.2.17934.89924. Senterre, B., E. Bidault, T. Stévart, M. Wagner, and P. Lowry. 2017. Mapping habitat-types in south-east Kouilou (Republic of Congo). Consultancy Report, Missouri Botanical Garden (MBG), Africa and Madagascar Department, St. Louis, Missouri, USA. P. 163. Senterre, B., R. M. Bristol, G. Gendron, and E. Henriette. 2021a. Fine-tuning conservation priorities in Seychelles at the landscape scale, using global KBA guidelines with both species and ecosystem criteria. Consultancy Report, United Nations Development Programme, GOS/UNDP/GEF Programme Coordination Unit, Victoria, Seychelles. Senterre, B., P. P. Lowry II, E. Bidault, and T. Stévart. 2021b. Ecosystemology: a new approach toward a taxonomy of ecosystems. . Ecological Complexity 47:100945. doi:https://doi.org/10.1016/j.ecocom.2021.100945. Senterre, B., A.-H. Paradis, E. Bidault, T. Stévart, and P. P. Lowry II. 2022. Qualité et distribution des savanes montagnardes du Nimba. Rapport de consultance, Missouri Botanical Garden (MBG), Africa and Madagascar Department. P. 73. http://dx.doi.org/10.13140/RG.2.2.13433.34401. Senterre, B., and M. Wagner. 2014. Mapping Seychelles habitat-types on Mahé, Praslin, Silhouette, La Digue and Curieuse. Consultancy Report, Government of Seychelles, United Nations Development Programme, Victoria, Seychelles. P. 119. https://doi.org/10.13140/RG.2.1.4558.6009.

Global Crop Type Validation Data Set for ESA WorldCereal System

This dataset was created by using a new IIASA tool, called “Street Imagery validation” (<a href="https://svweb.cloud.geo-wiki.org/">https://svweb.cloud.geo-wiki.org/</a>) where users could check street level images (e.g., Google Street Level images, Mapillary etc.) and identify the crop type where it is possible. The advantage of this tool is that there are plenty of georeferenced images with dates, going back in time. The disadvantage is that users need to check plenty of images where only few will clearly show cropland fields that are mature enough to be identified. To make the data collection more efficient, we provided our experts with preliminary maps of points in agricultural areas where street level images are available for the year 2021. Then, the experts checked those locations in an opportunistic way. The dataset is completely independent from all the existing maps and the reference datasets. There are 3 main data records uploaded: <ol> <li>sv_croptype_poly.zip – an archive with a shapefile containing all the collected polygons with crop type information. Not all the polygons correspond to actual field boundaries.</li> <li>sv_croptype_validations.csv – a table with crop type observations with centroid coordinates in WGS84</li> <li>sv_worldcereal_validation.csv – a table with a subset of crop type observations used in validation of WorldCereal crop type maps for 2021.</li> </ol> Fields: <ul> <li>"id" – unique observation identifier;</li> <li>"imgSource" – source of imagery used for visual inspection;</li> <li>"imgLoc" – image location;</li> <li>"svImgDate" – image date;</li> <li>"imageIdKey" – image unique identifier;</li> <li>"submitedAt" – date of submission of crop type observation;</li> <li>"cropType"  - crop type observation;</li> <li>"irrType" – irrigation type;</li> <li>"x", "y" – centroids of submitted polygons in WGS84.</li> </ul>

RoadArrowORIEN: dataset of 6701 images (64x64 pixels) of straight arrow-type road markings and their azimuths

The dataset consisting of 6701 PNG images (64x64 pixels) of straight arrow type road markings with different dimensions and orientations, together with the angle it forms with respect to the vertical axis (azimuth), has been constructed in the framework of the SROADEX project to train a regression neural network to calculate the azimuth of other straight arrows that can be identified in high resolution aerial orthoimages. The dataset was created by labeling with the labelme tool the straight arrows on orthoimage tesserae of 256x256 pixels. After semantic labeling by drawing an arrow on the orthoimage, the data was automatically processed to calculate the azimuthal angle. The procedure followed is as follows: 1.- The vertices of the generated arrow-shaped polygon have been extracted. 2.- Clusters of nearby points have been generated, with a minimum of 2 points and the cluster with less points and the one with more points has been identified.  3.- For the clusters, the centroid has been generated, preserving the information of the number of vertices that define the cluster. 4.- Then a vector has been generated with origin in the centroid of the arrow with less vertices grouped and as end the centroid with the highest number of vertices. 5.- Finally, the azimuth of this vector with respect to the ordinate axis has been calculated,  The images have been automatically cropped with a constant size of 64x64 pixels taking an extension greater than the occupied of the arrow in the scene. The shared dataset consists of a compressed file with the images and a text file in CSV format with the names of the images and the normaliced [0..1] Azimutal angles of the straight arrows contained in the images.

Maximum temperature data from thermal safety assessment of type 21700 lithium-ion batteries with NMC, NCA and LFP cathodes by means of Accelerating Rate Calorimetry (ARC)

Data of safety investigation and thermal abuse behavior of commercial type 21700 LIB cells is provided. It has been acquired with Accelerating Rate Calorimetry (ARC), using a Thermal Hazard Technology type ES ARC. Moreover, thermal abuse was done by means of the so-called Heat-Wait-Seek (HWS) test, at different states of charge (SOC) from 0 to 100. Different cathode chemistries are compared (NMC, NCA and LFP), as well as for NCA chemistry, the high energy (HE) and high power (HP) cell design. For each cell, data includes the maximum temperature measured during thermal abuse at the surface on the center of the cell. Additionally, the mean value and standard deviation for each cell type and state of charge is provided. This data is supporting this article in the journal Batteries: <a href="https://doi.org/10.3390/batteries9050237">https://doi.org/10.3390/batteries9050237</a> Additional supporting material to this article are the exothermal data for thermal abuse, that are published here: <a href="https://doi.org/10.5281/zenodo.7707929">https://doi.org/10.5281/zenodo.7707929</a>

opencc-by-4.0Feb 2023View details →

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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All three types of otoliths of Cyprinus carpio (total length=30 cm); left/right asterisci and lapilli from top and bottom view, and one sagittus.

The image shows all three types of otoliths of Cyprinus carpio (total length=30 cm): left/right asterisci and lapilli from top and bottom view, and one sagittus.

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). 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opencc-by-4.0Jul 2023View details →

Common factor GWAS and TWAS output for nociplastic type pain

file: GSEM_commonFactorGWAS_COPC_6trait_30MAY2023.csv.gz description: Common factor GWAS output for GenomicSEM analyses of 6 COPC traits (see doi: https://doi.org/10.1101/2023.06.27.23291959) columns: SNP = rsID SNP identifier  CHR = chromosome BP = base pair position MAF = minor allele frequency A1 = effect allele A2 = other allele i = index (1 - n SNPs) lhs = left hand side of equation  op = equation operator (lavaan syntax) rhs = right hand side of equation est = effect size (beta) se_c = standard error of effect estimate Z_Estimate = Z value Pval_Estimate = p value of effect Q = Q (heterogeneity) value Q_df = degrees of freedom for Q Q_pval = Q p value fail = GSEM fail message if applicable warning = GSEM warning message if applicable  Z_smooth = smoothing parameter if applicable  N_estimate = N estimate    file: GSEM_commonFactorTWAS_COPC_6trait_30MAY2023.csv.gz description: Common factor TWAS output for GenomicSEM analyses of 6 COPC traits (see doi: https://doi.org/10.1101/2023.06.27.23291959) columns: Gene = ensembl gene ID  Panel = which model (tissue+gene) i.e. reference weights file HSQ = gene heritability  i = index (1 - n gene-tissue models) lhs = equation left hand side op = operator (lavaan syntax) rhs = equation right hand side est = association estimate (beta) se_c = standard error of beta Z_Estimate = Z value  Pval_Estimate = p value of association test Q = Q (heterogeneity) value Q_df = degrees of freedom for Q Q_pval = p value for Q fail = GSEM fail message if applicable  warning = GSEM warning message if applicable  tissue = tissue p_bonf_tissue = adjusted p value - bonferroni adjustment within tissue  p_fdr_tissue = adjusted p value - false discovery rate adjustment within tissue threshold_bonf_tissue = p value threshold for bonferroni adjustment within tissue  p_bonf_experiment = adjusted p value - bonferroni adjustment experiment-wide p_threshold_bonf_experiment = p value threshold (bonferroni, experiment-wide) Q_bonf_tissue = adjusted p value for Q, bonferroni within-tissue

opencc-by-4.0Jul 2023View details →

Relationships among the generated content type, task, AI technique, and application domain in the retrieved works

Relationships among the generated content type, task, AI technique, and application domain in the retrieved works. Part of the study "What do we mean by GenAI?"

opencc-by-4.0Jul 2023View details →

Number of works published over the years grouped by generated content type

Number of works published over the years grouped by generated content type. Part of the study "What do we mean by GenAI?"

openCC0Jul 2022View details →

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.

openCustomJan 2014View details →

Plant community typing (2009 update), Andrews Experimental Forest

Plant Communities of the HJ Andrews Experimental Forest (revised 2009). A total of 23 forest communities have been identified and characterized in a preliminary manner. Data used in formatting the classification had previously been collected on 300 reconnaissance plots located on the H. J. Andrews Forest and surrounding area. Vegetation classification was facilitated by similarity analysis and stand ordination procedures developed by Dr. Will Moir, formerly of Colorado State University. Results of stand ordination indicate the presence of strong moisture and temperature gradients along which forest stands array themselves. The forest communities recognized in this classification are listed in the following internal report: http://andrewsforest.oregonstate.edu/pubs/pdf/pub1741.pdf

openOpenFeb 2016View details →

Final seedling counts for invasive plants seeded at CPCRW on a variety of substrate types.

This dataset contains final seedling counts for invasive plants seeded at the CPCRW research (seeding and measurements taken summer 2012) site on a variety of substrate types.

openCC0Dec 2020View details →

Long-term record of wetted area, substrate type, and plant features in Sycamore Creek, Arizona, USA (2010-2020)

The primary objective of this project is to understand how long-term climate variability and change influence the structure and function of desert streams via effects on hydrologic disturbance regimes. Climate and hydrology are intimately linked in arid landscapes; for this reason, desert streams are particularly well suited for both observing and understanding the consequences of climate variability and directional change. Researchers try to (1) determine how climate variability and change over multiple years influence stream biogeomorphic structure (i.e., prevalence and persistence of wetland and gravel-bed ecosystem states) via their influence on factors that control vegetation biomass, and (2) compare interannual variability in within-year successional patterns in ecosystem processes and community structure of primary producers and consumers of two contrasting reach types (wetland and gravel-bed stream reaches). This dataset was collected to understand changes of biota patch cover in different sites and reaches of Sycamore Creek and the transition of different algal and plant communities in post-flood succession.