Historic pictures of railway stations in Switzerland. The data is provided by SBB Historic

The Enhanced Master Station History Report (EMSHR) is a compiled list of basic, historical information for every station in the station history database, beginning in the 1700s through the present. The Enhanced MSHR version contains similar data elements and is in a similar format as the Standard/Legacy MSHR (DSI-9767_01), but the Enhanced MSHR includes additional stations that are included in the Global Historical Climatology Network (GHCN)-Daily product. Also, the Enhanced MSHR incorporates additional station networks, including international stations/networks. The Enhanced MSHR contains an expanded number of items to the original MSHR, such as station status, additional IDs, alternate station names, NWS region, NWS Weather Forecast Office (WFO), all elevations, latitude/longitude precision, and UTC offset.

Licence information and source

The MET Office copyright policy can be found at: [https://www.metoffice.gov.uk/about-us/legal#licences] Data source from: [https://www.metoffice.gov.uk/research/climate/maps-and-data/historic-station-data]

Cover image: [https://pixabay.com/photos/scarborough-sunrise-seascape-2850597/]

Historical availability of bicycles and docks to return bicycles at the Divvy stations (http://divvybikes.com/). For the current list of stations, see https://data.cityofchicago.org/d/bbyy-e7gq For real-time status of stations in machine-readable format, see https://feeds.divvybikes.com/stations/stations.json. Due to a change in the data source, discussed at http://dev.cityofchicago.org/open%20data/data%20portal/2019/07/10/divvy-datasets-frozen.html, records between 7/7/2019 and 12/9/2019 are missing.

The Standard/Legacy MSHR, formally identified as the DSI-9767 dataset, is the legacy dataset/report sorted by NCDC Station ID and period of record. This dataset/report includes historical station changes in reported fields generating multiple records for a station where a new record is created for every period in which a reported component for a station is changed. This dataset is continued to be produced to support legacy processing systems.

Abstract

The Global Historical Climatology Network daily (GHCNd) is an integrated database of daily climate summaries from land surface stations across the globe. GHCNd is made up of daily climate records from numerous sources that have been integrated and subjected to a common suite of quality assurance reviews.

GHCNd contains records from more than 100,000 stations in 180 countries and territories. NCEI provides numerous daily variables, including maximum and minimum temperature, total daily precipitation, snowfall, and snow depth. About half the stations only report precipitation. Both record length and period of record vary by station and cover intervals ranging from less than a year to more than 175 years.

Methodology

The process of integrating data from multiple sources into GHCNd takes place in three steps:

1. Screening the source data for stations whose identity is unknown or questionable
2. Classifying each station in a source dataset either as one that is already represented in GHCN-D or as a new site
3. Mingling the data from the different sources

%3C!-- --%3E

The process performs the first two of these steps whenever a new source dataset or additional stations become available, while the mingling of data is part of the automated processing that creates GHCNd on a regular basis.

Screening the Source Data

A station within a source dataset is considered for inclusion in GHCNd if it meets all of the following conditions:

• It can be identified with a name, latitude, and longitude contained in metadata provided as part of the source dataset or in standard station history information
• Its record contains 100 or more values for at least one of the GHCNd elements
• It does not fail the interstation duplicate check that compares records from all stations within a source dataset in order to identify cases in which more than 50% of a station's record is identical to the data from another station

%3C!-- --%3E

Classifying Stations

The next step is to determine for each station in the source dataset if data for the same location are already contained in GHCNd, or if the station represents a new site. Whenever possible, stations are matched on the basis of network affiliation and station identification number. If no match exists, then there is consultation from different networks for existing cross-referenced lists that identify the correspondence of station identification numbers.

For example, data for Alabaster Shelby County Airport, Alabama, USA, is stored under Cooperative station ID 010116 in NCEI's datasets 3200 and 3206 as well as in the data stream from the High Plains Regional Climate Center; they are combined into one GHCNd record based on the ID. In data set 3210 and the various sources for ASOS stations, however, the data for this location are stored under WBAN ID 53864 and must be matched with the corresponding Cooperative station ID using NCEI's Master Station History Record.

A third approach is to match stations on the basis of their names and location. This strategy is more difficult to automate than the other two approaches because identification of multiple stations within the same city or town, with the same name and small differences in coordinates, can be the result of either differences in accuracy or the existence of multiple stations in close proximity to each other. As a result, the employment of the third approach is used only when stations cannot be matched on the basis of station identification numbers or cross-reference information. This is the case, for example, when there is a need for matching stations outside the U.S. whose data originate from the Global Summary of the Day dataset and from the International Collection.

Mingling

The implementation of the above classification strategies yields a list of GHCNd stations and an inventory of the source datasets for integration of each station. This list forms the basis for integrating, or mingling, the data from the various sources to create GHCNd. Mingling takes place according to a hierarchy of data sources and in a manner that attempts to maximize the amount of data included while also minimizing the degree to which data from sources with different characteristics are mixed. While the mingling of precipitation, snowfall, and snow depth are separate, consideration of maximum and minimum temperatures is performed together in order to ensure the temperatures for a particular station and day always originate from the same source. Data from the Global Summary of the Day dataset are used only if no observations are available from any other source for that station, month, and element. Among the other sources, consideration of each day is made individually; if an observation for a particular station and day is available from more than one source, GHCNd uses the observation from the most preferred source available.

Several criteria are used for the hierarchy of data sources used in cases of overlap. In gener

This file contains the count of unscaled station entries for gated MBTA subway, Silver Line and light rail stations by service date and 30-minute period. To note:A service date is defined as 3:00AM – 2:59AM (2:59AM the next day).An entry is measured as a successful smart card tap, credit card tap, or ticket insertion recorded at a faregate.Entries are unscaled and so do not take into account non-interaction with the fare gate (e.g. children or other free passengers, fare evasion, etc.). Entries are only provided for times when the station is open. Employee taps are included in this data. This data does not represent total ridership at a station. Faregates are held open at times for special events or emergencies and entries are not captured accurately during those times.Files are separated by calendar year for previous years, and by month for the current calendar year.Due to data issues, data is not guaranteed to be complete for any stop or date.The following explanation of "split stations" only applies to those stations in the MBTA system that service multiple rapid transit lines.Since validated gated entries only determine that a person entered a station, it cannot be used to determine the number of passengers that use a specific line in stations that service multiple lines. To adjust for this, we rely on Central Transportation Planning Staff (CTPS) surveys that gather passenger behavior to develop "split factors." These factors determine the proportion of passengers that board each of the available lines in a split station after entry. For example, at Government Center, passengers tap and enter through the main gates and may choose to take the blue line or the green line. According to the surveys, we assume that about 90% of Government Center entrants ride the green line and the remaining 10% take the blue line. For the split stations in this dataset, the entries will show up to one decimal point to account for the factors and reduce rounding error when aggregating. It will also create two rows in the dataset for the station, one for each line.

Data Dictionary:NameDescriptionData TypeExampleservice_dateThe service date of the gated station entries. Times that are within the service date, but after midnight, are listed as ocurring on the previous calendar date, e.g. entries between 12:00:00AM - 12:30:00AM on Jan 2, 2020 is listed as '2020/01/01 12:00 AM'.Date2019/12/31, 12:00 AMtime_periodThe start time of the 30-minute time period, in 24-hour time. Periods starting after 11:59 PM are represented as 00:00 plus their 24-hour time. For example, 1:30:00AM - 1:59:59AM is listed as '01:30:00'.String01:30:00stop_idGTFS-compatible stop for which gated station entries should be returned; usually provided as the parent station id.Stringplace_wondlstation_nameGTFS-compatible station for which gated station entries should be returned.StringWonderlandroute_or_lineDescription of the route or service provided.StringBlue Linegated_entriesProperty of “Gated Station Entries”. Unscaled station entries for gated heavy rail and light rail stations for date and 30-minute increment.Integer324MassDOT/MBTA shall not be held liable for any errors in this data. This includes errors of omission, commission, errors concerning the content of the data, and relative and positional accuracy of the data. This data cannot be construed to be a legal document. Primary sources from which this data was compiled must be consulted for verification of information contained in this data.

Layer represents the geographic locations of all past and present meteorological stations. Many stations are equipped with meteorological sensors to collect meteorological observations in conjunction with water level data. The following observations may be retrieved from the CO-OPS website: wind speed and directions, air temperature, water temperature, barometric pressure, relative humidity, and visibility. Not every station has the full suite of sensors installed, and some meteorological stations are stand-alone. More information can be found at http://tidesandcurrents.noaa.gov/stations.html?type=Meteorological%20Observations

Layer represents the geographic locations that are no longer active at which current (water velocity) observations were collected. Generally, "currents" are defined as a horizonal movement of water. Currents may be classified as tidal and nontidal. Tidal currents are caused by gravitational interactions among the sun, moon, and earth and are part of the same general movement of the sea that is manifested in the vertical rise and fall, called tide. Nontidal currents include the permanent currents in the general circulatory systems of the sea, as well as temporary currents arising from more pronounced meteorological variability. Most stations with current observations provide readings every 6 minutes. Each year, CO-OPS measures currents at many coastal locations in order to provide accurate tidal current predictions for the maritime community. These data sets typically range from one to three months in length and at most locations, data are available throughout the water column. These data contain the raw current measurements taken during these surveys, which date back to 1997. Data from formerly active (real-time) stations also can be found here. More information can be found at http://tidesandcurrents.noaa.gov/cdata/StationList?type=Current+Data&filter=historic http://tidesandcurrents.noaa.gov/cdata/StationList?type=Current+Data&filter=historic

EDR has searched selected national collections of business directories and has collected listings of potential gas station/filling station/service station sites that were available to EDR researchers. EDR’s review was limited to those categories of sources that might, in EDR’s opinion, include gas station/filling station/service station establishments. The categories reviewed included, but were not limited to gas, gas station, gasoline station, filling station, auto, automobile repair, auto service station, service station, etc. This database falls within a category of information EDR classifies as "High Risk Historical Records", or HRHR. EDR’s HRHR effort presents unique and sometimes proprietary data about past sites and operations that typically create environmental concerns, but may not show up in current government records searches.

Timeseries data from 'Ocean Station Papa Historic CO2' (gov_ornl_cdiac_papa_145w_50n)

GC-Net Level 1 automated weather station data In Memory of Dr. Konrad (Koni) Steffen Author: B. Vandecrux Contact: bav@geus.dk Last update: 2023-09-01 Citation Steffen, K.; Vandecrux, B.; Houtz, D.; Abdalati, W.; Bayou, N.; Box, J.; Colgan, L.; Espona Pernas, L.; Griessinger, N.; Haas-Artho, D.; Heilig, A.; Hubert, A.; Iosifescu Enescu, I.; Johnson-Amin, N.; Karlsson, N. B.; Kurup Buchholz, R.; McGrath, D.; Cullen, N.J.; Naderpour, R.; Molotch, N.P.; Pederson, A. Ø.; Perren, B.; Philipps, T.; Plattner, G.K.; Proksch, M.; Revheim, M. K.; Særrelse, M.; Schneebli, M.; Sampson, K.; Starkweather, S.; Steffen, S.; Stroeve, J.; Watler, B.; Winton, Ø. A.; Zwally, J.; Ahlstrøm, A., 2023, "GC-Net Level 1 automated weather station data", https://doi.org/10.22008/FK2/VVXGUT, GEUS Dataverse, V3 as described and processed by: Vandecrux, B., Box, J. E., Ahlstrøm, A. P., Andersen, S. B., Bayou, N., Colgan, W. T., Cullen, N. J., Fausto, R. S., Haas-Artho, D., Heilig, A., Houtz, D. A., How, P., Iosifescu Enescu, I., Karlsson, N. B., Kurup Buchholz, R., Mankoff, K. D., McGrath, D., Molotch, N. P., Perren, B., Revheim, M. K., Rutishauser, A., Sampson, K., Schneebeli, M., Starkweather, S., Steffen, S., Weber, J., Wright, P. J., Zwally, H. J., and Steffen, K.: The historical Greenland Climate Network (GC-Net) curated and augmented Level 1 dataset, Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, 2023. Description The Greenland Climate Network (GC-Net) is a set of Automatic Weather Stations (AWS) set up and managed by the late Prof. Dr. Konrad (Koni) Steffen on the Greenland Ice Sheet (GrIS). This first station, "Swiss Camp" or the "ETH-CU" camp, was initiated in 1990 by A. Ohmura et al. (1991, 1992) with K. Steffen taking over the site from 1995 and expending the network from that year to 31 stations at 30 sites in Greenland (Steffen et al., 1996, 2001). The GC-Net was supported by multiple NASA, NOAA, and NSF grants throughout the years, and then supported by WSL in the later years. These data were previously hosted by the Cooperative Institute for Research in Environmental Sciences (CIRES) in Boulder, Colorado. Provided in this dataset are the 25 two-level stations from 24 sites on the Greenland ice sheet and 3 experimental stations in Antarctica. The remaining 6 Greenland stations have a different design and will be added once quality checked. Although the GC-Net AWS transmitted their data near-real time through satellite communication, the present dataset was made from uncorrupted datalogger files, retrieved every 1-2 years during maintenance. Full dataset description publication will be forthcoming. The Geological Survey of Denmark and Greenland (GEUS) has undertaken the continuation of multiple GC-Net sites through the Programme for Monitoring of the Greenland Ice Sheet (PROMICE.dk). The level 1 data is provided in the newly described csv-compatible NEAD format, which is a csv file with added metadata header. The format is documented at https://doi.org/10.16904/envidat.187 and a python package is available to read and write NEAD files: https://github.com/GEUS-Glaciology-and-Climate/pyNEAD . The GC-Net stations measure: - Air temperature from four sensors at two heights above the surface - Relative humidity at two heights above the surface - Wind speed and direction at two heights above the surface - Air pressure - Surface height from two sonic sounders - Incoming and outgoing shortwave radiation - Net radiation (long- and short-wave)* - Firn or ice temperatures at 10 levels below the surface In the L1 dataset, these measurements are cleaned from sensor, station or logger malfunctions, adjusted and/or filtered when and where possible. Additionally, the L1 dataset contains the following derived variables: - Surface height (corrected from the shifts in sonic sounder height) - Instrument heights (derived from sonic sounder height and station geometry) - Inter- or extrapolated temperature, relative humidity and wind speed at respectively 2, 2, and 10 m above the surface - Estimated depth of the subsurface temperature measurements (adjusted for snow accumulation, ice ablation and instrument replacement) - Interpolated firn or ice temperature at 10 m below the surface - Calculated solar an azimuth angles - Sensible and latent heat fluxes calculated after Steffen and Demaria (1996) Important links: - The level 1 processing scripts and discussion page for Q&A and issue reporting (under "issues" tab) is available at: https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing - The level 0 data (from which the L1 data was built from) is available at: https://www.doi.org/10.16904/envidat.1. - The compilation of handheld GPS coordinates for each site and for multiple years is available here: Vandecrux, B. and Box, J.E.: GC-Net AWS observed and estimated positions (Version v1) [Data set]. Zenodo....

Analysis of ‘Historical pictures of railway stations ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/historische-bahnhofbilder-schweizerische-bundesbahnen-sbb on 12 January 2022.

--- Dataset description provided by original source is as follows ---

Historical pictures of railway stations in Switzerland. The data is provided by SBB Historic

--- Original source retains full ownership of the source dataset ---

Layer represents any geographic location at which tidal (water level) observations have been collected and verified, including stations presently collecting observations. "Tide" is defined as the periodic rise and fall of a body of water resulting from gravitational interactions between Sun, Moon, and Earth. "Water level" is defined as the height of the water surface relative to a specific datum (reference elevation). Most stations with water level observations provide readings every 6 minutes. CO-OPS measures water levels at over 200 stations along the coast of the United States and its territories and around the Great Lakes. More information can be found at http://tidesandcurrents.noaa.gov/stations.html?type=Historic+Water+Levels

This dataset provides information about the number of properties, residents, and average property values for Day Avenue cross streets in Old Station, CA.

Historic railway stations

NOTE: Version 3 of GHCN has been discontinued at NCEI, and so this dataset is no longer being updated. Version 4 of GHCN can be accessed here [https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/global-historical-climatology-network-monthly-version-4]. The Global Historical Climatology Network (GHCN) is a baseline network of long-running surface global stations for the purpose of monitoring and detecting climate change. Monthly-summarized data from various global sources are processed on a daily basis assembled into version 3 of NCDC's GHCNM dataset. The CISL Research Data Archive is hosting a copy of the monthly-average surface temperatures in support of UCAR's Global Learning and Observation to Benefit the Environment (GLOBE) program.

Explore the historical Whois records related to train-station.info (Domain). Get insights into ownership history and changes over time.

WMS service of the urban development plan “At the old station (origin plan)” of the Great District City of Waghäusel, which was transformed to INSPIRE, based on an XPlanung dataset in version 5.0.

Images contain station history information for 175 stations in the National Water Level Observation Network (NWLON). The NWLON is a network of long-term, continuously operating water-level stations throughout the USA, including its island possessions and territories and the Great Lakes. NWLON stations are the foundation for reference stations for NOAA's tide prediction products, and serve as controls in determining tidal datums for all short-term water-level stations. Additional histories for approximately 6,000 secondary and tertiary stations are also included, but are likely incomplete due to defunding of the task prior to completion. Images created through a Climate Database Modernization Program task in partnership with NOAA's National Ocean Service, Center for Operational Oceanographic Products and Services (CO-OPS)/Requirements and Development Devision. The task leader was oceanographer Thomas F. Landon, and then Manoj R. Samant. Scanning began with a pilot in fall 2003, and continued through early 2011.