AndreaBozzo/ceres-open-data-index huggingface dataset:
Elk Home Range - Maple Creek - 2018-2021 [ds2991]

<div style='text-align:Left;'><div><div><p style='margin:0 0 11 0;'><span><span>The project lead for the collection of this data was Carrington Hilson. Elk (3 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2018-2021. The Maple Creek herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. </span></span></p><p style='margin:0 0 11 0;'><span><span>The methodology used for this migration analysis allowed for the mapping of the herd’s home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 3 elk, including 7 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Large water bodies were clipped from the final output. Home range is visualized as the 50</span></span><span><span>th</span></span><span><span> percentile contour (high use) and the 99</span></span><span><span>th</span></span><span><span> percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.</span></span></p></div></div></div>
Elk Home Range - McAdams - 2017-2021 [ds2992]

<div style='text-align:Left;'><div><div><p style='margin:0 0 11 0;'><span><span>The project lead for the collection of this data was Carrington Hilson. Elk (2 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2017-2021. The McAdams herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. </span></span></p><p style='margin:0 0 11 0;'><span><span>The methodology used for this migration analysis allowed for the mapping of the herd’s home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 2 elk, including 6 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Large water bodies were clipped from the final output. Home range is visualized as the 50</span></span><span><span>th</span></span><span><span> percentile contour (high use) and the 99</span></span><span><span>th</span></span><span><span> percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.</span></span></p></div></div></div>
California High Hazard Zones (Tier 2)

On October 30, 2015 Governor Brown issued an emergency declaration 
requiring public agencies to identify areas of tree mortality that hold 
the greatest potential to result in wildfire and/or falling trees and 
threaten people and property in these areas. Once identified, these 
areas will be prioritized for removal of dead and dying trees that 
present a threat to public safety. <br /><div><br /><div>Tier Two high hazard zones are defined by watersheds (HUC 12, average 
24,000 acres) that have significant tree mortality as well as 
significant community and natural resource assets. Work at the Tier Two
level addresses the immediate threat of falling trees and fire risk, and
 also supports broader forest health and landscape level fire planning 
issues.</div><div><br /></div>This service represents the latest official release of HHZ. It will be updated annually when a new version is released. As of June 2019, it represents HighHazardZones19_1.<br /></div>
Elk Home Range - OSOC - 2017-2021 [ds2993]

<div style='text-align:Left;'><div><div><p style='margin:0 0 11 0;'><span>The project lead for the collection of this data was Carrington Hilson. Elk (3 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2017-2021. The Old South Operations Center (OSOC) herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. </span></p><p style='margin:0 0 11 0;'><span><span>The methodology used for this migration analysis allowed for the mapping of the herd’s home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 3 elk, including 5 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Large water bodies were clipped from the final output. Home range is visualized as the 50</span></span><span><span>th</span></span><span><span> percentile contour (high use) and the 99</span></span><span><span>th</span></span><span><span> percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.</span></span></p></div></div></div>
Elk Home Range - Red Schoolhouse - 2017-2021 [ds2994]

<div style='text-align:Left;'><div><div><p style='margin:0 0 11 0;'><span><span>The project lead for the collection of this data was Carrington Hilson. Elk (3 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2017-2021. The Red Schoolhouse herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. </span></span></p><p style='margin:0 0 11 0;'><span><span>The methodology used for this migration analysis allowed for the mapping of the herd’s home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 3 elk, including 7 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Large water bodies were clipped from the final output. Home range is visualized as the 50</span></span><span><span>th</span></span><span><span> percentile contour (high use) and the 99</span></span><span><span>th</span></span><span><span> percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.</span></span></p></div></div></div>
Elk Home Range - Rowdy - 2017-2021 [ds2996]

<div style='text-align:Left;'><div><div><p style='margin:0 0 11 0;'><span>The project lead for the collection of this data was Carrington Hilson. Elk (4 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2017-2021. The Rowdy herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. </span></p><p style='margin:0 0 11 0;'><span>The methodology used for this migration analysis allowed for the mapping of the herd’s home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 4 elk, including 7 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Large water bodies were clipped from the final output. Home range is visualized as the 50thpercentile contour (high use) and the 99thpercentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.</span></p></div></div></div>
Elk Home Range - Sinkyone - 2019-2021 [ds2997]

<div style='text-align:Left;'><div><div><p style='margin:0 0 11 0;'><span>The project lead for the collection of this data was Carrington Hilson. Elk (2 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2019-2021. The Sinyone herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. </span></p><p style='margin:0 0 11 0;'><span>The methodology used for this migration analysis allowed for the mapping of the herd’s home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 2 elk, including 3 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Large water bodies were clipped from the final output. Home range is visualized as the 50</span><span>th</span><span>percentile contour (high use) and the 99</span><span>th</span><span>percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.</span></p></div></div></div>
Elk Home Range - Timmons - 2017-2021 [ds2998]

<div style='text-align:Left;'><div><div><p style='margin:0 0 11 0;'><span>The project lead for the collection of this data was Carrington Hilson. Elk (2 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2017-2021. The Timmons herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. </span></p><p style='margin:0 0 11 0;'><span>The methodology used for this migration analysis allowed for the mapping of the herd’s home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 2 elk, including 6 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of les than 27 hours. Large water bodies were clipped from the final output. Home range is visualized as the 50</span><span>th</span><span>percentile contour (high use) and the 99</span><span>th</span><span>percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.</span></p></div></div></div>
Elk Home Range - Tolowa - 2017-2021 [ds2999]

<div style='text-align:Left;'><div><div><p style='margin:0 0 11 0;'><span>The project lead for the collection of this data was Carrington Hilson. Elk (8 adult females) were captured and equipped with GPS collars (Lotek Iridium) transmitting data from 2017 to 2021. The Tolowa herd does not migrate between traditional summer and winter seasonal ranges. Therefore, annual home ranges were modeled using year-round data to demarcate high use areas in lieu of modeling the specific winter ranges commonly seen in other ungulate analyses in California. GPS locations were fixed between 1-6 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual pronghorn is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. </span></p><p style='margin:0 0 11 0;'><span>The methodology used for this migration analysis allowed for the mapping of the herd’s home range. Brownian bridge movement models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 8 elk, including 18 annual home range sequences, location, date, time, and average location error as inputs in Migration Mapper. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to a high variance parameter (greater than 8000), one home range sequence was run with a fixed motion variance of 1000. Large water bodies were clipped from the final output. Home range is visualized as the 50</span><span>th</span><span><span>percentile contour (high use) and the 99</span></span><span><span>th</span></span><span>percentile contour of the year-round utilization distribution. Home range designations for this herd may expand with a larger sample.</span></p></div></div></div>
NCCP Gains & Losses - San Diego [ds892]

<div style='text-align:Left;'><div><div><p><span>An NCCP identifies and provides for the regional protection of plants, animals, and their habitats, while allowing compatible and appropriate economic activity. Working with landowners, environmental organizations, and other interested parties, a local agency oversees the numerous activities that compose the development of an NCCP. CDFW and the U.S. Fish and Wildlife Service provide the necessary support, direction, and guidance to NCCP participants. HabiTrak consolidates the NCCP Habitat Gains and Losses into a single system.</span></p></div></div></div>
State Highway Network Postmiles Tenth

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Postmiles are valid postmile points at 0.1 (1/10th) mile intervals, based on the Caltrans Linear Referencing System. Exceptions that are included are the beginning and ends of routes (and other situations) that don't necessarily begin or end on the even 0.1 mile interval. This geometry is based upon the extraction from TSN on 8 June 2025. . Route geometry updates based upon this extraction include routes 3, 36, 38, 49, 89, 139, 174, and 221. All updates received an update date of 6/8/2025 except for route 221 which received a date of 7/1/2025.</SPAN></P></DIV></DIV></DIV>
State Highway Network Lines

<DIV STYLE="text-align:Left;font-size:12pt"><DIV><DIV><P><SPAN>StateHighways are state highways based upon the Caltrans Linear Referencing System. Each record in this layer represents a highway segment where the county, route, postmile prefix, and postmile suffix are the same. Each segment has a begin and end postmile. This geometry is based upon the extraction from TSN on 8 June 2025. Route geometry updates based upon this extraction include routes 3, 36, 38, 49, 89, 139, 174, and 221. All updates received an update date of 6/8/2025 except for route 221 which received a date of 7/1/2025.</SPAN></P></DIV></DIV></DIV>
California Bumble Bee Atlas Sampling Grids - 2022 [ds3004]

<div style='text-align:Left;'><div><div><p><span>The California Bumble Bee Atlas sampling grid cell map for 2022. Trained volunteers participate in the atlas by adopting a grid cell to conduct at least two surveys in 2022. The project launches on three different dates based on the start of bumble bee flight season: March 15th in southern California, April 15th in central and coastal California, and May 15th everywhere else.</span></p><p><span>For more information please visit </span><a href='https://www.cabumblebeeatlas.org:443/' style='text-decoration:underline;' rel='nofollow ugc'><span>https://www.cabumblebeeatlas.org/</span></a><span>. </span></p></div></div></div>
DWC COVID-19 Claims Data

The Division of Workers’ Compensation (DWC), Workers’ Compensation Information System (WCIS) has been collecting First Reports of Injury (FROI) and Subsequent Reports of Injury (SROI) submitted electronically by claims administrators and their trading partners using the International Association of Industrial Accident Boards and Commissions (IAIABC) FROI/SROI Release 1.0 standard since 2000. The numbers reflect the WCIS database information as of the run date of the report.
California Metropolitan Planning Organization (MPO)

<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This dataset contains the geographic boundaries of Metropolitan Planning Organizations (MPO) and is intended for metropolitan area multimodal transportation planning applications. MPOs designate the federally mandated and federally funded transportation policy-making organization in the United States. This dataset is for California only and is maintained by the Bureau of Transportation Statistics (BTS), but it has been modified by Caltrans to conform to the  California County Boundary dataset. MPO organizations include representatives from local government and governmental transportation authorities and they carry out the metropolitan transportation planning process in that area. </SPAN></P></DIV></DIV></DIV>
Regional Transportation Planning Agencies

<div style="text-align:Left;"><div><div><p><span>The RTPA polygon feature class provides California Regional Transportation Planning Agencies (RTPA) legislative boundaries, primarily for regional planning applications.</span></p><p><span>The list of California RegionalTransportation Planning Agencies is current as of February, 2014, provided by Division of Transportation Planning, Office of Regional and Interagency Planning. With the exception of Tahoe Regional Planning Agency (TRPA*), all of the RTPA boundaries follow county boundaries, some RTPA are multi-county. This feature class was created primarily based on 2010 Census county lines exclude the islands.</span></p><p><span>*TRPA is actually a bi-state agency created by the U.S. Congress and a compact between California and Nevada. Federal laws and California and Nevada statutes govern it. TRPA is composed of parts of two counties in California, El Dorado and Placer, and two counties in Nevada. The portions of two California counties outside the Tahoe basin are part of the Sacramento Area Council of Governments (SACOG).</span></p><p><span>Reference: CALCOG GUIDE TO REGIONAL PLANNING, AS REVISED BY SB 375 JANUARY 2009.</span></p></div></div></div>