These data are in addition to "Madison Wisconsin Daily Meteorological Data 1869-current." Additional variables added include: daily cloud cover, wind, solar radiation, vapor pressure, dew point temperature, total atmospheric pressure, and average relative humidity for Madison, Wisconsin. In addition, the adjustment factors which were applied on a given date to calculate the adjusted parameters in "Madison Wisconsin Daily Meteorological Data 1869-current" are also included in these data. Raw data, in English units, were assembled by Douglas Clark - Wisconsin State Climatologist. Data were converted to metric units and adjusted for temporal biases by Dale M. Robertson. For adjustments applied to various parameters see Robertson, 1989 Ph.D. Thesis UW-Madison. Adjusted data represent the BEST estimated daily data and may be raw data. Data collected at Washburn observatory, 8-1-1883 to 9-30-1904. Data collected at North Hall, 10-1-1904 to 12-31-1947 Data collected at Truax Field (Admin BLDG), 1-1-1948 to 12-31-1959. Data collected at Truax Field, center of field, 1-1-1960 to Present. Much of the data after 1990 were obtained in digital form from Ed Hopkins, UW-Meteorology. Data starting in 2002-2005 were obtained from Sullivan at http://www.weather.gov/climate/index.php?wfo=mkx%20 ,then go to CF6 and download monthly data to Madison_sullivan_conversion. Relative humidity data was obtained from 1986 to 1995 from CD's at the State Climatologist's Office. Since Robertson (1989) adjusted all historical data to that collected prior to 1989; no adjustments were applied to the recent data except for wind and estimated vapor pressure. Wind after January 1997, and only wind from the southwest after November 2007, was extended by Dale M. Robertson and Yi-Fang "Yvonne" Hsieh, see methods. Estimated vapor pressure after April 2002 was updated by Yvonne Hsieh, see methods.

A shapefile of annual average wind resource potential for Wisconsin, United States at a 50 meter height. This data set has been validated by NREL and wind energy meteorological consultants. Note: This data is not suitable for micro-siting potential development projects. This shapefile was generated from a raster dataset with a 200 m resolution, in a UTM zone 17, datum WGS 84 projection system. The wind power resource estimates were produced by AWS TrueWind using their MesoMap system and historical weather data under contract to Wind Powering America/NREL. This map has been validated with available surface data by NREL and wind energy meteorological consultants. For updated gridded long-term average wind data please see the "Global Wind Atlas" resource below. For more information on NREL's wind resource data development, see the "Wind Integration National Dataset (WIND) Toolkit" and the "WIND Toolkit Long-Term Ensemble Dataset" resources.

Multiple global change drivers are increasing the present and future novelty of environments and ecological communities. However, most assessments of environmental novelty have focused only on future climate and were conducted at scales too broad to be useful for land management or conservation. Here, using historical county-level datasets of agricultural land use, forest composition, and climate, we conduct a regional-scale assessment of environmental novelty for Wisconsin landscapes from ca. 1890 to 2012. Agricultural land-use data include six cropland types, livestock densities for four livestock species, and human populations. Forestry data comprise biomass-weighted relative abundances for 15 tree genera. Climate data comprise seasonal means for temperature and precipitation. We found that forestry and land use are the strongest cause of environmental novelty (NoveltyForest=3.66, NoveltyAg.=2.83, NoveltyClimate=1.60, with Wisconsin’s forests transformed by early 20th-century logging and its legacies and multiple waves of agricultural innovation and obsolescence. Climate change is the smallest contributor to contemporary novelty, with precipitation signals stronger than temperature. Magnitudes and causes of environmental novelty are strongly spatially patterned, with novelty in southern Wisconsin roughly twice that in northern Wisconsin. Forestry is the most important cause of novelty in the north, land use and climate change are jointly important in the southwestern Wisconsin, and land use and forest composition are most important in central and eastern Wisconsin. Areas of high regional novelty tend also to be areas of high local change, but local change has not pushed all counties beyond regional baselines. Seven counties serve as the best historical analogues for over half of contemporary Wisconsin counties (40/72), and so can offer useful historical counterparts for contemporary systems and help managers coordinate to tackle similar environmental challenges. Multi-dimensional environmental novelty analyses, like those presented here, can help identify the best historical analogues for contemporary ecosystems, places where new management rules and practices may be needed because novelty is already high, and the main causes of novelty. Separating regional novelty clearly from local change and measuring both across many dimensions and at multiple scales thus helps advance ecology and sustainability science alike.

Daily averaged weather timeseries (air temperature, pressure, wind speed, wind direction, precipitation, sky cover) at Palmer Station, Antarctica combining manual observations (1989 - Dec 12, 2003) and PALMOS automatic weather station measurements (Dec 13, 2003 - March 2019). Weather data acquisition was originally by manual observation and continued with an automated system installed in Nov 2001. Measurements began shifting from manual to automated observations in June 2003 until the manual observations were ended on December 12, 2003. Data are collected, compiled, and distributed by the US Antarctic polar contractor. Electronic distributed occurs monthly from Palmer station via internet and are available at University of Wisconsin weather archive: ftp://amrc.ssec.wisc.edu/pub/palmer/climatology/.Weather data acquisition was originally by manual observation and continued with an automated system installed in Nov 2001. Measurements began shifting from manual to automated observations in June 2003 until the manual observations were ended on December 12, 2003. Data are collected, compiled, and distributed by the US Antarctic polar contractor. Electronic distributed occurs monthly from Palmer station via internet and are available at University of Wisconsin weather archive: ftp://amrc.ssec.wisc.edu/pub/palmer/climatology/ _NCProperties=version=1|netcdflibversion=4.6.1|hdf5libversion=1.10.6 acknowledgement=Funding and support provided by the National Science Foundation cdm_data_type=TimeSeries cdm_timeseries_variables=station_id,latitude,longitude comment=The Palmer, Antarctica, Long-Term Ecological Research project is a member site of the Long-Term Ecological Research program, a network of sites investigating diverse biomes. A team of researchers seeks to understand the structure and function of the Western Antarctic Peninsula's marine and terrestrial ecosystems in the context of seasonal-to-interannual atmospheric and sea ice dynamics, as well as long-term climate change. The PAL measurement system (or grid) is designed to study marine and terrestrial food webs consisting principally of diatom primary producers, the dominant herbivore Antarctic krill, and the apex predator Adelie penguin. An attenuated microbial food web is also a focus. PAL studies these ecosystems annually over a regional scale grid of oceanographic stations and seasonally at Palmer Station.

Palmer Station is located on Anvers Island west of the Antarctic Peninula. The peninsula runs perpendicular to a strong climatic gradient between the cold, dry continental regime to the south, characteristic of the Antarctic interior, and the warm, moist, maritime regime to the north. North-south shifts in the gradient give rise to large environmental variability to climate change. Sea ice extent and variability affects ecosystem changes at all trophic levels. In addition to the long-term field and research activities, information management, graduate student training, education and outreach are an integral part of the program. contributor_email=pallter-im@ucsd.edu contributor_name=PAL Information Manager contributor_role=PrincipalInvestigator contributor_role_vocabulary=https://vocab.nerc.ac.uk/collection/G04/current/ Conventions=CF-1.8, ACDD-1.3, COARDS datazoo_dataset_id=27 datazoo_datatable_id=28 datazoo_datatable_label=Palmer Station Weather - Daily Averages datazoo_datatable_name=PalmerStationWeatherDailyAverages defaultDataQuery=null Easternmost_Easting=-64.05 featureType=TimeSeries geospatial_bounds_crs=EPSG:4326 geospatial_bounds_vertical_crs=EPSG:5831 geospatial_lat_max=-64.77 geospatial_lat_min=-64.77 geospatial_lat_resolution=0.00001 degree geospatial_lat_units=degrees_north geospatial_lon_max=-64.05 geospatial_lon_min=-64.05 geospatial_lon_resolution=0.00001 degree geospatial_lon_units=degrees_east geospatial_vertical_positive=down geospatial_vertical_units=EPSG:5831 history=/Users/kerfoot/data/lter/data/tsv/dat_28/2019_weather_averages.tsv infoUrl=https://pal.lternet.edu/ institution=National Science Foundation keywords_vocabulary=LTER Core Areas,LTER Controlled Vocabulary license_link=https://lternet.edu/data-access-policy/ methods=Wind Measurements: The Digitally recording anemometer replaced the analog anemometer as the instrument of record beginning October 25, 2003. The digital recording anemometer reports 2-minute average wind speed with a direction associated with each 2-minute average and the maximum 5-second wind gust for each 2-minute interval without a direction associated with it. The format of the wind section of this report has been modified slightly to accommodate the format of the new instruments data. naming_authority=edu.rutgers.rucool Northernmost_Northing=-64.77 program=LTER project=Palmer LTER references=https://pal.lternet.edu/ sea_name=Southern Ocean source=/Users/kerfoot/data/lter/data/tsv/dat_28/2010_weather_averages.tsv sourceUrl=(local files) Southernmost_Northing=-64.77 standard_name_vocabulary=CF Standard Name Table v77 subsetVariables=peak_windspeed, peak_wind_speed_2minute_peak, peak_wind_direction_true, peak_wind_direction, peak_wind_direction_2minute, cloud_cover, avg_temperature_flag, avg_pressure_flag, station_id, latitude, longitude time_coverage_end=2019-03-31T00:00:00Z time_coverage_start=1989-04-01T00:00:00Z Westernmost_Easting=-64.05

The repository contains the data (and basic code to read it) for the paper "Time matters: Empirical insights into the limits and challenges of temporal generalization in CSI-based Wi-Fi sensing". Two datasets are being released: The first one, namely "Our novel IP (Indoor positioning) dataset" (see paper Section 4.3), provides CSI-based indoor positioning data collected with an ESP32S2 micro-controller in passive sniffing mode considering 21 reference points at a residential location spanning three floors. Data collection was repeated twice, on two separate days, capturing realistic conditions including random device orientations, and uncontrolled Wi-Fi access point. The second one, namely "Our novel CSI signal temporal stability dataset" (see paper Section 4.4), supports studies on CSI signal stability over time, collected continuously over a period of more than three weeks in a controlled 30 m² laboratory environment. Data includes 5,769,240 CSI samples captured under varying occupancy conditions, including workdays, weekends, and holidays, enabling comprehensive temporal analysis. Thid data is paired with historical weather temperature data as recorded by an outdoor weather station placed at ∼7km distance from the University. If you use this data, please, cite the main paper as well: @article{brunello2025time, title={Time matters: Empirical insights into the limits and challenges of temporal generalization in CSI-based Wi-Fi sensing}, author={Brunello, Andrea and Montanari, Angelo and Montoliu, Ra{\'u}l and Moreira, Adriano and Saccomanno, Nicola and Sansano-Sansano, Emilio and Torres-Sospedra, Joaqu{\'\i}n}, journal={Internet of Things}, pages={101634}, year={2025}, publisher={Elsevier} }