This dataset has results and the model associated with the publication Ciulla et al., (2024). It contains a U-Net semantic segmentation model (unet_model.h5) and associated code implemented in tensorflow 2.0 for the model training and identification of oil and gas well symbols in USGS historical topographic maps (HTMC). Given a quadrangle map (7.5 minutes), downloadable at this url: https://ngmdb.usgs.gov/topoview/, and a list of coordinates of the documented wells present in the area, the model returns the coordinates of oil and gas symbols in the HTMC maps. For reproducibility of our workflow, we provide a sample map in California and the documented well locations for the entire State of California (CalGEM_AllWells_20231128.csv) downloaded from https://www.conservation.ca.gov/calgem/maps/Pages/GISMapping2.aspx. Additionally, the locations of 1,301 potential undocumented orphaned wells identified using our deep learning framework or the counties of Los Angeles and Kern in California, and Osage and Oklahoma in Oklahoma are provided in the file found_potential_UOWs.zip. The results of the visual inspection of satellite imagery in Osage County is in the file visible_potential_UOWs.zip. The dataset also includes a custom tool to validate the detected symbols in the HTMC maps (vetting_tool.py). More details about the methodology can be found in the associated paper: Ciulla, F., Santos, A., Jordan, P., Kneafsey, T., Biraud, S.C., and Varadharajan, C. (2024) A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: a Case Study for California and Oklahoma. Accepted for publication in Environmental Science and Technology. The geographical coordinates provided correspond to the locations of potential undocumented orphaned oil and gas wells (UOWs) extracted from historical maps. The actual presence of wells need to be confirmed with on-the-ground investigations. For your safety, do not attempt to visit or investigate these sites without appropriate safety training, proper equipment, and authorization from local authorities. Approaching these well sites without proper personal protective equipment (PPE) may pose significant health and safety risks. Oil and gas wells can emit hazardous gasses including methane, which is flammable, odorless and colorless, as well as hydrogen sulfide, which can be fatal even at low concentrations. Additionally, there may be unstable ground near the wellhead that may collapse around the wellbore. This dataset was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or the Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California.

ABSTRACT

    Statements about building walls, deportation and denying services to undocumented immigrants made by the US president may induce fear in Latino populations and create barriers to their health care access. To assess the impact of these statements on undocumented Latino immigrants’ (UDLI) and Latino legal residents/citizens’ (LLRC) perceptions of safety and their presentations for emergency care, we conducted surveys of adult patients at three county emergency departments (EDs) in California from June 2017 to December 2018. Of 1,684 patients approached, 1,337 (79.4%) agreed to participate: 34.3% UDLI, 36.9% LLRC, and 29.8% non-Latino legal residents/citizens (NLRC). The vast majority of UDLI (95%), LLRC (94%), and NLRC (85%) had heard statements about immigrants by President Trump. Most UDLI (89%), LLRC (88%), and NLRC (87%) either thought that these measures were being enacted now or will be enacted in the futur...

This study was conducted to examine whether a rising crime rate in El Paso, Texas and San Diego, California in 1986 could be attributed to, among other factors, the influx of undocumented aliens. Variables include level of involvement of undocumented aliens in serious felony arrests in San Diego and El Paso Counties, the outcome of serious felony arrest cases involving undocumented persons compared to others arrested for similar offenses, the impact of arrests of undocumented aliens on the criminal justice system in terms of workload and cost, the extent that criminal justice agencies coordinate their efforts to apprehend and process undocumented aliens who have committed serious crimes in San Diego and El Paso counties, and how differences in agency objectives impede or enhance coordination. Data are also provided on how many undocumented persons were arrested/convicted for repeat offense in these counties and which type of policies or procedures could be implemented in criminal justice agencies to address the issue of crimes committed by undocumented aliens. Data were collected in the two cities with focus on serious felony offenses. The collection includes sociodemographic characteristics, citizenship status, current arrest, case disposition, and prior criminal history with additional data from San Diego to compute the costs involving undocumented aliens.

Acknowledgement

The following table is imported from the 2019 Yearbook of Immigration Statistics under the Department of Homeland Security:

The 2019 Yearbook of Immigration Statistics is a compendium of tables that provide data on foreign nationals who are granted lawful permanent residence (i.e., immigrants who receive a “green card”), admitted as temporary nonimmigrants, granted asylum or refugee status, or are naturalized. The Yearbook also presents data on immigration enforcement actions, including apprehensions and arrests, removals, and returns.

Table 39. Aliens Removed or Returned: Fiscal Years 1892 to 2019 (https://www.dhs.gov/immigration-statistics/yearbook/2019/table39)

Inspiration

The data was collected to observe trends in history reflecting the number of immigrants deported - more specifically removed or returned.

This dataset includes the monthly count of individuals under age 19 receiving full scope Medi-Cal benefits as authorized by California Welfare and Institutions Code 14007.8. The count reflects the total number eligible during the month. California provides full scope Medi-Cal benefits to all children under the age of 19 regardless of immigration status. This program is referred to as SB 75 and was implemented May 16, 2016 pursuant to SB 75 (Chapter 18, Statutes of 2015), Section 14007.8 and added to the Welfare and Institutions Code and amended by SB 4 (Chapter 709, Statutes of 2015).

Abstract (en): The American College Catalog Study Database (CCS) contains academic data on 286 four-year colleges and universities in the United States. CCS is one of two databases produced by the Colleges and Universities 2000 project based at the University of California-Riverside. The CCS database comprises a sampled subset of institutions from the related Institutional Data Archive (IDA) on American Higher Education (ICPSR 34874). Coding for CCS was based on college catalogs obtained from College Source, Inc. The data are organized in a panel design, with measurements taken at five-year intervals: academic years 1975-76, 1980-81, 1985-86, 1990-91, 1995-96, 2000-01, 2005-06, and 2010-11. The database is based on information reported in each institution's college catalog, and includes data regarding changes in major academic units (schools and colleges), departments, interdisciplinary programs, and general education requirements. For schools and departments, changes in structure were coded, including new units, name changes, splits in units, units moved to new schools, reconstituted units, consolidated units, departments reduced to program status, and eliminated units. The American College Catalog Study Database (CCS) is intended to allow researchers to examine changes in the structure of institutionalized knowledge in four-year colleges and universities within the United States. For information on the study design, including detailed coding conventions, please see the Original P.I. Documentation section of the ICPSR Codebook. The data are not weighted. Dataset 1, Characteristics Variables, contains three weight variables (IDAWT, CCSWT, and CASEWEIGHT) which users may wish to apply during analysis. For additional information on weights, please see the Original P.I. Documentation section of the ICPSR Codebook. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Response Rates: Approximately 75 percent of IDA institutions are included in CCS. For additional information on response rates, please see the Original P.I. Documentation section of the ICPSR Codebook. Four-year not-for-profit colleges and universities in the United States. Smallest Geographic Unit: state CCS includes 286 institutions drawn from the IDA sample of 384 United States four-year colleges and universities. CCS contains every IDA institution for which a full set of catalogs could be located at the initiation of the project in 2000. CCS contains seven datasets that can be linked through an institutional identification number variable (PROJ_ID). Since the data are organized in a panel format, it is also necessary to use a second variable (YEAR) to link datasets. For a brief description of each CCS dataset, please see Appendix B within the Original P.I. Documentation section of the ICPSR Codebook.There are date discrepancies between the data and the Original P.I. Documentation. Study Time Periods and Collection Dates reflect dates that are present in the data. No additional information was provided.Please note that the related data collection featuring the Institutional Data Archive on American Higher Education, 1970-2011, will be available as ICPSR 34874. Additional information on the American College Catalog Study Database (CCS) and the Institutional Data Archive (IDA) database can be found on the Colleges and Universities 2000 Web site.

Pan evaporation is a measure of atmospheric evaporative demand (E0) for which long term and spatially distributed observations are available from the NOAA Cooperative Observer (COOP) Network. However, this data requires extensive quality control and homogenization due to documented and undocumented station moves and other factors including human errors in recording or digitization. Station-based Pan Evaporation measurements (in mm) from 247 stations across the continental United States were compiled and quality controlled for the analysis shown in Dewes et al., 2017. This dataset reports warm season (May-October; for 21 stations the data is only available for May-September) pan evaporation with at least 20 years of data between 1950 and 2001. Both monthly values and long-term monthly averages are made available, including the climatological measure for standard deviation and coefficient of variation. Dewes et al. (2017) used this dataset to evaluate the ability of different E0 formulations â Hargreaves-Samani, Priestly-Taylor, and Penman-Monteith â to reproduce the spatial patterns of observed warm-season E0 and its interannual variability. This data is an extension of the dataset described in Hobbins (2004) and Hobbins et al. (2004) with 21 additional stations north of 41oN latitude. The extension was needed in order to include data in the North Central Climate Science Center region. For these added stations, the procedure described in Hobbins (2004) for quality control was applied, including an adjustment in the mean when documented station moves occurred, and the removal of obvious outliers. The quality control procedure for the extended dataset did not automate tests for undocumented inhomogeneities for these stations. For all stations, a visual inspection of the timeseries was used to add additional breakpoints in the data for homogenization (only two were added in the extended set), and to eliminate two stations from consideration.

Abstract (en): This administrative dataset provides descriptive information about the families and children served through the federal Child Care and Development Fund (CCDF). CCDF dollars are provided to states, territories, and tribes to provide assistance to low-income families receiving or in transition from temporary public assistance, to obtain quality child care so they can work, or depending on their state's policy, to attend training or receive education. The Personal Responsibility and Work Opportunity Act of 1996 requires states and territories to collect information on all family units receiving assistance through the CCDF and to submit monthly case-level data to the Office of Child Care. States are permitted to report case-level data for the entire population, or a sample of the population, under approved sampling guidelines. The Summary Records file contains monthly state-level summary information including the number of families served. The Family Records file contains family-level data including single parent status of the head of household, monthly co-payment amount, date on which child care assistance began, reasons for care (e.g., employment, training/education, protective services, etc.), income used to determine eligibility, source of income, and the family size on which eligibility is based. The Child Records file contains child-level data including ethnicity, race, gender, and date of birth. The Setting Records file contains information about the type of child care setting, the total amount paid to the provider, and the total number of hours of care received by the child. The Pooling Factor file provides state-level data on the percentage of child care funds that is provided through the CCDF, the federal Head Start region the grantee (state) is in and is monitored by, and the state FIPS code for the grantee. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Datasets:DS0: Study-Level FilesDS1: Summary RecordsDS2: Family RecordsDS3: Child RecordsDS4: Setting RecordsDS5: Pooling FactorDS6: Adjusted Child Records File (Online Analysis Only)DS7: Unadjusted Child Records File (Online Analysis Only)DS8: Adjusted Family Records File (Online Analysis Only)DS9: Unadjusted Family Records File (Online Analysis Only) Children and families receiving assistance through the Child Care and Development Fund (CCDF), through their state, territory, or tribe. This sample dataset consists of monthly data provided by states that reported sample data and states that reported full population data, as well as any territory data received. Sampling of the data from states reporting full population data was done in accordance with Technical Bulletin #5, Appendix II: Annual Sampling Plan, Example A The month with the lowest caseload was selected for determining the sampling rate so that at least 200 samples were selected for each month. Additional information on the development of this sample dataset is provided in the accompanying technical documentation.

The ckanext-montrosemaps extension for CKAN appears to provide mapping capabilities. Based on the minimal documentation, it seems intended to enhance CKAN datasets with geographical visualization features. While specific functionality is undocumented in this readme, the name suggests an integration with mapping libraries to display datasets on a map. Key Features (Inferred): Geospatial Data Visualization: Likely provides the ability to display datasets containing geographical data on a map. Mapping Integration: Integrates with a mapping library (unspecified) to render map views. Potential Customization: May offer some level of customization for map display, such as marker styles or data overlays. Technical Integration: The installation instructions indicate that this extension operates as a CKAN plugin. To enable it, the plugin name, montrosemaps, must be added to the ckan.plugins setting within the CKAN configuration file. A CKAN restart is then required to activate the extension. Benefits & Impact (Inferred): By adding mapping capabilities, this extension could allow users to visualize and explore data geographically, enabling easier discovery and understanding of location-based datasets. It could enhance CKAN's usefulness for geographical data management and analysis. Due to limited documentation, the full extent of benefits is unknown.

DNA barcoding is particularly useful for identification and species delimitation in taxa with conserved morphology. Pseudoscorpions are arachnids with high prevalence of morphological crypsis. Here, we present the first comprehensive DNA barcode library for Central European Pseudoscorpiones, covering 70% of the German pseudoscorpion fauna (35 out of 50 species). For 21 species, we provide the first publicly available COI barcodes, including the rare Anthrenochernes stellae Lohmander, a species protected by the FFH Habitats Directive. The pattern of intraspecific COI variation and interspecific COI variation (i.e., presence of a barcode gap) generally allows application of the DNA barcoding approach, but revision of current taxonomic designations is indicated in several taxa. Sequences of 36 morphospecies were assigned to 74 BINs (barcode index numbers). This unusually high number of intraspecific BINs can be explained by the presence of overlooked cryptic species and by the accelerated substitution rate in the mitochondrial genome of pseudoscorpions, as known from previous studies. Therefore, BINs may not be an appropriate proxy for species numbers in pseudoscorpions, while partitions built with the ASAP algorithm (Assemble Species by Automatic Partitioning) correspond well with putative species. ASAP delineated 51 taxonomic units from our data, an increase of 42% compared with the present taxonomy. The Neobisium carcionoides complex, currently considered a polymorphic species, represents an outstanding example of cryptic diversity: 154 sequences from our dataset were allocated to 23 BINs and 12 ASAP units.

Key results across measurement type and political parties.