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Dataset Card for Data Science Job Salaries
Dataset Summary
Content
Column Description
work_year The year the salary was paid.
experience_level The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director
employment_type The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/data-science-job-salaries.
https://www.myvisajobs.com/terms-of-service/https://www.myvisajobs.com/terms-of-service/
A dataset that explores Green Card sponsorship trends, salary data, and employer insights for senior business analyst in the U.S.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Department for the Aging (DFTA) - Senior Center Local Law 140 Client Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ca1cc3ce-a025-47da-a80c-2c7b3e3b35ba on 12 February 2022.
--- Dataset description provided by original source is as follows ---
Client participation at senior centers. This information is required by Local Law 140 to provide to the city council and publish on DFTA's website and open data source. It contents data on senior center daily participation at the senior centers.
--- Original source retains full ownership of the source dataset ---
https://www.myvisajobs.com/terms-of-service/https://www.myvisajobs.com/terms-of-service/
A dataset that explores Green Card sponsorship trends, salary data, and employer insights for senior programmer analyst in the U.S.
This blog post was posted by Kevin Duvall on December 18, 2020. It was written by Heather Strosnider, Co-Lead, Integrated Surveillance, Centers for Disease Control and Prevention (CDC)/Joint Coordination Cell; Kelly Bennett, Co-Lead, Integrated Surveillance, Assistant Secretary for Preparedness and Response (ASPR)/Joint Coordination Cell; Amy Gleason, Data Strategy and Execution Workgroup Lead, US Digital Service/Joint Coordination Cell; Kristen Honey, Chief Data Scientist and Senior Advisor to Assistant Secretary for Health (ASH), HHS; and Kevin Duvall, Deputy Chief Data Officer (CDO), Office of the CDO, U.S. Department of Health & Human Services (HHS).
The surveys contain quantitative data on position, seniority, subject area, contract type, salary, career history and some demographics (age, gender, family status). In addition there were a range of open-ended questions relating to experiences of employment, expectations for careers and views on what leads to success. A significant part of the project was to undertake a coding exercise of all of the open-ended questions in the survey.
There are two SPSS data files – 4,282 in Higher Education and 2,444 in Research Institutes – covering 70/75 questions in HE and RI respectively from the survey. There are a further 300 variables, mostly indicator variables, which were derived in the quantitative and qualitative analysis. This project investigates the career patterns of research scientists in the UK using data collected by the Athena Survey of Science Engineering and Technology. It aims to identify the factors associated with a successful career, and to examine why the experiences of men and women in the profession differ so significantly. Specifically, women take home only 80% of the earnings of their male counterparts and, though they account for a third of the country’s research scientists, compose only 2% of the highest grades. It compares the experience of researchers employed by three different types of organisation, universities, research institutes and industry, and will assess the impact of each on career opportunity, progression and pay. The analysis of the factors determining pay and promotion will control for age, seniority, subject area and employer. It will utilise the descriptions that people give of their usual tasks and responsibilities, details of involvement in research projects, editing journals as indicators of productivity and prestige. This regression analysis will be supplemented by a qualitative analysis of what scientists report about their employment conditions and work environment, and how this has affected their career. We find evidence that female scientists in the UK face glass ceilings both in terms of pay and promotion. Not only do women earn less because they are less likely to be promoted, they are also likely to earn less when they are employed within the same grades. Interestingly, the point at which women hit the glass ceiling depends upon institutions. In Universities the glass ceiling is thickest at the point of promotion from senior lecturer to professor, a typical glass ceiling, whereas in Research Institutes women seem to face disadvantage in obtaining promotion from scientist (post-doc) to senior scientist, perhaps better described as a sticky floor. In both cases, these are the most demanding promotions but ceteris paribus they are significantly more demanding for women.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Senior Centers [arcgis_rest_services_ploi_senior_ctr_MapServer_0]’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/156c66e9-cf80-401a-ba9c-286cfb0de5ec on 27 January 2022.
--- Dataset description provided by original source is as follows ---
https://gis3.montgomerycountymd.gov/arcgis/rest/services/ploi/senior_ctr/MapServer/0
--- Original source retains full ownership of the source dataset ---
https://fred.stlouisfed.org/legal/#copyright-pre-approvalhttps://fred.stlouisfed.org/legal/#copyright-pre-approval
Graph and download economic data for Software Development Job Postings on Indeed in the United States (IHLIDXUSTPSOFTDEVE) from 2020-02-01 to 2025-03-21 about software, jobs, and USA.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Espaces seniors de l'Aude’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/60e8541eb523fa1738decb05 on 12 January 2022.
--- Dataset description provided by original source is as follows ---
Liste des Espaces seniors du Département de l'Aude
--- Original source retains full ownership of the source dataset ---
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Výsledky prednostného hlasovania za SR’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/bc7f38b3-1b49-4061-b372-447707e5714d on 16 January 2022.
--- Dataset description provided by original source is as follows ---
Výsledky volieb do Národnej rady SR 2006
--- Original source retains full ownership of the source dataset ---
This dataset contains current job postings available on the City of New York’s official jobs site (http://www.nyc.gov/html/careers/html/search/search.shtml). Internal postings available to city employees and external postings available to the general public are included.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Earnings of females and males employees.’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mpwolke/cusersmarildownloadsearningcsv on 28 January 2022.
--- Dataset description provided by original source is as follows ---
The Bureau of Labor Statistics reported that, in 2013, female full-time workers had median weekly earnings of $706, compared to men's median weekly earnings of $860. Women aged 35 years and older earned 74% to 80% of the earnings of their male counterparts. https://en.wikipedia.org › wiki › Gender_pay_gap_in_the_United_States
What is the gender pay gap 2019? Study after study has identified a persistent gender pay gap. A PayScale report found that women still make only $0.79 for each dollar men make in 2019. A Bureau of Labor Statistics (BLS) analysis discovered that in 2018, median weekly earnings for female full-time wage and salary workers was 81% of men's earnings.Jul 11, 2019 https://www.forbes.com/sites/shaharziv/2019/07/11/gender-pay-gap-bigger-than-you-thnk/#36ca335f7d8a.
Linked through data.gov.au for discoverability and availability. This dataset was originally found on data.gov.au https://data.gov.au/data/dataset/a5776c56-bdde-4643-a3fd-dcc2775d7d7a ***Photo by Samantha Sophia on Unsplash.
Great females scientists: Mileva Maric', Frances "Poppy" Northcut, Hedy Lamarr, Marie Sklodowska Curie and Ada Lovelace. If you don't know them yet, just search on Google.
--- Original source retains full ownership of the source dataset ---
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Butte County Land Use Survey 2004’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/e3e4d389-a9ca-470a-9040-a13d22e59d3e on 28 January 2022.
--- Dataset description provided by original source is as follows ---
This map is designated as Final.
Land-Use Data Quality Control
Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.
Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legend specific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.
Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.
The 2004 Butte County land use survey data set was developed by DWR through its Division of Planning and Local Assistance (DPLA). The data was gathered using aerial photography and extensive field visits. The land use boundaries and attributes were digitized and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s Northern District (ND). Quality control procedures were performed jointly by staff at DWR’s DPLA headquarters and ND, under the supervision of Tito Cervantes, Senior Land and Water Use Scientist. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Butte County conducted by DWR, Northern District Office staff, under the leadership of Tito Cervantes, Senior Land and Water Use Supervisor. The field work for this survey was conducted during the summer of 2004. ND staff physically visited each delineated field, noting the crops grown at each location. Field survey boundary data was developed using: 1. The aerial photography used for this survey was taken in June of 2004. 9”x9” color photographs were generated from an altitude of about 6,000 feet above ground to produce a 1:24,000 scale photo. 2. The 9”x9” photos were taken to the field and virtually all the areas were visited to positively identify the land use. Site visits occurred July through September 2004. Land use codes were hand written on the photos. 3. Using AUTOCAD, the land use boundaries were digitized from USGS Digital Orthophoto Quarter Quadrangles (DOQQs) and attributes were entered from the field photos (using a standardized digitizing process). 4. After quality control/assurance procedures were completed on each file (DWG), the data was finalized. 5. The linework and attributes from each DWG quad file were brought into ARCINFO and both quad and survey wide coverages were created, and underwent quality checks. The survey wide coverage was then converted to a shapefile using ARCVIEW. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed, especially in forested areas. Before final processing, standard quality control procedures were performed jointly by staff at DWR's Northern District, and at DPLA headquarters under the leadership of Jean Woods, Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the 9' x 9' color photos, is approximately 23 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.
--- Original source retains full ownership of the source dataset ---
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘IH202 - Persons aged 65 and older’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/08e8175a-daad-429b-976d-509d554b09ea on 12 January 2022.
--- Dataset description provided by original source is as follows ---
Persons aged 65 and older
--- Original source retains full ownership of the source dataset ---
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Del Norte County Land Use Survey 2006’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/e6c0678e-9a08-4107-8ac7-d5d630259aa7 on 26 January 2022.
--- Dataset description provided by original source is as follows ---
This map is designated as Final.
Land-Use Data Quality Control
Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.
Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.
Provisional datasets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.
The 2006 Del Norte County land use survey data set was developed by DWR through its Division of Planning and Local Assistance which, following reorganization in 2009 has been subdivided into the Division of Statewide Integrated Water Management (DSIWM) and the Division of Integrated Regional Water Management (DIRWM). The data was gathered using aerial photography and extensive field visits. The land use boundaries and attributes were digitized and the resultant data went through standard quality control procedures before finalizing. The land uses that were gathered were detailed agricultural land uses, and lesser detailed urban and native vegetation land uses. The data was gathered and digitized by staff of DWR’s Northern Regional Office. Quality control procedures were performed jointly by staff at DWR’s Statewide Integrated Water Management headquarters and Northern Regional Office, under the supervision of Tito Cervantes, Senior Land and Water Use Scientist. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Butte County conducted by DWR, Northern District Office staff, under the leadership of Tito Cervantes, Senior Land and Water Use Supervisor. The field work for this survey was conducted during the summer of 2004. ND staff physically visited each delineated field, noting the crops grown at each location. Field survey boundary data was developed using: 1. The county was surveyed using the 2005 one-meter resolution National Agriculture Imagery Program (NAIP) digital aerial photos as a digital reference for line work and field work. 2. From the 2005 NAIP imagery, digital 7.5’quadrangle sized images were created, with one-meter resolution. These were used in the spring of 2006 to develop the digital land use boundaries that would be used in the survey. The digitizing of these boundaries was done using AutoCAD Map software. 3. The digital images and land use boundaries were copied onto laptop computers that, in most cases, were used as the field data collection tools. The staff took these laptops into the field and virtually all the areas were visited to positively identify the agricultural land use. The site visits occurred between June and August 2006. Land use codes were digitized directly into the laptop computers using AUTOCAD (using a standardized digitizing process). Some staff took the printed aerial photos into the field and wrote land use codes directly onto these photo field sheets. The data from the photo field sheets were digitized back in the office. For both data gathering techniques any land use boundary changes were noted and corrected in the office. Urban and native classes of land use were mapped by both field observation and photo interpretation. 4. The linework and attributes from each quadrangle drawing file were brought into ARCINFO and both quadrangle and survey-wide coverages were created, and underwent quality checks. These coverages were converted to shapefiles using ArcMAP. 5. After quality control/assurance procedures were completed on each file, the data was finalized. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed, especially in forested areas. Before final processing, standard quality control procedures were performed jointly by staff at DWR's Northern District, and at DPLA headquarters under the leadership of Jean Woods, Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the 2005 one-meter resolution National Agriculture Imagery Program (NAIP), is approximately 12.1 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.
--- Original source retains full ownership of the source dataset ---
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Department for the Aging (DFTA) Reported Service Units by Month of Senior Center Contracted Providers’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d86885a6-fde0-4f9b-90ac-f5e5c997cce1 on 28 January 2022.
--- Dataset description provided by original source is as follows ---
Reported Units of Service by Month of registered and contracted agencies providing Senior Center Services.
--- Original source retains full ownership of the source dataset ---
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Care places for older people with dependency or social risk: assisted living homes’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-analisi-transparenciacatalunya-cat-api-views-cjhy-4fxg on 08 January 2022.
--- Dataset description provided by original source is as follows ---
Les places en habitatges tutelats per a la gent gran recollides en aquest dataset formen part del mapa de serveis socials de Catalunya, concretament dels serveis d’atenció especialitzada.
Es tracta de places finançades, i la data de referència és el 31 de desembre de cada any.
--- Original source retains full ownership of the source dataset ---
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Zoznam faktúr’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/2d500022-743b-454c-af99-dba77847b563 on 11 January 2022.
--- Dataset description provided by original source is as follows ---
Faktúry Štatistického úradu SR
--- Original source retains full ownership of the source dataset ---
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Analysis of ‘Offshore Oil Leases’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7dbfeba0-c6d3-4ccd-9885-9e6751da10c4 on 27 January 2022.
--- Dataset description provided by original source is as follows ---
California State Lands Commission Offshore Oil Leases in the vicinity of Santa Barbara, Ventura, and Orange County.
The polygons in this layer show the position of Offshore Oil Leases as documented by former State Lands Senior Boundary Determination Officer, Cris N. Perez and as reviewed and updated by GIS and Boundary staff.
Background:
This layer represents active offshore oil and gas agreements in California waters, which are what remain of the more than 60 originally issued. These leases were issued prior to the catastrophic 1969 oil spill from Platform A in federal waters off Santa Barbara County, and some predate the formation of the Commission. Between 2010 and 2014, the bulk of the approximately $300 million generated annually for the state's General Fund from oil and gas agreements was from these offshore leases.
In 1921, the Legislature created the first tidelands oil and gas leasing program. Between 1921 and 1929, approximately 100 permits and leases were issued and over 850 wells were drilled in Santa Barbara and Ventura Counties. In 1929, the Legislature prohibited any new leases or permits. In 1933, however, the prohibition was partially lifted in response to an alleged theft of tidelands oil in Huntington Beach. It wasn't until 1938, and again in 1955, that the Legislature would allow new offshore oil and gas leasing. Except for limited circumstances, the Legislature has consistently placed limits on the areas that the Commission may offer for lease and in 1994, placed the entirety of California's coast off-limits to new oil and gas leases.
Layer Creation Process:
In 1997 Cris N. Perez, Senior Boundary Determination Officer of the Southern California Section of the State Lands Division, prepared a report on the Commission’s Offshore Oil Leases to:
A. Show the position of Offshore Oil Leases.
B. Produce a hard copy of 1927 NAD Coordinates for each lease.
C. Discuss any problems evident after plotting the leases.
Below are some of the details Cris included in the report:
I have plotted the leases that were supplied to me by the Long Beach Office and computed 1927 NAD California Coordinates for each one. Where the Mean High Tide Line (MHTL) was called for and not described in the deed, I have plotted the California State Lands Commission CB Map Coordinates, from the actual field surveys of the Mean High Water Line and referenced them wherever used.
Where the MHTL was called for and not described in the deed and no California State Lands Coordinates were available, I digitized the maps entitled, “Map of the Offshore Ownership Boundary of the State of California Drawn pursuant to the Supplemental Decree of the U.S. Supreme Court in the U.S. V. California, 382 U.S. 448 (1966), Scale 1:10000 Sheets 1-161.” The shore line depicted on these maps is the Mean Lower Low Water (MLLW) Line as shown on the Hydrographic or Topographic Sheets for the coastline. If a better fit is needed, a field survey to position this line will need to be done.
The coordinates listed in Cris’ report were retrieved through Optical Character Recognition (OCR) and used to produce GIS polygons using Esri ArcGIS software. Coordinates were checked after the OCR process when producing the polygons in ArcMap to ensure accuracy. Original Coordinate systems (NAD 1927 California State Plane Zones 5 and 6) were used initially, with each zone being reprojected to NAD 83 Teale Albers Meters and merged after the review process.
While Cris’ expertise and documentation were relied upon to produce this GIS Layer, certain polygons were reviewed further for any potential updates since Cris’ document and for any unusual geometry. Boundary Determination Officers addressed these issues and plotted leases currently listed as active, but not originally in Cris’ report.
On December 24, 2014, the SLA boundary offshore of California was fixed (permanently immobilized) by a decree issued by the U.S. Supreme Court United States v. California, 135 S. Ct. 563 (2014). Offshore leases were clipped so as not to exceed the limits of this fixed boundary.
Lease Notes:
PRC 1482
The “lease area” for this lease is based on the Compensatory Royalty Agreement dated 1-21-1955 as found on the CSLC Insider. The document spells out the distinction between “leased lands” and “state lands”. The leased lands are between two private companies and the agreement only makes a claim to the State’s interest as those lands as identified and surveyed per the map Tract 893, Bk 27 Pg 24. The map shows the State’s interest as being confined to the meanders of three sloughs, one of which is severed from the bay (Anaheim) by a Tideland sale. It should be noted that the actual sovereign tide and or submerged lands for this area is all those historic tide and submerged lands minus and valid tide land sales patents. The three parcels identified were also compared to what the Orange County GIS land records system has for their parcels. Shapefiles were downloaded from that site as well as two centerline monuments for 2 roads covered by the Tract 893. It corresponded well, so their GIS linework was held and clipped or extended to make a parcel.
MJF Boundary Determination Officer 12/19/16
PRC 3455
The “lease area” for this lease is based on the Tract No. 2 Agreement, Long Beach Unit, Wilmington Oil Field, CA dated 4/01/1965 and found on the CSLC insider (also recorded March 12, 1965 in Book M 1799, Page 801).
Unit Operating Agreement, Long Beach Unit recorded March 12, 1965 in Book M 1799 page 599.
“City’s Portion of the Offshore Area” shall mean the undeveloped portion of the Long Beach tidelands as defined in Section 1(f) of Chapter 138, and includes Tract No. 1”
“State’s Portion of the Offshore Area” shall mean that portion of the Alamitos Beach Park Lands, as defined in Chapter 138, included within the Unit Area and includes Tract No. 2.”
“Alamitos Beach Park Lands” means those tidelands and submerged lands, whether filled or unfilled, described in that certain Judgment After Remittitur in The People of the State of California v. City of Long Beach, Case No. 683824 in the Superior Court of the State of California for the County of Los Angeles, dated May 8, 1962, and entered on May 15, 1962 in Judgment Book 4481, at Page 76, of the Official Records of the above entitled court”
*The description for Tract 2 has an EXCEPTING (statement) “therefrom that portion lying Southerly of the Southerly line of the Boundary of Subsidence Area, as shown on Long Beach Harbor Department {LBHD} Drawing No. D-98. This map could not be found in records nor via a PRA request to the LBHD directly. Some maps were located that show the extents of subsidence in this area being approximately 700 feet waterward of the MHTL as determined by SCC 683824. Although the “EXCEPTING” statement appears to exclude most of what would seem like the offshore area (out to 3 nautical miles from the MHTL which is different than the actual CA offshore boundary measured from MLLW) the 1964, ch 138 grant (pg25) seems to reference the lands lying seaward of that MHTL and ”westerly of the easterly boundary of the undeveloped portion of the Long Beach tidelands, the latter of which is the same boundary (NW) of tract 2. This appears to then indicate that the “EXCEPTING” area is not part of the Lands Granted to City of Long Beach and appears to indicate that this portion might be then the “State’s Portion of the Offshore Area” as referenced in the Grant and the Unit Operating Agreement. Section “f” in the CSLC insider document (pg 9) defines the Contract Lands: means Tract No. 2 as described in Exhibit “A” to the Unit Agreement, and as shown on Exhibit “B” to the Unit Agreement, together with all other lands within the State’s Portion of the Offshore Area.
Linework has been plotted in accordance with the methods used to produce this layer, with record lines rotated to those as listed in the descriptions. The main boundaries being the MHTL(north/northeast) that appears to be fixed for most of the area (projected to the city boundary on the east/southeast); 3 nautical miles from said MHTL on the south/southwest; and the prolongation of the NWly line of Block 50 of Alamitos Bay Tract.
MJF Boundary Determination Officer 12-27-16
PRC
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Dataset Card for Data Science Job Salaries
Dataset Summary
Content
Column Description
work_year The year the salary was paid.
experience_level The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director
employment_type The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/data-science-job-salaries.