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zenodo44/100

Gravity modeling of the Alpine lithosphere affected by magmatism based on seismic tomography

The Southern Alpine regions have been affected by several magmatic and volcanic events between the Paleozoic and the Tertiary. This activity has undoubtedly had an important effect on the density distribution and structural setting at lithosphere scale. Combining the information from gravity field and a high-resolution seismic tomography has been carried out a new 3D lithosphere density model of the Alpine region.

opencc-by-4.0Jun 2020View details →

zenodo44/100

Isotopes and related data associated with water tracing with environmental DNA in a high-Alpine catchment

Isotopes and related data associated with water tracing with environmental DNA in a high-Alpine catchment Prepared by Natalie Ceperley, February 2020.   All methods associated with this data are available in the manuscript: Elvira Mächler, Anham Salyani, Jean-Claude Walser, Annegret Larsen, Bettina Schaefli, Florian Altermatt, and Natalie Ceperley.  2019.  Water tracing with environmental DNA in a high-Alpine catchment, Hydrology and Earth System Sciences. https://doi.org/10.5194/hess-2019-551.  Related data sets are and will be published in the Vallon de Nant Community on Zenodo. Associated sequencing data are publicly available on European Nucleotide Archive (Mächler et al., 2020).  All isotope data analyzed in the laboratory of Torsten W. Vennemann at the University of Lausanne.    All Files:     ▪    NaN - No measurement or sample     ▪    Details regarding measurement are available in paper or supplement.   Files:  1)    climate_hydro_2017_daily.csv      ⁃    16 columns:          ⁃    1. day of year with January 1, 2017 = 1         ⁃    2-5. Q: daily mean, min, max, and baseflow discharge as measured at outlet (location ER/MR), in liters / day          ⁃    6. P: mean mm of rain across catchment per day         ⁃    7. SR: total solar radiation per day in W/hr/m2 as median of 4 meteorological stations         ⁃    8-10. SCA: mean, min, and max snow covered area on days with satellite imagery available for whole catchment area, in %         ⁃    11-13. water temperature, mean, min, and max, at outlet (location ER/MR), in degrees C         ⁃    14-16. air temperature, mean, min, and max at 4 meteorological stations, in degrees C 2)    delta-18-O_permil.csv      ⁃    stable isotopes of water (delta 18-O) in per mil     ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 3)    delta-2-H_permil.csv      ⁃    stable isotopes of water (delta 2-H) in per mil     ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 4)    dqdt_outlet_prev48hrs.csv     -    dq/dt determined at the outlet for the previous 48 hours at sampling moment (TimeOfSamples_HR.csv) for each sampling site     ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 5)    ednasamplecount.csv      -    this is the tally of samples (1 sample includes 4 replicates)     ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 6)    electricalconductivity_instrument.csv      ⁃    Code:              108 - post-analyzed using a glass bodied 6 mm probe in the laboratory (Jenway  4510, Staffordshire, UK).              102 - hand measurement with WTW (multi-3510 with a  IDS-tetracon-925, Xylem Analytics, Germany)     ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 7)    electricalconductivity_uScm.csv      -    this is the electrical conductivity in micro siemens per cm, according to the instruments coded in electricalconductivity_instrument.csv     ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 8)    LC-excess.csv      -     this is the line control execss from the meteoric water line as determined by the samples in the file: precipitationistopemetadata.csv     ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 9)    locations.csv      ⁃    Location codes used in other files.      -    Coordinates in CH1903 / LV03 and WGS 84 (lat/lon). Elevation in m. asl.  10)    precipitationisotopemetadata.csv      -     This is the sampling information for the isotope data that was used to calculate the meteoric water line.      -    The full data set will become available in a subsequent publication on Zenodo linked to the same community.      -    4 columns:          -    1. code: rain (1) or snow (2)         -    2. collection date and time         -    3. elevation in m. asl.          -    4. in the case of rain, this is the depth of collection in mm (area normalized volume), in the case of snow, this is the mean depth below the surface that the sample was taken from in cm.      ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 11)    sampledates.csv      -    These are the sample dates in day, month, year and day of year corresponding to the rows in other files 12)    stationlocations.csv     -     These are the locations of four meteorological stations and discharge measurement station.      -    Coordinates in CH1903 / LV03 and WGS 84 (lat/lon). Elevation in m. asl.  13)    TimeOfSamples_HR.csv      -    This is the time of the sample in hours and decimals correspond to minutes past hour     ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 14)    watertemperature_degC.csv      ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)     -     measure in degrees C     -     instrument in watertemperature_instrument.csv 15)    watertemperature_instrument.csv      ⁃    Code:              1 = hand measurement with WTW (multi-3510 with a  IDS-tetracon-925, Xylem Analytics, Germany)             2 = HOBO Pendant Temperature/Light Data Logger 64K - UA-002-64", Onset (Bourne, MA, USA)             3 = Continually logging WTW (IDS-tetracon-325, Xylem Analytics, Germany)             4 = Continually logging (10min) HOBO U24-001 Conductivity, Onset (Bourne, MA, USA)      ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)

opencc-by-4.0Dec 2018View details →

zenodo44/100

Global Alpine Treeline Elevational Transects

Elevational transects within alpine treeline ecotones worldwide.

opencc-by-4.0Feb 2024View details →

zenodo44/100

Data for "Hatching phenology is lagging behind an advancing snowmelt pattern in a high-alpine bird"

Abstract To track peaks in resource abundance, temperate-zone animals use predictive environmental cues to rear their offspring when conditions are most favourable. However, climate change threatens the reliability of such cues when an animal and its resource respond differently to a changing environment. This is especially problematic in alpine environments, where climate warming exceeds the Holarctic trend and may thus lead to rapid asynchrony between peaks in resource abundance and periods of increased resource requirements such as reproductive period of high-alpine specialists. We therefore investigated interannual variation and long-term trends in the breeding phenology of a high-alpine specialist, the white-winged snowfinch, Montifringilla nivalis, using a 20-year dataset from Switzerland. We found that two thirds of broods hatched during snowmelt. Hatching dates positively correlated with April and May precipitation, but changes in mean hatching dates did not coincide with earlier snowmelt in recent years. Our results offer a potential explanation for recently observed population declines already recognisable at lower elevations. We discuss non-adaptive phenotypic plasticity as potential causes for the asynchrony between changes in snowmelt and hatching dates of snowfinches, but the underlying causes are subject to further research.

opencc-by-4.0Sep 2021View details →

zenodo44/100

PIANO (Penetration and Interruption of Alpine Foehn) – flux station data set

ABSTRACT This resource comprises meteorological and turbulence data from four flux stations operated during the PIANO (Penetration and Interruption of Alpine Foehn) field campaign. The campaign took place in and around Innsbruck, Austria, during autumn and early winter 2017. The goal of the PIANO campaign was to study south foehn events, in particular the interaction between cold air pools and foehn, the mechanisms by which foehn can break through to reach the valley floor and the processes affecting the subsequent breakdown of foehn. This dataset provides near-surface turbulence observations (including surface fluxes obtained using the eddy covariance technique), along with radiation and soil measurements, as well as meteorological information. DATA SET DESCRIPTION 1. Spatial coverage and locations Three eddy covariance (EC) stations were operated at grassland sites during the PIANO campaign. One station (‘EC_South’) was installed in the Wipp Valley near to the village of Patsch, south of the city of Innsbruck. Two stations were installed in the Inn Valley, one to the east of Innsbruck in the region of Thaur (‘EC_East’) and one to the west of Innsbruck at Innsbruck Airport (‘EC_West’). Data from a fourth EC station at the Innsbruck Atmospheric Observatory (IAO, Karl et al. (2020)) in the centre of Innsbruck (‘EC_Centre’) was also used. Precise station co-ordinates are provided in the data files. Three of the stations were located on grassland surrounded by mixed agricultural fields: the two stations in the Inn Valley (EC_East, EC_West) were installed on the fairly flat valley floor, while the site in the Wipp Valley (EC_South) gently sloped downwards to the west. During the campaign the vegetation was generally short at 5-10 cm. As far as possible, sites were selected to have a clear fetch for at least a few hundred metres. All three grassland sites experienced snow cover during winter. The urban station (EC_Centre) is a long-term site installed above roof level and representative of the surrounding neighbourhood close to the city centre of Innsbruck. 2. Temporal coverage The temporal coverage of the datasets for the PIANO campaign are as follows: • EC_West: 15 Sep 2017 - 31 Dec 2017 • EC_South: 08 Sep 2017 – 15 Dec 2017 • EC_East: 13 Oct 2017 – 15 Dec 2017 • EC_Centre: 1 Sep 2017 – 31 Dec 2017 The timeseries for EC_East begins later than the other sites because electrical interference thought to be from a nearby transmitter meant there was no useable flux data for the first month. The site was relocated on 13 October 2017 (no data is included before this date). Repeated theft of the batteries at EC_East resulted in gaps for the last few days of the dataset in December 2017. Due to issues with remote data collection, data availability at EC_West is low in September 2017. The PIANO campaign took place during autumn and early winter 2017 but the EC_West station was operated for longer (until 22 May 2018 after which use of the site was no longer permitted) as it provided a useful rural comparison station for the urban measurements (Karl et al., 2020; Ward et al., submitted). Data for 1 January – 22 May 2018 are available from the first author on request. Data collection at the long-term EC_Centre/IAO site began in spring 2017 and is ongoing. 3. Instrument details At EC_West a closed-path eddy covariance system (CPEC200, Campbell Scientific) provided fast response measurements of the three wind components, temperature, water vapour mixing ratio and carbon dioxide mixing ratio. At EC_East and EC_South a sonic anemometer (CSAT3B, Campbell Scientific) and krypton hygrometer (KH20, Campbell Scientific) provided fast response measurements of the three wind components, temperature and water vapour. These fast data were logged at 20 Hz (CR6, Campbell Scientific). All three stations were equipped with a four-component radiometer (CNR4, Kipp and Zonen) to provide incoming and outgoing shortwave and longwave radiation. Meteorological measurements included air temperature and humidity (Rotronic HC2A-S3, mounted in an actively ventilated radiation shield Rotronic RS12T), atmospheric pressure (Campbell CS100, mounted inside the logger box) and precipitation (ARG100 tipping bucket gauge, Campbell Scientific). Soil instruments comprised two soil heat flux plates at 0.05 m depth (HFP01, Hukseflux), two soil temperature sensors (107, Campbell Scientific) at 0.02 and 0.04 m depth and a soil probe (ACC-SEN-SDI, Acclima) providing soil moisture and soil temperature at 0.05 m depth. At each site, the fast-response anemometer and gas analyser were mounted on a tripod at around 2.5 m above ground, while the radiometer and temperature-humidity probe were slightly lower, at around 2.0 m (exact sensor heights are provided in the data files). At EC_Centre a closed-path eddy covariance system (CPEC200, Campbell Scientific) provided fast response measurements of the three wind components, temperature, water vapour mixing ratio and carbon dioxide mixing ratio at 10 Hz (CR3000, Campbell Scientific) measured at 42.8 m above ground level on a lattice mast installed on top of a university building. A four-component radiometer (CNR4, Kipp and Zonen) provided incoming and outgoing shortwave and longwave radiation and air temperature and humidity are also measured (Rotronic HC2A-S3, mounted in a ventilated radiation shield). Atmospheric pressure is measured by a pressure sensor mounted inside one of the electronics boxes supplied as part of the CPEC200 (EC100, Campbell Scientific). No soil or precipitation measurements were made at the urban station. 4. Data processing The fast-response eddy covariance data were processed to 30-min statistics following standard procedures using EddyPro version 7.0.7 (LI-COR Biosciences, 2021). These include despiking of raw data, time-lag compensation using maximum covariance, double coordinate rotation (meaning the 30-min mean vertical wind speed is forced to zero), simple block averaging (i.e. no filtering was applied), humidity correction of sonic temperature (Schotanus et al., 1983), and spectral corrections at low frequencies (Moncrieff et al., 2004) and high frequencies (after Fratini et al. (2012) for the closed-path CPEC200 data and Moncrieff et al. (1997) for the krypton hygrometer data). Oxygen (Tanner et al., 1993; van Dijk et al., 2003) and density (Webb et al., 1980) corrections were also applied at the sites with krypton hygrometers. Automated calibration (zero and span for carbon dioxide and zero for water vapour) was performed for the CPEC instruments once per day at EC_West and twice per day at EC_Centre. In addition to the standard processing described above, gust speeds were calculated from the sonic data. First the instantaneous horizontal wind speed was calculated (neglecting any vertical component). A 3-s running mean of the horizontal wind speed was then obtained, and the gust speed taken as the maximum of this 3-s running mean over a 1-min averaging interval. The dissipation rate of turbulent kinetic energy was obtained from the fast-response measurements of the three wind components (u, v, w) as follows. First, spectra were calculated for u, v and w using evenly spaced logarithmic frequency bins. The inertial subrange was identified as the region around 1 Hz where a local linear fit to the spectral slope was within ±20% of the expected -5/3 slope. The dissipation rate was calculated for each frequency bin in the identified inertial subrange according to Kolmogorov theory (e.g. Kaimal and Finnigan, 1994), using a value of 0.55 for u and 0.73 for v and w for the Kolmogorov inertial subrange constants, and the mean value over the frequency bins was used to provide the dissipation rate for u, v, and w for each 30-min period. Further discussion can be found in Ward et al. (in prep.). Quality control removed data during times of power outage and instrument malfunction and data adversely affected by rainfall (all KH20 data during rainfall were removed). To exclude any potential effects of turbulence distortion, data were removed when the wind direction was within ±10° of the mounting structure. Data falling outside physically reasonable thresholds were removed, including times when the rotation angle exceeded 45°. Stationarity tests following Foken and Wichura (1996) were applied with a threshold of 100 (i.e. data were excluded when the difference between 5-min and 30-min statistics exceeded 100%). For the meteorological, radiation and soil data, quality control removed data during times of power outage and instrument malfunction (including when dew on the radiometer adversely affected readings). 5. Data file structure Two files in netCDF format are provided containing processed and quality-controlled data: • PIANO_EC_MetData_QC_1min_v1-00.nc containing the meteorological, radiation and soil data for each site at 1-min resolution. This file also contains horizontal wind speed (before co-ordinate rotation), wind direction and gust speed for each site at 1-min resolution. • PIANO_EC_FluxData_QC_30min_v1-00.nc containing processed statistics and fluxes for each site at 30-min resolution. There are also quicklook plots (provided in PNG format, monthly and for the whole period) showing the data contained in these files. Four sets of files in ASCII format are provided containing the fast (10/20 Hz) eddy covariance data for each site for every 30-minute period. These files are timestamped with the time corresponding to the end of the period and are named: • PIANO_EC_FastData_SITENAME_yyyymmdd_HHMM.csv. These sets of files are provided as a single .zip folder for each site which is named according to the site. All timestamps are given in UTC (in seconds since 00:00 UTC 01 January 1970) and denote the end of the averaging period. The following variables can be found in the MetData file: air temperature (ta), relative humidity (rh), atmospheric pressure (pa), precipitation (prec), soil temperature (ts1, ts2, ts3), soil volumetric water content (vwc), soil heat flux from each heat flux plate (shf1, shf2), incoming shortwave radiation (swin), outgoing shortwave radiation (swout), incoming longwave radiation (lwin), outgoing longwave radiation (lwout), wind speed (wspeed, i.e. vector average horizontal wind speed before double rotation), wind direction (wdir) and gust speed (gust). The following variables can be found in the FluxData file: friction velocity (ustar), sensible heat flux (h), latent heat flux (le), carbon dioxide flux (fco2), stability parameter (zeta), turbulent kinetic energy (tke), wind speed (wspeed, i.e. vector average wind speed after double rotation), wind direction (wdir), unrotated vertical wind velocity (wunrot, i.e. before double rotation), the standard deviation of the wind components and temperature (sigu, sigv, sigw, sigt), and dissipation rate of turbulent kinetic energy calculated from u, v and w spectra (epu, epv, epw). The following variables can be found in the RawData files: unrotated lateral, longitudinal and vertical wind components (in m s-1), temperature (in degree C), water vapour concentration (supplied for EC_West and EC_Centre as the mixing ratio (in mmol m-1) and supplied for EC_South and EC_East as the absolute humidity (g m-3) and carbon dioxide mixing ratio (in μmol mol-1) for EC_West and EC_Centre. Note that the absolute value of the water vapour concentration from the krypton hygrometers should not be used. These lateral, longitudinal and vertical wind components are as measured in the co-ordinate system of the sonic anemometers and the angle of installation of the sonic needed to convert to north-south east-west co-ordinates is given in the FluxData file. 6. Publications Data from these flux stations have been included in multiple publications as part of the PIANO project (Haid et al., 2020; Haid et al., 2021; Muschinski et al., 2021; Umek et al., 2021; Umek et al., submitted) as well as publications as part of a related study on turbulent exchange in complex environments (Ward et al., in prep.; Ward et al., submitted). 7. Contact Contact helen.ward(at)uibk.ac.at for any questions regarding the data set. 8. Acknowledgements The PIANO campaign was supported by the Austrian Science Fund (FWF) and the Weiss Science Foundation under Grant P29746-N32. Collection of this dataset was also supported by an FWF Lise Meitner project (M2244-N32) and a research stipend from Innsbruck University. Measurements at IAO are supported by the Bundesministerium für Wissenschaft, Forschung und Wirtschaft (Hochschulraum-Strukturmittel grant), the European Commission for funding ALP-AIR within FP7-PEOPLE and the FWF (P30600_NBL, P33701-N). The PIANO campaign was also supported by KIT IMK-IFU, Austro Control GmbH, Zentralanstalt für Meteorologie und Geodynamik (ZAMG), the Hydrographic Service of Tyrol, Innsbrucker Kommunalbetriebe AG (IKB), Bergisel Betriebsgesellschaft m.b.H., Innsbrucker Nordkettenbahnen Betriebs GmbH, T-Mobile Austria GmbH, Unser Lagerhaus Warenhandelsgesellschaft, PEMA Immobilien GmbH, HTL Anichstraße, Hilton Innsbruck, TINETZ-Tiroler Netze GmbH, Land Tirol, and the communities Patsch and Völs. 9. References Foken T, Wichura B (1996) Tools for quality assessment of surface-based flux measurements. Agric. For. Meteorol. 78: 83-105 doi: 10.1016/0168-1923(95)02248-1 Fratini G, Ibrom A, Arriga N, Burba G, Papale D (2012) Relative humidity effects on water vapour fluxes measured with closed-path eddy-covariance systems with short sampling lines. Agric. For. Meteorol. 165: 53-63 doi: 10.1016/j.agrformet.2012.05.018 Haid M, Gohm A, Umek L, Ward HC, Muschinski T, Lehner L, Rotach MW (2020) Foehn–cold pool interactions in the Inn Valley during PIANO IOP2. Q. J. R. Meteorol. Soc. 146: 1232-1263 doi: 10.1002/qj.3735 Haid M, Gohm A, Umek L, Ward HC, Rotach MW (2021) Cold-air pool processes in the Inn Valley during foehn: A comparison of four cases during PIANO. Boundary Layer Meteorology doi: 10.1007/s10546-021-00663-9 Kaimal JC, Finnigan JJ (1994) Atmospheric Boundary Layer Flows: Their structure and management. Oxford University Press, 289 pp. Karl T et al. (2020) Studying urban climate and air quality in the Alps - The Innsbruck Atmospheric Observatory. Bull. Amer. Meteorol. Soc. doi: 10.1175/BAMS-D-19-0270.1 LI-COR Biosciences (2021) Eddy Covariance Processing Software - version 7.0.7, Available at www.licor.com/EddyPro. Moncrieff JB, Clement R, Finnigan JJ, Meyers T (2004) Averaging, detrending and filtering of eddy covariance time series. In: X Lee, Massman WJ and Law BE (Editors), Handbook of Micrometeorology: a guide for surface flux measurements. Moncrieff JB et al. (1997) A system to measure surface fluxes of momentum, sensible heat, water vapour and carbon dioxide. Journal of Hydrology 188-199: 589-611 Muschinski T, Gohm A, Haid M, Umek L, Ward HC (2021) Spatial heterogeneity of the Inn Valley Cold Air Pool during south foehn: Observations from an array of temperature. Meteorol. Z. 30: 153-168 doi: 10.1127/metz/2020/1043 Schotanus P, Nieuwstadt FTM, Bruin HAR (1983) Temperature measurement with a sonic anemometer and its application to heat and moisture fluxes. Bound.-Layer Meteor. 26: 81-93 doi: 10.1007/bf00164332 Tanner B, Swiatek E, Greene J (1993) Density fluctuations and use of the krypton hygrometer in surface flux measurements. Management of irrigation and drainage systems: integrated perspectives. American Society of Civil Engineers, New York, NY: 945-952 Umek L, Gohm A, Haid M, Ward HC, Rotach MW (2021) Large eddy simulation of foehn-cold pool interactions in the Inn Valley during PIANO IOP2. Quart J Roy Meteorol Soc 147: 944-982 doi: 10.1002/qj.3954 Umek L, Gohm A, Haid M, Ward HC, Rotach MW (submitted) Influence of grid resolution of large-eddy simulations on foehn-cold pool interaction. Quart J Roy Meteorol Soc van Dijk A, Kohsiek W, de Bruin HAR (2003) Oxygen Sensitivity of Krypton and Lyman-α Hygrometers. J. Atmos. Ocean. Technol. 20: 143-151 doi: 10.1175/1520-0426(2003)020<0143:osokal>2.0.co;2 Ward HC, Rotach MW, Gohm A, Graus M, Karl T, Haid M, Umek L, Muschinski T (submitted) Energy and mass exchange at an urban site in mountainous terrain – the Alpine city of Innsbruck. Atmos. Chem. Phys. Ward HC, Rotach MW, Graus M, Karl T, Gohm A, Umek L, Haid M (in prep.) Turbulence characteristics at an urban site in highly complex terrain. Webb EK, Pearman GI, Leuning R (1980) Correction of flux measurements for density effects due to heat and water-vapor transfer. Q. J. R. Meteorol. Soc. 106: 85-100

opencc-by-4.0Dec 2021View details →

zenodo44/100

The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers

These datasets are part of the submitted paper for the LREC2022 conference entitled: "The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers" The various data sources, as well as the methodology, are explained in detail in the research paper which will be available soon. ALPIN stands for Austrian Language Polarity in Newspapers. The dictionary consists of three different parts which were merged together: <ul> <li>Austrian Media Corpus: AMC (AMC_v1.0.csv)</li> <li>STANDARD posts: STP (STP_v1.0.csv)</li> <li>Austriacisms: AUT (AUT_v1.0.csv)</li> </ul> Austrian Media Corpus (AMC) (Ransmayr et al., 2017) & STANDARD posts (STP) (Schabus et al., 2017) rely on the SPLM algorithm as used in SentiDraw (Sharma & Dutta 2021). Austriacisms (AUT) was generated by using the Best-Worst scaling (BWS) (Kiritchenko and Mohammad, 2017b). The AUT list was collected from the “Variantenwörterbuch des Deutschen” (Ammon et al., 2016) (thereby only selecting those words that only surface in Austrian German and in no other variety of German) and an austriacism list of Wikipedia (https://de.wikipedia.org/wiki/Liste_von_Austriazismen). The scores are scaled to the interval [-1, 1] using the min-max-abs scaling, ranging from negative to positive. References: Sharma, S. S., & Dutta, G. (2021). SentiDraw: Using star ratings of reviews to develop domain specific sentiment lexicon for polarity determination. Information Processing & Management, 58(1), 102412. Kiritchenko, S. and Mohammad, S. M. (2017b). Capturing reliable fine-grained sentiment associations by crowdsourcing and best-worst scaling. Schabus, D., Skowron, M., & Trapp, M. (2017). One Million Posts: A Data Set of German Online Discussions. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1241–1244. https://doi.org/10.1145/3077136.3080711 Ransmayr, J., Mörth, K., & Ďurčo, M. (2017). AMC (Austrian Media Corpus). In Korpusbasierte Forschungen zum österreichischen Deutsch. In Digitale Methoden der Korpusforschung in Österreich (= Veröffentlichungen zur Linguistik und Kommunikationsforschung Nr. 30) (pp. 27–38). Verlag der Österreichischen Akademie der Wissenschaften. Ammon, U., Bickel, H., & Ebner, J. (2016). Variantenwörterbuch des Deutschen : die Standardsprache in Österreich, der Schweiz, Deutschland, Liechtenstein, Luxemburg, Ostbelgien und Südtirol sowie Rumänien, Namibia und Mennonitensiedlungen. Walter de Gruyter.

opencc-by-4.0Jan 2022View details →

zenodo44/100

DNA sequence and taxonomic gap analyses to quantify the coverage of aquatic cyanobacteria and eukaryotic microalgae in reference databases: Results of a survey in the Alpine region

This dataset has been prepared as part of the Interreg Alpine Space project Eco-AlpsWater (ASP569) - Innovative Ecological Assessment and Water Management Strategy for the Protection of Ecosystem Services in Alpine Lakes and Rivers, <a href="https://www.alpine-space.eu/projects/eco-alpswater/en/home">https://www.alpine-space.eu/projects/eco-alpswater/en/home</a> Individual archives include 16S rRNA (cyanobacteria) and 18S rRNA (microalgae) FASTA sequences and associated blastn results obtained from the high throughput sequencing of plankton and biofilm bulk/eDNA samples collected in 2019 in 37 lakes and 22 rivers across the Alpine region. These are supporting files for the paper by Salmaso et al., 2022. DNA sequence and taxonomic gap analyses to quantify the coverage of aquatic cyanobacteria and eukaryotic microalgae in reference databases: Results of a survey in the Alpine region. Science of the Total Environment, in press.

opencc-by-4.0Apr 2022View details →

zenodo44/100

Data for: A severe landslide event in the Alpine foreland under possible future climate and land-use changes

Data underlying manuscript and supplementary figures of the corresponding publication, as well as the scripts to conduct the final analyses.

opencc-by-4.0Feb 2022View details →

zenodo44/100

CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) + meteorological parameters in alpine grasslands at Nivolet Plain, Gran Paradiso National Park, 2020 (IGG-CNR-CZO@NIVOLET)

CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) measured at Nivolet Plain, Gran Paradiso National Park, Italy, in a high-altitude Alpine grassland environment (about 2500-2700 m.a.s.l.) using the closed portable flux chamber method during the 2020 vegetative season (July-October), approximately twice a month. NEE is measured with a transparent chamber, while ER with a dark chamber (transparent chamber shaded with a cloth). Data represent the average values and the corresponding standard deviations obtained from five sites at different altitudes and soil substrates. Each average value is obtained as a mean over a set of 20 point-measurements for each site and each sampling date. Flux data are complemented by measurements of soil temperature and soil volumetric water content, air temperature, air RH, and solar radiance. During the measurement, air is pumped from the chamber to an IR gas analyzer (IRGA) and then injected again in the chamber. The CO2 concentration inside the chamber is measured for about 90 seconds and then the rate of concentration change is linearly interpolated (over 60s) to obtain the flux measurements. A detailed description of the sampling method can be found in Magnani et al. (2020). Instrumentation used: accumulation chambers (height: 31.5 cm; area of the base: 363 cm2), LI-COR LI-840 & LI-850 IR spectrophotometers, stainless-steel collars (inserted into the soil to a depth of about 1 cm), portable meteorological stations (pyranometer LSI Lastem DPA053, thermohygrometer LSI Lastem DMA672.1), pt100 soil temperature sensors, SM150T soil moisture sensor.

opencc-by-4.0Apr 2022View details →

zenodo44/100

CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) + meteorological parameters in alpine grasslands at Nivolet Plain, Gran Paradiso National Park, 2021 (IGG-CNR-CZO@NIVOLET)

CO2 Net Ecosystem Exchange (NEE) and Ecosystem Respiration (ER) measured at Nivolet Plain, Gran Paradiso National Park, Italy, in a high-altitude Alpine grassland environment (about 2500-2700 m.a.s.l.) using the closed portable flux chamber method during the 2021 vegetative season (July-October), approximately twice a month. NEE is measured with a transparent chamber, while ER with a dark chamber (transparent chamber shaded with a cloth). Data represent the average values and the corresponding standard deviations obtained from five sites at different altitudes and soil substrates. Each average value is obtained as a mean over a set of 20 point-measurements for each site and each sampling date. Flux data are complemented by measurements of soil temperature and soil volumetric water content, air temperature, air RH, and solar radiance. During the measurement, air is pumped from the chamber to an IR gas analyzer (IRGA) and then injected again in the chamber. The CO2 concentration inside the chamber is measured for about 90 seconds and then the rate of concentration change is linearly interpolated (over 60s) to obtain the flux measurements. A detailed description of the sampling method can be found in Magnani et al. (2020). Instrumentation used: accumulation chambers (height: 31.5 cm; area of the base: 363 cm2), LI-COR LI-840 & LI-850 IR spectrophotometers, stainless-steel collars (inserted into the soil to a depth of about 1 cm), portable meteorological stations (pyranometer LSI Lastem DPA053, thermohygrometer LSI Lastem DMA672.1), pt100 soil temperature sensors, SM150T soil moisture sensor.

opencc-by-4.0Apr 2022View details →

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Whole-lake metabolism in arctic-alpine lakes

This dataset contains daily and seasonal averages of whole-lake metabolism in 43 arctic-alpine Swedish lakes, as well as meteorological and lake physical conditions. Metabolism was estimated based on the free-water dissolved oxygen (DO) method following the maximum likelihood estimation approach by Solomon et al. (2013, Limnology & Oceanography) and using scripts by Windslow et al. 2016 (Inland Waters; R package LakeMetabolizer). The dataset is described in a manuscript submitted to Limnology and Oceanography. Changes in version 1.01 relative to original version: We corrected negligible mistakes in the metabolism data (third decimal of seasonal mean gross primary production, ecosystem respiration and net ecosystem production).

opencc-by-4.0Feb 2022View details →

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Allopatric and sympatric diversification within roach (Rutilus rutilus) of large pre‐alpine lakes

This is the data for the study entitled "Allopatric and sympatric diversification within roach (Rutilus rutilus) of large pre‐alpine lakes" published in the Journal of Evolutionary Biology <a href="https://doi.org/10.1111/jeb.13502">https://doi.org/10.1111/jeb.13502 .</a> The dataset consists of three files: Morphology.txt Morphology data from seven Swiss lakes. Given are the individual ID, the respective lake, the habitat classification, grouped habitat classification, length of each fish (mm), and the raw x and y coordinates for 11 landmarks. Geometric morphometric landmarks were set as follow: 1) anterior tip of snout, 2) anterior tip of lower jaw, 3) anterior, and 4) posterior point of operculum, 5) junction where the dorsolateral part of the head and body fuse, anterior insertion points of the 6) pectoral, 7) pelvic, and 8) anal fin, 9) ventral and 10) dorsal junction of the caudal peduncle and tail, 11) anterior insertion of the dorsal fin.   Stable_isotopes.txt Morphology data from five Swiss lakes. Given are the individual ID, lake, baseline corrected delta 13C values.   Stable_isotopes_baselines.txt Morphology data from five Swiss lakes. Given are the lake, the tissue used, delta 13C values.

opencc-by-4.0Jun 2019View details →

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Microclimate predicts frost-hardiness of alpine Arabidopsis thaliana populations better than elevation

In mountain regions, topological differences on the micro-scale can strongly affect microclimate and may counteract the average effects of elevation, such as decreasing temperatures. While these interactions are well understood, their effect on plant adaptation is understudied.   We investigated winter frost hardiness of Arabidopsis thaliana accessions originating from 13 sites along altitudinal gradients in the Southern Alps during three winters on an experimental field station on the Swabian Jura and compared levels of frost damage with the observed number of frost days and the lowest temperature in eight collection sites.   We found that frost-hardiness increased with elevation in a log-linear fashion. This is consistent with adaptation to a higher frequency of frost conditions, but also indicates a decreasing rate of change in frost hardiness with increasing elevation. Moreover, the number of frost days measured with temperature loggers at the collection sites correlated much better with frost-hardiness than the elevation of collection sites, suggesting that populations were adapted to their local microclimate. Notably, the variance in frost days across sites increased exponentially with elevation. Together, our results suggest that strong microclimate heterogeneity of high alpine environments can preserve functional genetic diversity among small populations.   Synthesis. Here we tested how plant populations differed in their adaptation to frost exposure along an elevation gradient and whether microsite temperatures improve the prediction of frost hardiness. We found that local temperatures, particularly the number of frost days, is a better predictor of the frost hardiness of plants than elevation. This reflects a substantial variance in frost frequency between sites at similar high elevations. We conclude that high mountain regions harbor microsites that differ in their local microclimate and thereby can preserve a high functional genetic diversity among them. Therefore, high mountain regions have the potential to function as a refugium in times of global change.

opencc-by-4.0Aug 2019View details →

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Dataset for the publication "Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site"

This datasset contains data to reproduce the following figures of the paper Superconducting gravimeter observations show that satellite-derived snow depth image improves the simulation of the snow water equivalent evolution in a high alpine site: <ul> <li> Time series data of Figures 1c and 2 </li> <li> Data (*.asc) used for plotting Figures 1d and 1e (as well as Figure S3 and S4) </li> <li>Pléiades snow depth map (Figure S1)</li> <li> Data used for plotting Figure S2 </li> </ul>

opencc-by-4.0Aug 2024View details →

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Atmospheric sounding of the boundary layer over alpine glaciers using fixed-wing UAVs

Additional code and data for the paper by Groos et al. entitled "Atmospheric sounding of the boundary layer over alpine glaciers using fixed-wing UAVs" Correspondence: Alexander R. Groos (alexander.groos@fau.de) The repository contains: (1) The raw data (log files) for each UAV-based atmospheric sounding (2) The postprocessed and reformatted data for each sounding and vertical profile (3) The commented R-Scripts for data processing, analysis and visualisation (4) A subset of the meteorological data from the nearby weather stations Description of sub-folders: -aws_data -- aws_fisistock.txt                # meteorological data from AWS Fisistock for the period of the campaign -- aws_gandegg.txt                # meteorological data from AWS Gandegg for the period of the campaign -- aws_sackhorn.txt                # meteorological data from AWS Sackhorn for the period of the campaign - processed_data -- kanderfirn_2021-06-16_10:45_p1_pprz.tab    # meteorological data for first profile/descent at about   -- kanderfirn_2021-06-16_10:45_p2_fr.tab    # flight recorder data for second profile/descent at about 10:45 CEST -- kanderfirn_2021-06-16_10:45_p2_pprz.tab    # meteorological data for second profile/descent at about 10:45 CEST -- kanderfirn_2021-06-16_10:45_pprz.tab    # meteorological data for the entire sounding (first and second profile/descent) at about 10:45 CEST --        . --        . --        . -- kanderfirn_2021-06-16_16:50_p1_pprz.tab    # meteorological data for first profile/descent at about 16:50 CEST -- kanderfirn_2021-06-16_16:50_p2_fr.tab    # flight recorder data for second profile/descent at about 16:50 CEST -- kanderfirn_2021-06-16_16:50_p2_pprz.tab    # meteorological data for second profile/descent at about 16:50 CEST -- kanderfirn_2021-06-16_16:50_pprz.tab    # meteorological data for the entire sounding (first and second profile/descent) at about 16:50 CEST -- kanderfirn_soundings_2021-06-16.csv    # summary table of vertical profiles (1 m height intervals): one column for each profile/descent and variable -- kanderfirn_turbulence_2021-06-16.csv    # summary table of vertical turbulence profiles (1 m height intervals): one column for each profile/descent - raw_data -- fr_kanderfirn_2021-06-16_10:45.LOG        # flight recorder data from the sounding at about 10:45 CEST (binary file) --        . --        . --        . -- fr_kanderfirn_2021-06-16_16:50.LOG        # flight recorder data from the sounding at about 16:50 CEST (binary file) -- pprz_kanderfirn_2021-06-16_10:45.LOG    # meteorological data from the sounding at about 10:45 CEST (human readable text file) --        . --        . --        . -- pprz_kanderfirn_2021-06-16_16:50.LOG    # meteorological data data from the sounding at about 16:50 CEST (human readable text file) - R_scripts -- figures.R                      # Script to create Figures 5, 6, 8, 9, 10, 11, 12 -- lapse_rate.R                  # Script to calculate lapse rates and surface-based inversions (includes code for Figures 7 and B1) -- postprocessing.R                  # Script to reformat preprocessed and preselected pprz-files -- turbulence.R                # Script for the calculation of the turbulence proxy from the recorded roll rate

opencc-by-4.0Oct 2024View details →

zenodo44/100

Model data and code for "Freeze-thaw effects on daily sediment transport in an Alpine river"

Supporting information for the research article "Freeze-thaw effects on daily sediment transport in an Alpine river" by Skålevåg et al., submitted to Water Resources Research. This data repository contains the processed data, model code, and results presented in the research article. Please refer to the article and its supplementary information for details on primary data.   Contents: <ul> <li>processed data: <ul> <li>Standardised target and predictor variables, in addition to non-standardised data used for freeze-thaw state classification <a href="https://zenodo.org/api/records/13928999/draft/files/model_variables.csv/content" target="_blank" rel="noopener noreferrer">model_variables.csv</a></li> <li>Means and standard deviations of standardised variables <a href="https://zenodo.org/api/records/13928999/draft/files/regression_variables_mean_std.csv/content" target="_blank" rel="noopener noreferrer">regression_variables_mean_std.csv</a></li> </ul> </li> <li>model code: <ul> <li>final model presented in research article: <a href="https://zenodo.org/api/records/13928999/draft/files/model.py/content" target="_blank" rel="noopener noreferrer">model.py</a></li> <li>model comparison performed as part of model development: <a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_predictors_and_segmentation.html/content" target="_blank" rel="noopener noreferrer">model_comparison_predictors_and_segmentation.html</a></li> </ul> </li> <li>results: <ul> <li>final model: <ul> <li>Inference trace from the pymc model <a href="https://zenodo.org/api/records/13928999/draft/files/inference.nc/content" target="_blank" rel="noopener noreferrer">inference.nc</a></li> <li>Summary table of the inference trace <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary.csv/content" target="_blank" rel="noopener noreferrer">inference_summary.csv</a></li> <li>Visualisation of the inference trace <a href="https://zenodo.org/api/records/13928999/draft/files/inference_trace.png/content" target="_blank" rel="noopener noreferrer">inference_trace.png</a></li> </ul> </li> <li>other models: <ul> <li>non-segmented sediment rating curve: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_SRC.nc/content" target="_blank" rel="noopener noreferrer">inference_SRC.nc</a> and <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_SRC.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_SRC.csv</a></li> <li>non-segmented "pooled" model with all predictors: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_full_nonsegmented.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_full_nonsegmented.csv</a></li> <li>freeze-thaw-state-segmented sediment rating curve: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_segm_SRC.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_segm_SRC.csv</a></li> <li>freeze-thaw-state-segmented "unpooled" model with all predictors: <a href="https://zenodo.org/api/records/13928999/draft/files/inference_summary_full_unpooled.csv/content" target="_blank" rel="noopener noreferrer">inference_summary_full_unpooled.csv</a></li> </ul> </li> <li>model comparison: <ul> <li><a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_waic.csv/content" target="_blank" rel="noopener noreferrer">model_comparison_waic.csv</a></li> <li> <div><a href="https://zenodo.org/api/records/13928999/draft/files/model_comparison_loo.csv/content" target="_blank" rel="noopener noreferrer">model_comparison_loo.csv</a></div> </li> </ul> </li> </ul> </li> </ul>

opencc-by-4.0Oct 2024View details →

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Leaf area index and above-ground biomass estimation of an alpine peatland with a UAV multi-sensor approach

Main data used for the scientific paper entitled: "Leaf area index and above-ground biomass estimation of an alpine peatland with a UAV multi-sensor approach". <ol> <li>"Danta_dem_10cm_px.tif": orthomosaic-derived DEM</li> <li>"Danta_rgb_2.2cm_px.tif": ortophoto </li> <li>"GPS points": list of GPS samples points</li> <li>"Main data": field vegetation data and indexes used for the regressions</li> <li>"Raw PointCloud". Lidar original dataset</li> <li>"Pre-processed PointCloud": Lidar dataset after pre-processing (see paper's methods) </li> <li>"DTM_DantaGround_grid50cm_minimo": Output (TIFF); the LiDAR-derived DTM showed in the paper</li> <li>"LAI": Output (Shapefile); the LiDAR-derived LAI showed in the paper.</li> </ol>

opencc-by-4.0May 2023View details →

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Local Earthquake Tomography of the Alpine Region from 24 Years of Data - DELIVERABLES

<h1>Local Earthquake Tomography of the Alpine Region from 24 Years of Data</h1> M. Bagagli(1), I. Molinari(2), T. Diehl(3), E. Kissling(4) (1) Dipartimento Scienze della Terra, Università di Pisa, 56126 Pisa, Italy (2) Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, 40127 Bologna, Italy (3) Swiss Seismological Service, ETH Zurich, 8006 Zürich, Switzerland (4) Institute of Geophysics, Department of Earth Sciences, ETH Zürich, 8006 Zürich, Switzerland mail-to: matteo.bagagli@dst.unipi.it date: 08.11.2024 version: 1.0 ----------------------------------------------------------------------------------------------------- This repository contains the all the deliverables of the aforementioned manuscript. The folder is organized into subfolders for the relative tasks. - Min1D_StatDelays - 3Dtomo - EMSC_Catalog_May2007_Dec2015 - tomo2plt_scripts - inventories For additional details, we refer the reader to the main manuscript and its supplementary materials.

opencc-by-4.0Nov 2024View details →

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Alpine ice sheet erosion potential aggregated variables

These data contain domain-integrated and time-integrated model output variables presented in the reference below or otherwise relevant to last glacial cycle glacier erosion in the Alps. Reference: <ul> <li>J. Seguinot and I. Delanay. Last glacial cycle glacier erosion potential in the Alps, submitted to Earth Surface Dynamics Discussions, 2021.</li> </ul> File names: <pre><code>alpero.{1km|2km}.{epic|grip|md01}.{cp|pp}.agg.nc</code></pre> <ul> <li>Horizontal resolution: <ul> <li>1km: 1 km horizontal resolution</li> <li>2km: 2 km horizontal resolution</li> </ul> </li> <li>Temperature forcing: <ul> <li>epic: EPICA ice core temperature forcing</li> <li>grip: GRIP ice core temperature forcing</li> <li>md01: MD01-2444 core temperature forcing</li> </ul> </li> <li>Precipitation forcing: <ul> <li>cp: constant precipitation</li> <li>pp: palaeo-precipitation reduction</li> </ul> </li> </ul> Variables: <ul> <li>Coordinate variables: <ul> <li>x: X-coordinate in Cartesian system</li> <li>y: Y-coordinate in Cartesian system</li> <li>lon: longitude</li> <li>lat: latitude</li> <li>time: time</li> <li>age: model age</li> <li>z: elevation band midpoints</li> <li>d: distance along transect</li> </ul> </li> <li>Glacier erosion variables: <ul> <li>coo2020_cumu: Cook et al. (2020) cumulative glacial erosion potential</li> <li>coo2020_rate: Cook et al. (2020) domain total volumic erosion rate</li> <li>coo2020_hyps: Cook et al. (2020) erosion rate geometric mean</li> <li>coo2020_rhin: Cook et al. (2020) rhine transect erosion rate</li> <li>her2015_cumu: Herman et al. (2015) cumulative glacial erosion potential</li> <li>her2015_rate: Herman et al. (2015) domain total volumic erosion rate</li> <li>her2015_hyps: Herman et al. (2015) erosion rate geometric mean</li> <li>her2015_rhin: Herman et al. (2015) rhine transect erosion rate</li> <li>hum1994_cumu: Humphrey and Raymond (1994) cumulative glacial erosion potential</li> <li>hum1994_rate: Humphrey and Raymond (1994) domain total volumic erosion rate</li> <li>hum1994_hyps: Humphrey and Raymond (1994) erosion rate geometric mean</li> <li>hum1994_rhin: Humphrey and Raymond (1994) rhine transect erosion rate</li> <li>kop2015_cumu: Koppes et al. (2015) cumulative glacial erosion potential</li> <li>kop2015_rate: Koppes et al. (2015) domain total volumic erosion rate</li> <li>kop2015_hyps: Koppes et al. (2015) erosion rate geometric mean</li> <li>kop2015_rhin: Koppes et al. (2015) rhine transect erosion rate</li> </ul> </li> <li>Other variables: <ul> <li>cumu_sliding: cumulative basal motion</li> <li>glacier_time: total ice cover duration</li> <li>warmbed_time: temperate-based ice cover duration</li> <li>glacier_area: glacierized area</li> <li>volumic_lift: volumic bedrock uplift</li> <li>warmbed_area: temperate-based ice cover area</li> </ul> </li> </ul> Data format: The data use compressed netCDF format. For quick inspection I recommend ncview. Conversion to GeoTIFF (and other GIS formats) can be achieved with e.g. GDAL:: <pre><code>gdal_translate NETCDF:filename.nc:variable filename.variable.tif</code></pre> The list of variables (subdatasets) can be obtained from ncdump or gdalinfo. To convert all variables to separate files use: <pre><code>gdalinfo $filename | grep NETCDF | cut -d '=' -f 2 | egrep -v '(lat|lon|time_bounds)' | while read sub do gdal_translate $sub ${filename%.nc}.${sub##*:}.tif done</code></pre> Variable long names, units, PISM configuration parametres and additional information are contained within the netCDF metadata. Also see glacial cycle <a href="https://doi.org/10.5281/zenodo.1423160">aggregated</a> and <a href="https://doi.org/10.5281/zenodo.1423175">continuous</a> variables. Changes: <ul> <li>Version 2: <ul> <li>Add variable for glacierized area within 100-m elevation band.</li> <li>Use 100-m instead of 10-m elevation bands for erosion rate.</li> </ul> </li> <li>Version 1: <ul> <li>Initial version.</li> </ul> </li> </ul>

opencc-by-4.0Feb 2021View details →

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Dataset: Time-dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling positive matrix factorisation (PMF) window

Uploaded igor pxp files are the data to generate all figures of the results from our publication in Atmospheric Chemistry and Physics with the name of "Time dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling PMF window" by Chen et al. (2021). This study deployed a novel and advanced source apportionment technique on a dataset measured in Magadino. Rolling PMF allows retrieving more realistic, time-dependent and detailed information of the organic aerosol sources. This work highlights the strength of the rolling PMF mechanism by comparing it with the results derived from conventional seasonal PMF. Overall, this comprehensive interpretation of chemical speciation monitor (ACSM) data could be a role model for similar analyses.

opencc-by-4.0Jul 2021View details →

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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