This dataset shows whether each dataset on data.maryland.gov has been updated recently enough. For example, datasets containing weekly data should be updated at least every 7 days. Datasets containing monthly data should be updated at least every 31 days. This dataset also shows a compendium of metadata from all data.maryland.gov datasets.

This report was created by the Department of Information Technology (DoIT) on August 12 2015. New reports will be uploaded daily (this report is itself included in the report, so that users can see whether new reports are consistently being uploaded each week). Generation of this report uses the Socrata Open Data (API) to retrieve metadata on date of last data update and update frequency. Analysis and formatting of the metadata use Javascript, jQuery, and AJAX.

This report will be used during meetings of the Maryland Open Data Council to curate datasets for maintenance and make sure the Open Data Portal's data stays up to date.

As of April 2025, 36 percent of apps on the Google Play Store were updated on a weekly basis. In comparison, 73 percent of mobile apps were updated on a monthly basis. Over 95 percent of Android mobile apps hosted on the Google Play Store were updated each year.

This dataset shows whether each dataset on data.maryland.gov has been updated recently enough. For example, datasets containing weekly data should be updated at least every 7 days. Datasets containing monthly data should be updated at least every 31 days. This dataset also shows a compendium of metadata from all data.maryland.gov datasets.

This report was created by the Department of Information Technology (DoIT) on August 12 2015. New reports will be uploaded daily (this report is itself included in the report, so that users can see whether new reports are consistently being uploaded each week). Generation of this report uses the Socrata Open Data (API) to retrieve metadata on date of last data update and update frequency. Analysis and formatting of the metadata use Javascript, jQuery, and AJAX.

This report will be used during meetings of the Maryland Open Data Council to curate datasets for maintenance and make sure the Open Data Portal's data stays up to date.

As of February 2022, the first Android apps appearing to users on the Google Play Store results page were updated on average every 58 days. Social media apps presented the most frequent updates on average, with a median update date of 13 days, and an average update frequency of 54 days. By comparison, finance apps had a median and an average of 55 days for new releases, respectively.

This dataset shows whether each dataset on data.maryland.gov has been updated recently enough. For example, datasets containing weekly data should be updated at least every 7 days. Datasets containing monthly data should be updated at least every 31 days. This dataset also shows a compendium of metadata from all data.maryland.gov datasets.

This report was created by the Department of Information Technology (DoIT) on August 12 2015. New reports will be uploaded daily (this report is itself included in the report, so that users can see whether new reports are consistently being uploaded each week). Generation of this report uses the Socrata Open Data (API) to retrieve metadata on date of last data update and update frequency. Analysis and formatting of the metadata use Javascript, jQuery, and AJAX.

This report will be used during meetings of the Maryland Open Data Council to curate datasets for maintenance and make sure the Open Data Portal's data stays up to date.

We summarized annual remote sensing land cover classifications from the U.S. Geological Survey Land Cover Monitoring, Assessment, and Projection (LCMAP) annual time series to characterize the frequency of forest change across the conterminous United States (CONUS) between 1985-2020. Tabular output includes information on 1) the area classified as forest in each State by year, 2) the forest area in each frequency class (1 - 36 years) in each State, and 3) the forest area and proportion of total forest area that has changed (or not changed) in each State over the entire time series (1985-2020).

Most companies expect to update their AI models quarterly per a survey conducted in the middle of 2023. This is likely to keep a good and regular schedule without overloading those working on updating the models. Only around two percent of respondents had no plans to update their models. In the fast moving environment of AI, it would likely leave a model critically behind if there was no data updates.

According to a study conducted in 2023, 37 percent of the most popular Android apps and mobile games hosted on the Google Play Store for the United States market updated the screenshots on their description page twice or more times in the past year. In comparison, eight in 10 apps in the Google Play Stores did not update their titles, while 75 percent of apps did not update their icons.

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Developing countries should have submitted their first Biennial Update Report (BUR) by December 2014, according to their capabilities and level of support provided for reporting. The pre-ETF framework required developing country Parties to submit their BUR every two years (decision 1/CP.16, para. 60(c)). As of 2022, many countries have not yet submitted their first BUR. Contrary to the case of National Communication (NC), subsequent BURs were submitted by the majority of countries within the time frame indicated in the decision, most likely due to the institutional set-up and technical capacity built for the NC.

Under the ETF framework, the BUR will be superseded by the biennial transparency report (BTR). Developing countries are required to submit their first BTR by December 2024 and every two years thereafter.

The fact that countries have not succeeded in preparing and submitting a BUR or have not submitted subsequent BURs every two years could indicate that they face challenges in preparing and submitting a BTR and fulfilling the frequency requirement of two years as well. These countries would benefit from additional support to set up proper institutional, legal and procedural arrangements to be able to prepare their BTR in a timely manner.

Initial attack frequency zones are used by pilots and dispatchers for purposes of response to incidents such as wildland fires. Initial attack frequency zones are agreed upon annually by the Communications Duty Officer at the National Interagency Fire Center (NIFC), other frequency managers, and the FAA, and can't be changed during the year without required approval from the CDO at NIFC. Each zone has assigned to it FAA-issued frequencies that are to be used only within the zone boundary. The initial attack frequency zones are delineated to help ensure that frequencies used do not "bleed" over into other incident areas and causing issues for incident communications. The data contains no actual frequencies, but does contain the zones in which they are used. 01/12/2023 - Tabular changes only. Oregon Initial Attack Frequency Zones renumbered per Kim Albracht, Communications Duty Officer, with input from other Northwest personnel. Edits by JKuenzi, USFS. Changes are as follows:OR09 changed to OR02OR02 changed to OR03OR03 changed to OR04OR04 changed to OR05OR05 changed to OR07OR07 changed to OR08OR08 changed to OR09OR01 and OR06 remained unchanged.01/10/2023 - Geospatial and tabular changes made. Two islands on west side of OR05 absorbed into OR03. Change made to both Initial Attack Frequency Zones-Federal and to Dispatch Boundaries per Kaleigh Johnson (Asst Ctr Mgr), Jada Altman (Dispatch Ctr Mgr), and Jerry Messinger (Air Tactical Group Supervisor). Edits by JKuenzi, USFS. 01/09/2023 - Geospatial and tabular changes to align Federal Frequency Zones to Dispatch Area boundaries in Northwest GACC. No alignments made to USWACAC, USWAYAC, or USORWSC. Changes approved by Ted Pierce (NW Deputy Coordination Ctr Mgr), Kaleigh Johnson (Assistant Ctr Mgr), and Kim Albracht (Communications Duty Officer). Edits by JKuenzi, USFS. Specific changes include: WA02 changed to WA04. New WA02 carved out of WA01 and OR01. OR09 carved out of OR01 and OR02. Boundary adjustments between OR07, OR05, and OR03.11/8/2022 - Geospatial and tabular changes. Boundary modified between Big Horn and Rosebud Counties of MT07 and MT08 per KSorenson and KPluhar. Edits by JKuenzi, USFS. 09/06/2022-09/26/2022 - Geospatial and tabular changes in accordance with proposed GACC boundary re-alignments between Southern California and Great Basin in the state of Nevada. Boundary modified between CA03 and NV03, specifically between Queen Valley and Mono Valley. The team making the changes is made up of Southern Calif (JTomaselli) and Great Basin (GDingman) GACCs, with input from Ian Mills and Lance Rosen (BLM). Changes proposed will be put into effect for the 2023 calendar year, and will also impact alignments of GACC boundaries and Dispatch boundaries in the area described. Initial edits provided by Ian Mills and Daniel Yarborough. Final edits by JKuenzi, USFS. A description of the change is as follows: The northwest end of changes start approximately 1 mile west of Mt Olsen and approximately 0.5 mile south of the Virginia Lakes area.Head northwest passing on the northeast side of Red Lake and the south side of Big Virginia Lake to follow HWY 395 North east to CA 270.East through Bodie to the CA/NV state line.Follows the CA/NV State Line south to HWY CA 167/NV 359.East on NV359 to where the HWY intersects the corner of FS/BLM land.Follows the FS/BLM boundary to the east and then south where it ties into the current GACC boundary. 09/07/2022 - 09/08/2022 - Tabular and geospatial changes. Multiple boundaries modified in Northern Rockies GACC to bring Dispatch Boundaries and Initial Attack Frequency Zone lines closer in accordance with State boundaries. Information provided by Don Copple, State Fire Planning & Intelligence Program Manager for Montana Dept of Natural Resources & Conservation (DNRC), Kathy Pipkin, Northern Rockies GACC Center Manager, and Kat Sorenson, R1 Asst Aircraft Coordinator. Edits by JKuenzi, USFS. The following changes were made:Initial Attack Frequency Zone changes made to the following: Dillon Interagency Dispatch Ctr (USMTDDC) (MT03), Helena Interagency Dispatch Ctr (USMTHDC) (MT04), Lewistown Interagency Dispatch Ctr (USMTLEC) (MT06), and Missoula Interagency Dispatch Ctr (USMTMDC) (MT02).Talk was also directed to removing the Initial Attack Frequency Zone line between MT05 and MT07, but that currently remains unchanged until Telecommunications (Kimberly Albracht) can get approval from the Frequency Managers and the FAA.10/15/2021 - Geospatial and tabular changes. Boundary alignments for the Duck Valley Reservation in southern Idaho along the Nevada border. Changes impacting ID02 and NV01. The Duck Valley Reservation remains within NV01. The only change was to the alignment of the physical boundary surrounding the Reservation in accordance with the boundary shown on the 7.5 minute quadrangle maps and data supplied by CClay/JLeguineche/Gina Dingman-USFS Great Basin Coordination Center (GBCC) Center Manager. Edits by JKuenzi, USFS. 9/30/2021 - Geospatial and tabular changes. Boundary alignments for Idaho on Hwy 95 NE of Weiser between Boise Dispatch Center and Payette Interagency Dispatch Center - per CClay/JLeguineche/Gina Dingman-USFS Great Basin Coordination Center (GBCC) Center Manager. Edits by JKuenzi, USFS. Boundary changes at: Weiser (T11N R5W Sec 32), (T11N, R5W, Sec 3), (T12N R5W, Sec 25), and Midvale.9/21/2021 - Geospatial and tabular changes in accordance with proposed GACC boundary re-alignments between Southwestern and Southern GACCs where a portion of Texas, formerly under Southwestern GACC direction was moved to the Southern GACC. Changes to Federal Initial Attack Frequency Zones by Kim Albracht, Communications Duty Officer (CDO) include the following: State designation TXS06 changed to federal TX06.State designation TXS05 changed to federal TX05.State designation TXS04 changed to federal TX04.State designation TXS03 changed to federal TX03.State designation TXS02 changed to federal TX02.State designation TXS01 changed to federal TX01.The Oklahoma Panhandle, formerly TXS01 changed to OK04.All changes proposed for implementation starting in January 2022. Edits by JKuenzi, USFS. See also data sets for Geographic Area Coordination Centers (GACC), and Dispatch Boundary for related changes.8/17/2021 - Tabular changes only. As part of GACC realignment for 2022, area changed from state designation TXS01 to federal TX01 per Kim Albracht, Communications Duty Officer (CDO) at National Interagency Fire Center (NIFC). Edits by JKuenzi, USFS. 2/19/2021 - Geospatial and tabular changes. Boundary changes for Idaho originally submitted in 2016 but never completed in entirety. Changes between Initial Attack Zones ID01 and ID02 and with Dispatch Boundaries - per Chris Clay-BLM Boise, DeniseTolness-DOI/BLM ID State Office GIS Specialist, and Gina Dingman-USFS Great Basin Coordination Center (GBCC) Center Manager. Edits by JKuenzi, USFS. Boundary changes at: (T13N R3E Sec 25), (T15N R3E Sec 31), (T16N R3E Sec 18-20, and 30), and (T16N R2E Sec 13) all from ID02 to ID01. (T10N R4E Sec 4-9,17-18, 20) and (T11N R4E Sec15-16, 21-22, 27-29, 34-31) from ID01 to ID02. 11/10/2020 - Michigan split from MI01 only, to MI01(Upper Penninsula) and MI02 in the south, per Kim Albracht, Communications Duty Officer. No change made to Dispatch Zone Boundary. Edits by JKuenzi. 11/4/2020 - Oregon OR07 divided into OR07 and OR08 per Kim Albracht, Communications Duty Officer. Edits by JKuenzi.10/26/2020 - Multiple boundary changes made to Federal Initial Attack Zones, but without any change to Dispatch Zone Boundaries: Raft River District of Sawtooth National Forest changed from UT01 to ID04; land east of Black Pine District of Sawtooth National Forest changed from ID05 to ID04. Direction from Denise Tolness, DOI/BLM GIS Specialist, and Gina Dingman, Great Basin Coordination Center Manager. Parts of Craters of the Moon National Monument changed from ID04 to ID05; Sheep Mountain (Red Rocks) area changed from MT03 to ID05, per Denise Tolness, Gina Dingman, and Kathryn "Kat" Sorenson, R1 Assistant Aircraft Coordinator. Edits for all changes made by JKuenzi.4/2/2020 - State owned land added and a portion of the boundary modified between MT01 and MT02 per Mike J Gibbons, Flathead Dispatch Center Mgr, and Kathryn "Kat" Sorenson, R1 Assistant Aircraft Coordinator. Edits by JKuenzi.2/21/2020 - Existing boundaries are updated, where possible, to a uniform base layer using the August 2019 Census State & County boundaries, along with Geographic Area Command Center boundaries, Dispatch Zone Boundaries, and Initial Attack State Zones. Edits by JKuenzi.2019-2020 - Initial Attack Frequency Zone data was provided by Kim Albracht, Acting and Permanent Communications Duty Officer (CDO) at National Interagency Fire Center (NIFC), and maintained by Jill Kuenzi, USFS Fire & Aviation Mgt Geospatial Coordinator, NIFC, Boise, ID. Efforts made to tie changes with the Initial Attack Frequency Zones to other closely related datasets such as Geospatial Area Command Centers (GACCs),and Dispatch Areas, Major work completed to bring all the datasets up to date on consistent base data (8/2019 Census data), into alignment where possible, and to establish a scheduled update cycle for the nation. 2017-2019 - Initial Attack Frequency Zone data was provided by Gary Stewart, Communications Duty Officer (CDO) at National Interagency Fire Center (NIFC), and maintained by Jill Kuenzi, USFS Fire & Aviation Mgt Geospatial Coordinator, NIFC, Boise, ID.2015-2016 - Initial Attack Frequency Zone data was provided by Gary Stewart, Communications Duty Officer (CDO) at National Interagency Fire Center (NIFC), and maintained by Dianna Sampson, BLM Geospatial Data Analyst, NIFC, Boise, ID.

This statistic displays the share of responses to the question, "When did your organization most recently update its bring your own device (BYOD) policy?" in Europe in 2015. A six percent share of respondents had last updated their policy more than two years ago.

This Data Release contains an updated version of the San Andreas catalog of low-frequency earthquakes (LFEs): Shelly, D. R. (2017), A 15 year catalog of more than 1 million low-frequency earthquakes: Tracking tremor and slip along the deep San Andreas Fault, J. Geophys. Res. Solid Earth, 122, 3739–3753, doi:10.1002/2017JB014047. This catalog contains 88 LFE families, with each family consisting of events detected by cross-correlation with the associated waveform template. These templates were identified and located by Shelly and Hardebeck (2010): Shelly, D. R., and J. L. Hardebeck (2010), Precise tremor source locations and amplitude variations along the lower-crustal central San Andreas Fault, Geophys. Res. Lett., 37, L14301, doi:10.1029/2010GL043672. For completeness, we repeat the original catalog information provided in the supplement of Shelly (2017) below, with minor modifications: Catalog Time-Period and Format: The low-frequency earthquake catalog spans from April 2001 to 30 April 2024 and contains 1,528,117 events. Format: YYYY MM DD s_of_day HH mm ss.ss ccsum meancc med_cc seqday ID latitude longitude depth n_chan Explantions: YYYY MM DD (year month day) - Event time (template start time in UTC - ~1s prior to first S-wave arrival time at an HRSN station) s_of_day - Event time (template start time in UTC - ~1s prior to first S-wave arrival time at an HRSN station), second of the day (i.e. 0-86400), HH mm ss.ss (hour, minute, second) - Event time (template start time in UTC- ~1s prior to S-wave arrival time at first HRSN station) ccsum - correlation sum across all stations (must exceed 4.0) meancc - mean correlation among stations with data med_cc - median correlation seqday - sequential day since March 1, 2001 ID - reference ID of family latitude longitude depth - estimated location for that family (Shelly and Hardebeck, 2010) n_chan - number of data channels existing for event (some channels may exist, but not have good data) Family IDs: Each family has an associated identification code, which is a number followed by 1-4 ‘s’. The family IDs are almost meaningless and are simply used as unique identifiers. Originally the numeric code was taken from the second of the day at which the initial template for this family occurred. The number of ‘s’ indicates the number of iterations of stacking and cross-correlation that were applied to derive the template waveforms (see Methods). The lower amplitude and more distant sources typically benefitted from multiple iterations of stacking and cross correlation, before the final template stabilized in its detection set. Data channels Used (station.channels): GHIB.13, EADB.123, JCSB.1, FROB.123, JCNB.123, VCAB.123, MMNB.123, CCRB.123, LCCB.123, SMNB.123, RMNB.123, SCYB.123 JCNB failed in 2008 and was replaced by a shallow sensor. New sensor not used. RMNB failed in 2011 and was not replaced. GHIB.2 was never operational JCSB.23 have poor signal to noise and are not used. VARB was replaced with a new sensor at a new depth in 2003, and this station was not used in original template formation. As of 2024, detection capabilities were substantially degraded with a maximum of 16 channels of data available for detection. This is due to outages in GHIB (since 2020), FROB (since 2023), VCAB (since 2023), and CCRB (since 2022), in addition to the outages described above. It is unclear when/if these stations might be repaired in the future. Channel swap on FROB, VCAB (after BP->SP channel swap, before 2011-7-14): 2011/4/21-2011/7/14: Swap VCAB.2 and VCAB.3 2010/11/10-2011/7/14: Swap FROB.2 and FROB.3 Disregard mean correlation, enforce network correlation sum only (because of poor but present data): 2012/2/13-2014/4/23 Polarity corrections during initial processing: CCRB.123, correct for reversed polarity from 2001-6-1 to 2001-12-13. FROB.123, correct for reverse polarity from 2010/12/10-2011/4/7 MMNB.123, correct for reverse polarity from 2010/12/10-2011/4/7 FROB.23, correct for reverse polarity from 2010/4/8 to 2011-7-14 Polarity corrections applied in post-processing (these are minor and done after initial detection stage): 2011-4-7 to 2011-5-27: zero FROB.2 channel (wiring mistake, FROB.2 duplicates FROB.3) 2005/4/11-2005/5/13: reverse GHIB.13 2005/12/15-present: reverse GHIB.3 2002/11/22-2003/1/16: reverse EADB.2 2002/11/21-2003/1/17: reverse VCAB.3

This dataset shows whether each dataset on data.maryland.gov has been updated recently enough. For example, datasets containing weekly data should be updated at least every 7 days. Datasets containing monthly data should be updated at least every 31 days. This dataset also shows a compendium of metadata from all data.maryland.gov datasets.

This report was created by the Department of Information Technology (DoIT) on August 12 2015. New reports will be uploaded daily (this report is itself included in the report, so that users can see whether new reports are consistently being uploaded each week). Generation of this report uses the Socrata Open Data (API) to retrieve metadata on date of last data update and update frequency. Analysis and formatting of the metadata use Javascript, jQuery, and AJAX.

This report will be used during meetings of the Maryland Open Data Council to curate datasets for maintenance and make sure the Open Data Portal's data stays up to date.

Drought Frequency change This service has been created to be used with application https://discomap.eea.europa.eu/climate and should be used in that context. Please contact us (discomap@eea.europa.eu) if you want to reuse

This dataset shows whether each dataset on data.maryland.gov has been updated recently enough. For example, datasets containing weekly data should be updated at least every 7 days. Datasets containing monthly data should be updated at least every 31 days. This dataset also shows a compendium of metadata from all data.maryland.gov datasets.

This report was created by the Department of Information Technology (DoIT) on August 12 2015. New reports will be uploaded daily (this report is itself included in the report, so that users can see whether new reports are consistently being uploaded each week). Generation of this report uses the Socrata Open Data (API) to retrieve metadata on date of last data update and update frequency. Analysis and formatting of the metadata use Javascript, jQuery, and AJAX.

This report will be used during meetings of the Maryland Open Data Council to curate datasets for maintenance and make sure the Open Data Portal's data stays up to date.

This statistic shows the frequency mid-market businesses update their mobile applications in Europe in 2016. The majority of respondents updated their mobile apps every month with a total of 25 percent.

This data release consists of Microsoft Excel workbooks, shapefiles, and a figure (png format) related to a cooperative project between the U.S. Geological Survey (USGS) and the Florida Flood Hub for Applied Research and Innovation at the University of South Florida to derive projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future (2020-59 or 2050-89) to historical (1966-2005) extreme-precipitation depths fitted to extreme-precipitation data using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors are tabulated by duration (1, 3, and 7 days) and return period (5, 10, 25, 50, 100, 200, and 500 years). The official historical NOAA Atlas 14 DDF curves based on partial-duration series (PDS) can be multiplied by the change factors derived in this project to determine projected future extreme precipitation for events of a given duration and return period. Various statistical, dynamical and hybrid downscaled precipitation datasets were used to derive the change factors at the grid cells closest to the NOAA Atlas 14 stations including (1) the Coordinated Regional Downscaling Experiment (CORDEX), (2) the Localized Constructed Analogues (LOCA) dataset, (3) the Multivariate Adaptive Constructed Analogs (MACA) dataset, (4) the Analog Resampling and Statistical Scaling Method by Jupiter Intelligence using the Weather Research and Forecasting Model (JupiterWRF). The emission scenarios evaluated include representative concentration pathways RCP4.5 and RCP8.5 from the Coupled Model Intercomparison Project Phase 5 (CMIP5) for the downscaled climate datasets CORDEX, LOCA, and MACA. The emission scenarios evaluated for the JupiterWRF downscaled dataset include RCP8.5 from CMIP5, and shared socioeconomic pathways SSP2-4.5 and SSP5-8.5 from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Only daily durations are evaluated for JupiterWRF. When applying change factors to the historical NOAA Atlas 14 DDF curves to derive projected future precipitation DDF curves for the entire range of durations and return periods evaluated as part of this project, there is a possibility that the resulting projected future DDF curves may be inconsistent across duration and return period. By inconsistent it is meant that the precipitation depths may decrease for longer durations instead of increasing. Depending on the change factors used, this may happen in up to 6% of cases. In such a case, it is recommended that users use the higher of the projected future precipitation depths derived for the duration of interest and the previous shorter duration. This data release consists of three shapefiles: (1) polygons of climate regions (Climate_regions.shp); (2) polygons of Areal Reduction Factor (ARF) regions for the state of Florida (ARF_regions.shp); and (3) point locations of NOAA Atlas 14 stations in Florida for which depth-duration-frequency curves and change factors of precipitation depths were developed as part of this project (Atlas14_stations.shp). This data release also includes 35 tables. Eight tables tables contain computed change factors for the four downscaled climate datasets for the two future projection periods: (1) CORDEX 2020-59 (CF_CORDEX_2040_to_historical.xlsx); (2) CORDEX 2050-89 (CF_CORDEX_2070_to_historical.xlsx);(3) LOCA 2020-59 (CF_LOCA_2040_to_historical.xlsx); (4) LOCA 2050-89 (CF_LOCA_2070_to_historical.xlsx);(5) MACA 2020-59 (CF_MACA_2040_to_historical.xlsx); (6) MACA 2050-89 (CF_MACA_2070_to_historical.xlsx); (7) JupiterWRF 2038-42 (CF_JupiterWRF_2040_to_historical.xlsx); and (8) JupiterWRF 2068-72 (CF_JupiterWRF_2070_to_historical.xlsx). Twelve tables contain the corresponding DDF values for the historical and future projection periods in each of the four downscaled climate datasets: (1) CORDEX historical (DDF_CORDEX_historical.xlsx); (2) CORDEX 2020-59 (DDF_CORDEX_2040.xlsx); (3) CORDEX 2050-89 (DDF_CORDEX_2070.xlsx); (4) LOCA historical (DDF_LOCA_historical.xlsx); (5) LOCA 2020-59 (DDF_LOCA_2040.xlsx); (6) LOCA 2050-89 (DDF_LOCA_2070.xlsx); (7) MACA historical (DDF_MACA_historical.xlsx); (8) MACA 2020-59 (DDF_MACA_2040.xlsx); (9) MACA 2050-89 (DDF_MACA_2070.xlsx); (10) JupiterWRF historical (DDF_JupiterWRF_historical.xlsx); (11) JupiterWRF 2038-42 (DDF_JupiterWRF_2040.xlsx); and (12) JupiterWRF 2068-72 (DDF_JupiterWRF_2070.xlsx). Twelve tables contain quantiles of change factors at 242 NOAA Atlas 14 stations in Florida derived from downscaled climate datasets considering: (1) all models and all emission scenarios evaluated for 2020-59 (CFquantiles_2040_to_historical_all_models_allRCPs.xlsx); (2) all models and all emission scenarios evaluated for 2050-89 (CFquantiles_2070_to_historical_all_models_allRCPs.xlsx); (3) all models and only the RCP4.5 and SSP2-4.5 emission scenarios for 2020-59 (CFquantiles_2040_to_historical_all_models_RCP4.5.xlsx); (4) all models and only the RCP4.5 and SSP2-4.5 emission scenarios for 2050-89 (CFquantiles_2070_to_historical_all_models_RCP4.5.xlsx); (5) all models and only the RCP8.5 and SSP5-8.5 emission scenarios for 2020-59 (CFquantiles_2040_to_historical_all_models_RCP8.5.xlsx); (6) all models and only the RCP8.5 and SSP5-8.5 emission scenarios for 2050-89 (CFquantiles_2070_to_historical_all_models_RCP8.5.xlsx); (7) best models and all emission scenarios evaluated for 2020-59 (CFquantiles_2040_to_historical_best_models_allRCPs.xlsx); (8) best models and all emission scenarios evaluated for 2050-89 (CFquantiles_2070_to_historical_best_models_allRCPs.xlsx); (9) best models and only the RCP4.5 and SSP2-4.5 emission scenarios for 2020-59 (CFquantiles_2040_to_historical_best_models_RCP4.5.xlsx); (10) best models and only the RCP4.5 and SSP2-4.5 emission scenarios for 2050-89 (CFquantiles_2070_to_historical_best_models_RCP4.5.xlsx); (11) best models and only the RCP8.5 and SSP5-8.5 emission scenarios for 2020-59 (CFquantiles_2040_to_historical_best_models_RCP8.5.xlsx); and (12) best models and only the RCP8.5 and SSP5-8.5 emission scenarios for 2050-89 (CFquantiles_2070_to_historical_best_models_RCP8.5.xlsx). Finally, three tables contain miscellaneous information: (1) information about downscaled climate datasets and National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations used in this project (Datasets_station_information.xlsx); (2) best models for each downscaled climate dataset and for all downscaled climate datasets considered together (Best_model_lists.xlsx); and (3) areal reduction factors by region in Florida (Areal_reduction_factors.xlsx). An R script is provided which generates boxplots of change factors at a NOAA Atlas 14 station, or for all NOAA Atlas 14 stations in an ArcHydro Enhanced Database (AHED) basin or county (create_boxplot.R). A Microsoft Word file documenting code usage and available options is also provided within this data release (Documentation_R_script_create_boxplot.docx). Disclaimer: As a reminder, projected future (2020-59 and 2050-89) and historical (1966-2005) DDF curves fitted to extreme-precipitation data from models in each downscaled climate dataset are provided as part of this data release as a way to verify the computed change factors. However, these model-based projected future and historical DDF curves are expected to be biased and only their ratio (change factor) is considered a reasonable approximation of how historically-observed DDF depths might be multiplicatively amplified or muted in the future periods 2020-59 and 2050-89. Some very high outlier change factor values may occur due to the presence of a single very large extreme event in the future period selected for analysis (which causes a very long tail in the fitted distribution) or they may be due to non-convergence of the fitting process. In general, these very high outliers occur extremely rarely in the more than two million change factors computed as part of this project. For example, only 0.008% of the change factors are greater than 8, while only 0.26% of the change factors are greater than 4. The very high outlier values are also more common in the MACA dataset and for the longest return periods which are more uncertain. Version 1.1 Changes from previous version: A bug in R script create_boxplot.R was identified and fixed in this version. The bug resulted in the script failing to generate the outlier csv file if a basin or county was defined as including some stations with JupiterWRF change factors available, and some stations without JupiterWRF change factors. Revision History: First release: July 2023 Version 1.1: September 2023

Statistics for VideoLectures related to Data Science

We summarized annual remote sensing land cover classifications from the U.S. Geological Survey Land Cover Monitoring, Assessment, and Projection (LCMAP) annual time series to characterize forest change across the conterminous United States (CONUS) for the years 1985-2020. The raster output includes a map where each pixel is given an integer value based on the number of years in which it was classified as forest across the annual LCMAP time series. Values of 36 indicate the pixel was classified as forest across all years while a value of 0 indicates forests (tree cover) was never detected during the time series.