A major goal of community ecology is understanding the processes responsible for generating biodiversity patterns along spatial and environmental gradients. In stream ecosystems, system specific conceptual frameworks have dominated research describing biodiversity change along longitudinal gradients of river networks. However, support for these conceptual frameworks has been mixed, mainly applicable to specific stream ecosystems and biomes, and these frameworks have placed less emphasis on general mechanisms driving biodiversity patterns. Rethinking biodiversity patterns and processes in stream ecosystems with a focus on the overarching mechanisms common across ecosystems will provide a more holistic understanding of why biodiversity patterns vary along river networks. In this study, we apply the Theory of Ecological Communities (TEC) conceptual framework to stream ecosystems to focus explicitly on the core ecological processes structuring communities: dispersal, speciation, niche selection, and ecological drift. Using a unique case study from high elevation networks of connected lakes and streams, we sampled stream invertebrate communities in the Sierra Nevada, CA to test established stream ecology frameworks and compared them to the TEC framework. Local diversity increased and β-diversity decreased moving downstream from the headwaters, consistent with the river continuum concept and the small but mighty framework of mountain stream biodiversity. Local diversity was also structured by distance below upstream lakes, where diversity increased with distance below upstream lakes, in support of the serial discontinuity concept. Despite some support for the biodiversity patterns predicted from the stream ecology frameworks, no single framework was fully supported, suggesting “context dependence”. By framing our results under the TEC, we found species diversity was structured by niche selection, where local diversity was highest in environmentally favorable sites. Local diversity was also highest in sites with small community sizes, countering predicted effects of ecological drift. Moreover, higher β-diversity in the headwaters was influenced by dispersal and niche selection, where environmentally harsh and spatially isolated sites exhibit higher community variation. Taken together our results suggest that combining system specific ecological frameworks with the TEC provides a powerful approach for inferring the mechanisms driving biodiversity patterns and provides a path toward generalization of biodiversity research across ecosystems. Methods Study Area The study area was located in the Sierra Nevada Mountains of eastern California (USA) and encompasses portions of Inyo National Forest and Sequoia-Kings Canyon National Park. Over the ice-free seasons (June-September), we sampled five distinct lake-stream networks, where each network was within a spatially distinct catchment and were treated as independent replicate systems (Fig. 3). The Kern (n=24) and Bubbs (n=26) networks were sampled in 2011, the Evolution (n=21) and Cascades (n=11) networks in 2018, and Rock Creek (n=36) in 2019. For each lake-stream network, streams were sampled throughout the network along a spatial gradient from headwaters downstream as well as along a spatial gradient downstream from lakes. Because the spatial distances of the river networks and the distance separating lakes naturally vary among networks as well as backcountry sampling constraints, the number of sites sampled along the distance from headwaters gradient varied (n=11 to n=36) and the downstream lake gradient varied (n=1 to n=9). This field system and the data collected naturally provide spatial gradients relevant to test stream ecology theories. In addition, this data is ideal for testing TEC processes because of the naturally varying gradients of community size, connectivity, and environmental heterogeneity present in our sampling design. Field Methods At each sampling location, we established transects in riffle sections of streams. At five equally spaced points along transects we measured stream depth and current velocity at mid-depth using a portable flow meter (Marsh-McBirney Flow Mate 2000). We then calculated stream discharge as the sum of the product of average depth x current velocity x width/5 over all transect points (Gordon et al. 2010; Herbst et al. 2018). A calibrated YSI multiparameter device was placed above transects to measure temperature, dissolved oxygen, conductivity, and pH. Benthic chlorophyll data was collected by scrubbing the entire surface area of three randomly selected cobble sized rocks (64-255 mm) of benthic algae (periphyton) with a toothbrush for 60 seconds (Herbst and Cooper 2010). Chlorophyll measurements were taken using a handheld fluorometer (Turner Designs Aquafluor), which measures raw fluorescence units. Florescent measurements were calibrated to chlorophyll concentration using a known concentration of Rhodamine. We standardized chlorophyll measurements by accounting for both the surface area of rocks and volume of water used to remove algae. Eight to twelve macroinvertebrate samples at each site were collected using a D-frame kick net (250 mm mesh, 30cm opening, 0.09m2 sample area) in riffle habitats, depending on the density of macroinvertebrate samples collected. We took samples by placing the net on the streambed, then turning and brushing all substrate by hand in the sampling area (30cm x 30cm) immediately above the net, with dislodged invertebrates being carried by currents into the net. All macroinvertebrate samples were preserved in 75% ethanol within 48 hours of sampling. Samples were sorted, identified, and counted in the laboratory. Taxa were identified to the finest taxonomic level possible, usually to genus or species for insects (excluding Chironomidae) and order or class for non-insects (Merritt, Cummins, and Berg 2019). The replicate samples taken at each site were pooled together and divided by the number of replicates and the area sampled to determine the density of invertebrate communities. Spatial Data Stream distance measurements were taken using the R package “riverdist”, which utilizes data from the USGS National Hydrological Dataset Flowline in order to determine pairwise distances from sampling sites along the river network (Tyers 2020). We determined distance below upstream lakes, with the closest upstream lake location being the outlet of the lake determined by the USGS Watershed Boundary Dataset. For sites where multiple upstream lakes were draining into streams, we defined the upstream lake as the closest upstream lake to sites that was also along the mainstem of the flowline. We determined distance from headwaters as the streamwise distance from sites to the uppermost portion (headwaters) of the mainstem of streams, where the headwaters of streams was determined by the endpoint (beginning) of the flowline in the USGS NHD Flowline Dataset (U.S. Geological Survey 2016). In cases where multiple headwater stream reaches corresponded to downstream sites, we defined the headwaters as the particular reach that accounted for the most discharge which was determined using USGS Flowline Dataset. Upstream lake area and perimeter measurements were determined using the USGS Watershed Boundary Dataset. Land-cover proportions were computed using the 2016 USGS National Land Cover Database (Jin et al. 2019).

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in State Center. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In State Center, the median household income for the households where the householder is White decreased by $4,103(5.36%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $76,603 in 2013 and $72,500 in 2023.
• Black or African American: As per the U.S. Census Bureau population data, in State Center, there are no households where the householder is Black or African American; hence, the median household income for the Black or African American population is not applicable.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in State Center.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are presented in 2023-inflation-adjusted dollars.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for State Center median household income by race. You can refer the same here

Context

Cultural diversity in the U.S. has led to great variations in names and naming traditions and names have been used to express creativity, personality, cultural identity, and values. Source: https://en.wikipedia.org/wiki/Naming_in_the_United_States

Content

This public dataset was created by the Social Security Administration and contains all names from Social Security card applications for births that occurred in the United States after 1879. Note that many people born before 1937 never applied for a Social Security card, so their names are not included in this data. For others who did apply, records may not show the place of birth, and again their names are not included in the data.

All data are from a 100% sample of records on Social Security card applications as of the end of February 2015. To safeguard privacy, the Social Security Administration restricts names to those with at least 5 occurrences.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:usa_names

https://cloud.google.com/bigquery/public-data/usa-names

Dataset Source: Data.gov. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source — http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @dcp from Unplash.

Inspiration

What are the most common names?

What are the most common female names?

Are there more female or male names?

Female names by a wide margin?

Background: Efforts to support disadvantaged communities have been prioritized through initiatives like Justice40, the Inflation Reduction Act (IRA), and the Bipartisan Infrastructure Law (BIL). Identifying disadvantaged communities involves several datasets with associated variables related to vulnerability indicators and scores. There are three key datasets:

1. Climate and Economic Justice Screening Tool (CEJST) from the White House Council on Environmental Quality (CEQ).
2. Energy Justice Mapping Tool from the Department of Energy (DOE) Office of Economic Impact & Diversity.
3. Environmental Justice Screening Tool from the Environmental Protection Agency (EPA) Office of Environmental Justice and External Civil Rights.

Problem:

1. Each dataset employs similar but distinct criteria and outcomes.
2. The unique IDs or GEOIDs differ across datasets. CEJST uses the 2010 census boundaries, while the Energy Justice Mapping Tool and the Environmental Justice Screening Tool are based on the 2019 and 2021 census boundaries, respectively.

To address these issues, this dataset consolidates information on disadvantaged communities and their associated variables by combining the three distinct datasets:

• CEJST: Provides binary data indicating whether a tract is a disadvantaged community. A community is classified as disadvantaged if it meets any of the following thresholds: 1) one or more indicators within categories such as climate change, energy, health, housing, pollution, transportation, and water & wastewater, coupled with low income; 2) one or more indicators in workforce development category and education; or 3) tribal lands. Environment and pollution indicators come from the EPA, while socio-demographic indicators are from the American Community Survey (ACS) for 2015-2019.

• Energy Justice Mapping Tool: Offers a DAC score, a continuous variable representing the sum of the 36 indicator percentiles. It includes environment, pollution, and socio-demographic indicators from the EPA and ACS (2015-2019).

• Environmental Justice Screening Tool: Includes the 13 Environmental Justice (EJ) Index and Supplemental Index. These continuous variables are weighted with socio-demographic indicators from ACS (2017-2021).

Data Descriptions

• Unit of Analysis: Census tract
• Geometry: 2021 census boundaries
• Columns:
  • Socio-demographic indicators
  • 13 Environmental Justice (EJ) index values
  • 13 Supplemental index values
  • Binary indicator for disadvantaged community classification
  • Disadvantaged Community (DAC) scores
• Datasets:
  • results/DAC.csv: Contains all columns from the three datasets.
  • results/DAC_s.csv: A shorter version, including socio-demographic indicators and EJ and Supplemental indices (Environmental Justice Screening Tool), disadvantaged community classification (CEJST), and DAC scores (Energy Justice Mapping Tool).
• Code:
  • syntax/code.R: This script illustrates the methodology for merging the three datasets, culminating in the creation of the two CSV files located in the results directory.

Purpose:

The dataset aims to help researchers identify overall disadvantaged communities or determine which specific communities are classified as disadvantaged. By consolidating these datasets, researchers can more effectively analyze and compare the various criteria used to define disadvantaged communities, enhancing the comprehensiveness of their studies.

Note

For complete data descriptions and sources, please refer to the original datasets.

• Climate and Economic Justice Screening Tool (CEJST)
• Energy Justice Mapping Tool
• Environmental Justice Screening Tool

NOTE: A more current version of the Protected Areas Database of the United States (PAD-US) is available: PAD-US 2.1 https://doi.org/10.5066/P92QM3NT. The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme (https://communities.geoplatform.gov/ngda-cadastre/). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all public and nonprofit lands and waters. Most are public lands owned in fee; however, long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas with over twenty-five attributes in nine feature classes to support data management, queries, web mapping services, and analyses. NOTE: A more current version of the Protected Areas Database of the United States (PAD-US) is available: PAD-US 2.1 https://doi.org/10.5066/P92QM3NT This PAD-US Version 2.0 dataset includes a variety of updates and changes from the previous Version 1.4 dataset. The following list summarizes major updates and changes: 1) Expanded database structure with new layers: the geodatabase feature class structure now includes nine feature classes separating fee owned lands, conservation (and other) easements, management designations overlapping fee lands, marine areas, proclamation boundaries and various 'Combined' feature classes (e.g. 'Fee' + 'Easement' + 'Designation' feature classes); 2) Major update of the Federal estate including data from 8 agencies, developed in collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/); 3) Major updates to 30 States and limited additions to 16 other States; 4) Integration of The Nature Conservancy's (TNC) Secured Lands geodatabase; 5) Integration of Ducks Unlimited's (DU) Conservation and Recreation Lands (CARL) database; 6) Integration of The Trust for Public Land's (TPL) Conservation Almanac database; 7) The Nature Conservancy (TNC) Lands database update: the national source of lands owned in fee or managed by TNC; 8) National Conservation Easement Database (NCED) update: complete update of non-sensitive (suitable for publication in the public domain) easements; 9) Complete National Marine Protected Areas (MPA) update: from the NOAA MPA Inventory, including conservation measure ('GAP Status Code', 'IUCN Category') review by NOAA; 10) First integration of Bureau of Energy Ocean Management (BOEM) managed marine lands: BOEM submitted Outer Continental Shelf Area lands managed for natural resources (minerals, oil and gas), a significant and new addition to PAD-US; 11) Fee boundary overlap assessment: topology overlaps in the PAD-US 2.0 'Fee' feature class have been identified and are available for user and data-steward reference (See Logical_Consistency_Report Section). For more information regarding the PAD-US dataset please visit, https://usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the “Data Manual for PAD-US” available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual .

The California Department of Transportation (Caltrans) and California Department of Fish and Game (CDFG) commissioned the California Essential Habitat Connectivity Project because a functional network of connected wildlands is essential to the continued support of California's diverse natural communities in the face of human development and climate change. The Essential Connectivity Map depicts large, relatively natural habitat blocks that support native biodiversity (Natural Landscape Blocks) and areas essential for ecological connectivity between them (Essential Connectivity Areas). This coarse-scale map was based primarily on the concept of ecological integrity, rather than the needs of particular species. Essential Connectivity Areas are placeholder polygons that can inform land-planning efforts, but that should eventually be replaced by more detailed Linkage Designs, developed at finer resolution based on the needs of particular species and ecological processes. It is important to recognize that even areas outside of Natural Landscape Blocks and Essential Connectivity Areas support important ecological values that should not be "written off" as lacking conservation value. Furthermore, because the Essential Habitat Connectivity Map was created at the statewide scale, based on available statewide data layers, and ignored Natural Landscape Blocks smaller than 2,000 acres squared, it has errors of omission that should be addressed at regional and local scales.

This layer package was loaded using Data Basin.Click here to go to the detail page for this layer package in Data Basin, where you can find out more information, such as full metadata, or use it to create a live web map.

The NYS Department of Environmental Conservation (DEC) collects and maintains several datasets on the locations, distribution and status of species of plants and animals. Information on distribution by county from the following three databases was extracted and compiled into this dataset. First, the New York Natural Heritage Program biodiversity database: Rare animals, rare plants, and significant natural communities. Significant natural communities are rare or high-quality wetlands, forests, grasslands, ponds, streams, and other types of habitats. Next, the 2nd NYS Breeding Bird Atlas Project database: Birds documented as breeding during the atlas project from 2000-2005. And last, DEC’s NYS Reptile and Amphibian Database: Reptiles and amphibians; most records are from the NYS Amphibian & Reptile Atlas Project (Herp Atlas) from 1990-1999.

The Natural Diversity Database Areas is a 1:24,000-scale, polygon feature-based layer that represents general locations of endangered, threatened and special concern species and significant natural communities. The layer is based on information collected by DEEP staff, cooperating scientists, conservation groups and landowners. In some cases an occurrence represents a location derived from literature, museum records and specimens. These data are compiled and maintained by the DEEP Bureau of Natural Resources, Natural Diversity Database Program. The layer is updated every six months and reflects information that has been submitted and accepted up to that point. The layer includes state and federally listed species and significant natural communities. It does not include Natural Area Preserves, designated wetland areas or wildlife concentration areas. These general locations were created by randomly shifting the true locations of terrestrial species and then adding a 0.25 mile buffer distance to each point, and by mapping linear segments with a 300 foot buffer associated with aquatic, riparian and coastal species. The exact location of the species or community falls somewhere within the polygon area and not necessarily in the center. Attribute information includes the date when these data were last updated. Species names are withheld to protect sensitive species from collection and disturbance. Data is compiled at 1:24,000 scale. These data are updated every six months, approximately in June and December. It is important to use the most current data available.

Despite nearly a century of study, the diversity of marine fungi remains poorly understood. Historical surveys utilizing microscopy or culture-dependent methods suggest that marine fungi are relatively species-poor, predominantly Dikarya, and localized to coastal habitats. However, the use of high-throughput sequencing technologies to characterize microbial communities has challenged traditional concepts of fungal diversity by revealing novel phylotypes from both terrestrial and aquatic habitats. Here, I used ion semiconductor sequencing (Ion Torrent) of the ribosomal large subunit (LSU/28S) to explore fungal diversity from water and sediment samples collected from four habitats in coastal North Carolina. The dominant taxa observed were Ascomycota and Chytridiomycota, though all fungal phyla were represented. Diversity was highest in sand flats and wetland sediments, though benthic sediments harbored the highest proportion of novel sequences. Most sequences assigned to early-diverging fungal groups could not be assigned beyond phylum with statistical support, suggesting they belong to unknown lineages.

California Medical Fee-for-Service

Geographic, Legal and Specialty Data for In-State and Out-of-State Providers

By California Health and Human Services [source]

About this dataset

Welcome to the California Health and Human Services Agency's Open Data Portal! Here, you can explore and utilize information from one of the state's most valuable assets: the non-confidential data set of Medi-Cal Fee-for-Service (FFS) program providers.

This dataset provides insight into Medi-Cal FFS enrollment. The information was retrieved from the Provider Master File (PMF), which is maintained by the Provider Enrollment Division (PED). With this dataset, you will gain insights into provider number, legal name, type description, specialty description and other geographical data points such as county code, attention line address parts , landmark coordinate points (longitude/latitude) and more!

The goal with this Open Data Portal initiative is to empower Californians with:

• Increased public access to high quality health & human service data;
• Stemmed creativity & innovation in research;
• The ability to make informed decisions about our health & services providers;
• Transparency in government policy expenditure measures.

Our hope is that you'll use these tools for responsible data analytics exploration on not just Medi-Cal FFS provision but on any related subject matter that interest& benefit your community at large. Good luck & happy researching!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Research Ideas

• Creating a mobile application or website to help people easily and quickly find their nearest Medi-Cal FFS providers based on location, specialty and provider type.
• Developing analytics tools to help organizations understand the concentrations of providers across the state in order to inform decision making when considering regional expansion and improving service accessibility.
• Developing a tool that visualizes specialty diversity across the state to identify areas with low provider density while helping inform strategies aimed at increasing access to care for communities with high needs populations

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: Profile_of_Enrolled_Medi-Cal_Fee-for-Service_FFS_Providers_as_of_May_1_2016.csv | Column name | Description | |:----------------------------|:---------------------------------------------------------------| | NPI | National Provider Identifier (Number) | | SERVICE LOCATION NUMBER | Unique identifier for the provider's service location (Number) | | LEGAL NAME | Legal name of the provider (Text) | | TYPE DESCRIPTION | Type of provider (Text) | | SPECIALTY DESCRIPTION | Specialty of the provider (Text) | | OUT OF STATE INDICATOR | Indicates if the provider is located out of state (Boolean) | | IN/OUT OF STATE | Indicates if the provider is located in or out of state (Text) | | COUNTY CODE | County code of the provider's service location (Number) | | COUNTY NAME | County name of the provider's service location (Text) | | ADDRESS ATTENTION | Attention line of the provider's address (Text) | | ADDRESS LINE 1 | First l...

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://communities.geoplatform.gov/ngda-cadastre/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1). Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/ ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the "State Name" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency. 5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field. State updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-history for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.

Parents, siblings, partners, and friends are often the first people to suspect a loved one is on the trajectory towards targeted violence or terrorism. These intimate bystanders are well positioned to facilitate prevention efforts if there are known and trusted reporting pathways to law enforcement or other resources. Little is known in the United States about the reporting processes for intimate bystanders to targeted violence or terrorism. This study is built on previous Australian and United Kingdom studies to understand the processes of intimate bystanders in the United States, in order to inform new, localized and contextually-sensitive understandings of and approaches to community reporting issues. Qualitative-quantitative interviews were conducted from March 2021 to July 2021 virtually over Zoom with 123 community members living in California and Illinois. The researchers describe their perspectives on barriers, facilitators, and pathways. The study sought to enhance prior studies with a larger and more demographically-diverse sample. It included a focus on ISIS/Al-Qa'eda-inspired foreign-terrorism, White Power movement-inspired domestic terrorism, and--of particular relevance to the US---non-ideologically motivated targeted, workplace violence.

The RRING Work Package 3 (WP3) objective was to clarify how Research Funding Organisations (RFOs) and Research Performing Organisations (RPOs) operated within region-specific research and innovation environments. It explored how they navigated the governance and regulatory frameworks for Responsible Research and Innovation (RRI), as well as offering their perspectives on the entities responsible for RRI-related policy and action in their locales.

This data set covers the global survey research part, which was designed to contextualise how RPOs and RFOs interacted within the research environment and with non-academic stakeholders. Countries were grouped according to the UNESCO regions of the world and key results per region are listed below. For a detailed analysis and further findings of the work completed under WP3 of the RRING project, please refer to the full deliverable document "State of the Art of RRI in the Five UNESCO World Regions" [link to be inserted].

European and North American States

‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of ensuring ethical principles were applied in R&I (92%), followed by diverse perspectives (88%), and gender equality (79%). Including ethnic minorities was the area which garnered the least attitudinal support (71%). Respondents took the most practical steps towards engaging with diverse perspectives (63%), and the least towards inclusion of ethnic minorities (24%).

‘Anticipative and reflective’: Respondents widely agreed (82%) with the importance of ensuring R&I work does not cause concerns for society, but only 37% confirmed they had taken practical steps to ensure this.

‘Open and transparent’: Vast majorities of respondents agreed on the importance of keeping R&I methods open and transparent (94%), with 65% also confirming they take practical steps to do this. An equally high number agreed on the importance of making the results of R&I work accessible to as wide a public as possible (94%), and 68% confirmed this through their reported actions. This indicated the smallest value-action gap of all RRI measures for respondents from European and North American countries. Attitudinal agreement on the importance of making data freely available to the public was lower (83%), as was the practical action aspect for this measure (45%).

‘Responsive and adaptive to change’: Most respondents agreed (89%) that it was important to ensure their work addresses societal needs, and 62% confirmed that they take practical steps towards this aim.

Latin American and Caribbean States

‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of gender equality in R&I (86%), followed by ensuring ethical principles are applied (85%), and diverse perspectives incorporated (83%). Including ethnic minorities was the area which garnered the least attitudinal support (77%). Respondents took the most practical steps towards ensuring ethical principles guide their work (50%), and the least towards including ethnic minorities (25%), but the smallest value action gap was found for gender equality.

‘Anticipative and reflective’: Respondents agreed (79%) that it is important to ensure R&I work does not cause concerns for society, but only 29% confirmed they had taken practical steps to ensure this.

‘Open and transparent’: The majority of respondents agreed on the importance of keeping R&I methods open and transparent (89%), with 45% indicating they had taken practical action. A majority also agreed on the importance of making the results of R&I work accessible to as wide a public as possible (88%), and 44% backed this up with practical action. Attitudinal agreement on the importance of making data freely available to the public was slightly lower (81%), as was the practical action aspect for this measure (35%).

‘Responsive and adaptive to change’: Most respondents agreed (84%) that it was important to ensure their work addresses societal needs, and 49% confirmed that they take practical steps towards this aim.

Asian and Pacific States

‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of ensuring ethical principles were applied in R&I (90%), followed by diverse perspectives (89%), and gender equality (86%). Including ethnic minorities was the area which garnered the least attitudinal support (76%). Respondents took the most practical steps towards engaging with diverse perspectives (65%), and the least towards including ethnic minorities (30%).

‘Anticipative and reflective’: Respondents widely agreed (78%) with the importance of ensuring R&I work does not cause concerns for society, and 42% confirmed they had taken practical steps to ensure this.

‘Open and transparent’: The majority of respondents agreed on the importance of keeping R&I methods open and transparent (91%), with 58% indicating they take practical steps to do this. A majority also agreed on the importance of making the results of R&I work accessible to as wide a public as possible (89%), and 64% backed this up with practical action. Attitudinal agreement on the importance of making data freely available to the public was lower (79%), as was the practical action aspect for this measure (40%).

‘Responsive and adaptive to change’: Most respondents agreed (92%) that it was important to ensure their work addresses societal needs, and 69% confirmed that they take practical steps towards this aim. This was the RRI measure with the smallest valueaction gap for respondents from the Asian and Pacific region.

Arab States

‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of ensuring ethical principles were applied in R&I (93%), followed by diverse perspectives (81%), and gender equality (85%). Including ethnic minorities was the area which garnered the least attitudinal support (74%). Respondents took the most practical steps towards engaging with diverse perspectives (66%), which equated to one of two equally small value-action gaps for respondents from Arab states, and the least practical steps towards inclusion of ethnic minorities (22%).

‘Anticipative and reflective’: A high proportion of respondents (85%) agreed that it is important to ensure R&I work does not cause concerns for society. However, only 38% confirmed they had taken practical steps to ensure this.

‘Open and transparent’: The majority of respondents agreed on the importance of keeping R&I methods open and transparent (89%), with 59% also confirming they take practical steps to do this. A majority also agreed on the importance of making the results of R&I work accessible to as wide a public as possible (90%), and 66% backed this up with practical action. Ensuring public accessibility of research results was the second of two measures with equally small value-action gaps. Attitudinal agreement on the importance of making data freely available to the public was much lower (78%), which also reflected the practical action aspect for this measure (49%).

‘Responsive and adaptive to change’: Most respondents agreed (96%) that it was important to ensure their work addresses societal needs, and 68% confirmed that they take practical steps to achieve this.

African States

‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of ensuring engagement with diverse perspectives and expertise in R&I (91%), followed by ensuring ethical principles are applied (90%), and gender equality (89%). Including ethnic minorities was the area which garnered the least attitudinal support (74%). Respondents took the most practical steps towards ensuring ethical principles guide their work (57%), and the least towards including ethnic minorities (32%).

‘Anticipative and reflective’: The majority of respondents (85%) agreed that it is important to ensure R&I work does not cause concerns for society, with 59% confirming that they take practical steps to ensure this.

‘Open and transparent’: A high proportion of respondents agreed on the importance of keeping R&I methods open and transparent (90%), with 54% also confirming they take practical steps to do this. A majority also agreed on the importance of making the results of R&I work accessible to as wide a public as possible (86%), and 56% backed this up with practical action. Attitudinal agreement on the importance of making data freely available to the public was significantly lower (73%), as was the practical action aspect for this measure (38%).

‘Responsive and adaptive to change’: Respondents mostly agreed (92%) that it was important to ensure their work addresses societal needs, and 64% confirmed that they take practical steps towards this aim. This was the RRI measure with the smallest valueaction gap for respondents from African states.

Note: Please refer to the "RRING WP3 - Survey Data Documentation" document for detailed instructions on how to use this dataset.

Understanding the structure and function of infaunal communities is useful in determining the biodiversity and ecosystem function of shallow estuaries. We conducted a survey of infaunal communities within three separate water basins [Mosquito Lagoon (ML), Indian River (IR), and Banana River (BR)] in the larger Northern Indian River Lagoon, FL, United States to establish a database of infaunal community structure and function. Twenty-seven sites were sampled quarterly from 2014 to 2016. Analysis of all samples determined that basin, season, and sediment composition were the primary drivers of macrobenthic community composition. Diversity was highest in the ML, and lower in spring compared to other seasons. The occurrence of a brown tide (Aureoumbra lagunensis) in 2016 allowed a comparison of winter and spring communities before (2015) and during (2016) a bloom event. Community composition and diversity at the BR sites were the most affected by the bloom event with the lowest diversity and abundances during the bloom. Diversity in the IR was also lower during the bloom, while the ML was unaffected by the bloom. Species of all feeding groups were affected by the bloom, with lower abundances found in all groups. In addition, to determine the overall trophic diversity of infaunal communities, we collected infaunal organisms from two of the quarterly sampled sites for isotope analyses. Values of δ13C and δ15N from infaunal tissue were compared to those of potential food sources at each site. Substantial interspecific variation in isotope values of infaunal organisms within a site suggests the presence of diverse nutritional modes that include suspension and deposit feeding and predation. Together, these data suggest that infaunal communities contribute to benthic pelagic coupling and nutrient cycling within the estuarine communities, but the overall function of these communities may be tightly linked to their species composition.

We observe that satellite imagery is a powerful source of information as it contains more structured and uniform data, compared to traditional images. Although computer vision community has been accomplishing hard tasks on everyday image datasets using deep learning, satellite images are only recently gaining attention for maps and population analysis. This workshop aims at bringing together a diverse set of researchers to advance the state-of-the-art in satellite image analysis.

To direct more attention to such approaches, we propose DeepGlobe Satellite Image Understanding Challenge, structured around three different satellite image understanding tasks. The datasets created and released for this competition may serve as reference benchmarks for future research in satellite image analysis. Furthermore, since the challenge tasks will involve "in the wild" forms of classic computer vision problems, these datasets have the potential to become valuable testbeds for the design of robust vision algorithms, beyond the area of remote sensing.

Facebook is becoming an essential tool for more than just family and friends. Discover how Cheltenham Township (USA), a diverse community just outside of Philadelphia, deals with major issues such as the Bill Cosby trial, everyday traffic issues, sewer I/I problems and lost cats and dogs. And yes, theft.

Communities work when they're connected and exchanging information. What and who are the essential forces making a positive impact, and when and how do conversational threads get directed or misdirected?

Use Any Facebook Public Group

You can leverage the examples here for any public Facebook group. For an example of the source code used to collect this data, and a quick start docker image, take a look at the following project: facebook-group-scrape.

Data Sources

There are 4 csv files in the dataset, with data from the following 5 public Facebook groups:

• Unofficial Cheltenham Township
• Elkins Park Happenings!
• Free Speech Zone
• Cheltenham Lateral Solutions
• Cheltenham Township Residents

post.csv

These are the main posts you will see on the page. It might help to take a quick look at the page. Commas in the msg field have been replaced with {COMMA}, and apostrophes have been replaced with {APOST}.

• gid Group id (5 different Facebook groups)
• pid Main Post id
• id Id of the user posting
• name User's name
• timeStamp
• shares
• url
• msg Text of the message posted.
• likes Number of likes

comment.csv

These are comments to the main post. Note, Facebook postings have comments, and comments on comments.

• gid Group id
• pid Matches Main Post identifier in post.csv
• cid Comment Id.
• timeStamp
• id Id of user commenting
• name Name of user commenting
• rid Id of user responding to first comment
• msg Message

like.csv

These are likes and responses. The two keys in this file (pid,cid) will join to post and comment respectively.

• gid Group id
• pid Matches Main Post identifier in post.csv
• cid Matches Comments id.
• response Response such as LIKE, ANGRY etc.
• id The id of user responding
• name Name of the user responding

member.csv

These are all the members in the group. Some members never, or rarely, post or comment. You may find multiple entries in this table for the same person. The name of the individual never changes, but they change their profile picture. Each profile picture change is captured in this table. Facebook gives users a new id in this table when they change their profile picture.

• gid Group id
• id Id of the member
• name Name of the member
• url URL of the member

Understanding the relationship between biodiversity and ecological stability is increasingly urgent as rapid species extinction continues. Though evidence of positive diversity-stability relationships is accumulating, empirical results are inconsistent and effect sizes tend to be small, raising questions about relative contributions of intrinsic (i.e., species composition/interactions) and extrinsic (i.e., environmental) drivers of stability. Community stability may be more strongly influenced by environmental conditions than by community diversity in some contexts, yet little is known about the comparative importance of diversity and climate means, extremes, and variability in regulating stability. We used a half-century of continental-scale bird data to quantify avian community temporal variability (a metric often used to approximate ecological stability) at 1,379 sites and compared relative effects of climatic variables and species richness. We found that extreme heat and extremely l..., , , # Code from: The relative influence of climate extremes and species richness on the temporal variability of bird communities

Date: Mar 30, 2023

Lead author contact: Samantha M. Cady, University of Nebraska-Lincoln (samantha.cady@unl.edu)

Additional authors/cooperators: Samuel D. Fuhlendorf (Oklahoma State University), Craig A. Davis (Oklahoma State University), Barney Luttbeg (Oklahoma State University), Caleb P. Roberts (University of Arkansas), and Scott R. Loss (Oklahoma State University)

DATA

User must download 5 open-source datasets and 1 dataset from Cady et al. (2023) on Dryad before running the accompanying R Code.

Open Source Data:

Datasets 1-3: Breeding Bird Survey 2019 Release, state-level data 1966-2018, weather.csv, and coords.csv. All three datasets are available online:

Pardieck, K.L., Ziolkowski, D.J., Lutmerding, M., Aponte, V.I. & Hudson, M.-A.R. (2019). North American Breeding Bird Survey Dataset 1966-2018 (ver. 2018.0). ...

The Washington Natural Heritage Program maintains a database of rare and imperiled species and plant communities for the state. The Element Occurrence (EO) records that form the core of the Natural Heritage database include information on the location, status, characteristics, numbers, condition, and distribution of elements of biological diversity using established Natural Heritage Methodology developed by NatureServe and The Nature Conservancy (TNC). An Element Occurrence (EO) is an area of land and/or water in which a species or natural community is, or was, present. An EO should have practical conservation value for the Element as evidenced by potential continued (or historical) presence and/or regular recurrence at a given location. For species Elements, the EO often corresponds with the local population, but when appropriate may be a portion of a population or a group of nearby populations (e.g., metapopulation). For community Elements, the EO may represent a stand or patch of a natural community, or a cluster of stands or patches of a natural community. Because they are defined on the basis of biological information, EOs may cross jurisdictional boundaries. An Element Occurrence record is a data management tool that has both spatial and tabular components including a mappable feature and its supporting database. EOs are typically represented by bounded, mapped areas of land and/or water or, at small scales, the centroid point of this area. EO records are most commonly created for current or historically known occurrences of natural communities or native species of conservation interest. They may also be created, in some cases, for extirpated occurrences.

This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. This data comes from the US Census Bureau's 2015 American Community Survey. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.