Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

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.

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.

OBIS-USA provides aggregated, interoperable biogeographic data collected primarily from U.S. waters and oceanic regions--the Arctic, the Atlantic and Pacific oceans, the Caribbean Sea, Gulf of Mexico and the Great Lakes. It provides access to datasets from state and federal agencies as well as educational and research institutions. OBIS-USA handles both specimen-based data and survey results. Survey data come from recovered archives and current research activities. The datasets document where and when species were observed or collected, bringing together marine biogeographic data that are spatially, taxonomically, and temporally comprehensive. The public OBIS-USA site (http://www.usgs.gov/obis-usa) provides actual data contents as well as summary data about what is contained in each dataset to assist users in evaluating suitability for use. Current functionality allows the user to locate, view, and aggregate the datasets and FGDC compliant metadata as well as to view and search the taxonomic, geographic, and temporal extent. To promote data interoperability, the data are available in accordance with the marine-focused implementation of the Darwin Core data standard. In addition to basic download functions (tab-delimited), OBIS-USA offers web services for query flexibility and a wide range of output formats, such as kml, NetCDF, MATLAB, json, and graph or map output, to enable diverse types of scientific and geospatial data use and analysis platforms and products. OBIS-USA's two web services (ERDDAP and GeoServer) enable integration of OBIS-USA biogeographic data with other data types, such as seafloor geology, physical oceanography, water chemistry, and climate data. The NOAA Environmental Research Division Data Access Program(ERRDDAP) enables users to query scientific data by flexible parameters and obtain output in many formats. Access can be found at http://www1.usgs.gov/erddap/tabledap/AllMBG.html . OBIS-USA uses the tabledap component of ERDDAP to access Darwin-Core-type tabular spatial data; tabledap is a superset of the OPeNDAP DAP constraint protocol. OBIS-USA offers an ESRI REST Service with access to Darwin-Core-type point data at http://gis1.usgs.gov/arcgis/rest/services/OBISUSA/OBIS_USA_All_Marine_Biogeographic_Records/MapServer/ and an OGC compliant Web Mapping Service (wms) http://gis1.usgs.gov/arcgis/services/OBISUSA/OBIS_USA_All_Marine_Biogeographic_Records/MapServer/WMSServer?request=GetCapabilities&service=WMS. OBIS-USA and collaborators are further deploying the Darwin Core standard to capture richer information, such as absence and abundance, observations on effort, individual tracking, and more advanced biogeography capabilities. Data are accepted into OBIS-USA from the data originator or holder, minimizing the burden on the participant. OBIS-USA works with data providers to understand the best process to transfer the data, review the data prior to their release, gather comprehensive metadata, and then allow public access to this information. Becoming part of the OBIS-USA network is intended to have tangible benefits for participants, for example, freeing the participant from responding to requests for data and alleviating security concerns since users do not directly access the participant's computers.

Key Table Information.Table Title.Subcounty General-Purpose Governments by Population-Size Group: U.S. and State: 2012 - 2022.Table ID.GOVSTIMESERIES.CG00ORG06.Survey/Program.Public Sector.Year.2024.Dataset.PUB Public Sector Annual Surveys and Census of Governments.Source.U.S. Census Bureau, Public Sector.Release Date.2023-08-24.Release Schedule.For information about Census of Governments planned data product releases, see https://www.census.gov/programs-surveys/gus/newsroom/updates.html.Dataset Universe.Census of Governments - Organization (CG):The universe of this file is all federal, state, and local government units in the United States. In addition to the federal government and the 50 state governments, the Census Bureau recognizes five basic types of local governments. The government types are: County, Municipal, Township, Special District, and School District. Of these five types, three are categorized as General Purpose governments: County, municipal, and township governments are readily recognized and generally present no serious problem of classification. However, legislative provisions for school district and special district governments are diverse. These two types are categorized as Special Purpose governments. Numerous single-function and multiple-function districts, authorities, commissions, boards, and other entities, which have varying degrees of autonomy, exist in the United States. The basic pattern of these entities varies widely from state to state. Moreover, various classes of local governments within a particular state also differ in their characteristics. Refer to the Individual State Descriptions report for an overview of all government entities authorized by state.The Public Use File provides a listing of all independent government units, and dependent school districts active as of fiscal year ending June 30, 2024. The Annual Surveys of Public Employment & Payroll (EP) and State and Local Government Finances (LF):The target population consists of all 50 state governments, the District of Columbia, and a sample of local governmental units (counties, cities, townships, special districts, school districts). In years ending in '2' and '7' the entire universe is canvassed. In intervening years, a sample of the target population is surveyed. Additional details on sampling are available in the survey methodology descriptions for those years.The Annual Survey of Public Pensions (PP):The target population consists of state- and locally-administered defined benefit funds and systems of all 50 state governments, the District of Columbia, and a sample of local governmental units (counties, cities, townships, special districts, school districts). In years ending in '2' and '7' the entire universe is canvassed. In intervening years, a sample of the target population is surveyed. Additional details on sampling are available in the survey methodology descriptions for those years.The Annual Surveys of State Government Finance (SG) and State Government Tax Collections (TC):The target population consists of all 50 state governments. No local governments are included. For the purpose of Census Bureau statistics, the term "state government" refers not only to the executive, legislative, and judicial branches of a given state, but it also includes agencies, institutions, commissions, and public authorities that operate separately or somewhat autonomously from the central state government but where the state government maintains administrative or fiscal control over their activities as defined by the Census Bureau. Additional details are available in the survey methodology description.The Annual Survey of School System Finances (SS):The Annual Survey of School System Finances targets all public school systems providing elementary and/or secondary education in all 50 states and the District of Columbia..Methodology.Data Items and Other Identifying Records.Population-size group - Municipal Governments - Less than 1,000 - CountPopulation-size group - Municipal Governments - 1,000 to 2,499 - CountPopulation-size group - Municipal Governments - 2,500 to 4,999 - CountPopulation-size group - Municipal Governments - 5,000 to 9,999 - CountPopulation-size group - Municipal Governments - 10,000 to 24,999 - CountPopulation-size group - Municipal Governments - 25,000 to 49,999 - CountPopulation-size group - Municipal Governments - 50,000 to 99,999 - CountPopulation-size group - Municipal Governments - 100,000 to 199,999 - CountPopulation-size group - Municipal Governments - 200,000 to 299,999 - CountPopulation-size group - Municipal Governments - 300,000 or more - CountPopulation-size group - Township Governments - Less than 1,000 - CountPopulation-size group - Township Governments - 1,000 to 2,499 - CountPopulation-size group - Township Governments - 2,500 to 4,999 - CountPopulation-size group - Township Governments - 5,000 to 9,999 - CountPopulation-size group - Township Governments - 10,000 to 24,999 - CountPo...

The State of Alabama contains the most diverse fish fauna of North America. The University of Alabama Ichthyological Collection (UAIC) documents this diversity and is one of the largest educational and research collections of fishes in the southeastern United States. This nationally and internationally recognized biological resource includes over one million preserved, skeletal, and frozen specimens, some dating back to the mid 1900's, and is the best single resource documenting past and present distributions and abundances of fishes in the State.

Introduction

There are several works based on Natural Language Processing on newspaper reports. Mining opinions from headlines [ 1 ] using Standford NLP and SVM by Rameshbhaiet. Al.compared several algorithms on a small and large dataset. Rubinet. al., in their paper [ 2 ], created a mechanism to differentiate fake news from real ones by building a set of characteristics of news according to their types. The purpose was to contribute to the low resource data available for training machine learning algorithms. Doumitet. al.in [ 3 ] have implemented LDA, a topic modeling approach to study bias present in online news media.

However, there are not many NLP research invested in studying COVID-19. Most applications include classification of chest X-rays and CT-scans to detect presence of pneumonia in lungs [ 4 ], a consequence of the virus. Other research areas include studying the genome sequence of the virus[ 5 ][ 6 ][ 7 ] and replicating its structure to fight and find a vaccine. This research is crucial in battling the pandemic. The few NLP based research publications are sentiment classification of online tweets by Samuel et el [ 8 ] to understand fear persisting in people due to the virus. Similar work has been done using the LSTM network to classify sentiments from online discussion forums by Jelodaret. al.[ 9 ]. NKK dataset is the first study on a comparatively larger dataset of a newspaper report on COVID-19, which contributed to the virus’s awareness to the best of our knowledge.

2 Data-set Introduction

2.1 Data Collection

We accumulated 1000 online newspaper report from United States of America (USA) on COVID-19. The newspaper includes The Washington Post (USA) and StarTribune (USA). We have named it as “Covid-News-USA-NNK”. We also accumulated 50 online newspaper report from Bangladesh on the issue and named it “Covid-News-BD-NNK”. The newspaper includes The Daily Star (BD) and Prothom Alo (BD). All these newspapers are from the top provider and top read in the respective countries. The collection was done manually by 10 human data-collectors of age group 23- with university degrees. This approach was suitable compared to automation to ensure the news were highly relevant to the subject. The newspaper online sites had dynamic content with advertisements in no particular order. Therefore there were high chances of online scrappers to collect inaccurate news reports. One of the challenges while collecting the data is the requirement of subscription. Each newspaper required $1 per subscriptions. Some criteria in collecting the news reports provided as guideline to the human data-collectors were as follows:

The headline must have one or more words directly or indirectly related to COVID-19.

The content of each news must have 5 or more keywords directly or indirectly related to COVID-19.

The genre of the news can be anything as long as it is relevant to the topic. Political, social, economical genres are to be more prioritized.

Avoid taking duplicate reports.

Maintain a time frame for the above mentioned newspapers.

To collect these data we used a google form for USA and BD. We have two human editor to go through each entry to check any spam or troll entry.

2.2 Data Pre-processing and Statistics

Some pre-processing steps performed on the newspaper report dataset are as follows:

Remove hyperlinks.

Remove non-English alphanumeric characters.

Remove stop words.

Lemmatize text.

While more pre-processing could have been applied, we tried to keep the data as much unchanged as possible since changing sentence structures could result us in valuable information loss. While this was done with help of a script, we also assigned same human collectors to cross check for any presence of the above mentioned criteria.

The primary data statistics of the two dataset are shown in Table 1 and 2.

Table 1: Covid-News-USA-NNK data statistics

No of words per headline

7 to 20

No of words per body content

150 to 2100

Table 2: Covid-News-BD-NNK data statistics No of words per headline

10 to 20

No of words per body content

100 to 1500

2.3 Dataset Repository

We used GitHub as our primary data repository in account name NKK^1. Here, we created two repositories USA-NKK^2 and BD-NNK^3. The dataset is available in both CSV and JSON format. We are regularly updating the CSV files and regenerating JSON using a py script. We provided a python script file for essential operation. We welcome all outside collaboration to enrich the dataset.

3 Literature Review

Natural Language Processing (NLP) deals with text (also known as categorical) data in computer science, utilizing numerous diverse methods like one-hot encoding, word embedding, etc., that transform text to machine language, which can be fed to multiple machine learning and deep learning algorithms.

Some well-known applications of NLP includes fraud detection on online media sites[ 10 ], using authorship attribution in fallback authentication systems[ 11 ], intelligent conversational agents or chatbots[ 12 ] and machine translations used by Google Translate[ 13 ]. While these are all downstream tasks, several exciting developments have been made in the algorithm solely for Natural Language Processing tasks. The two most trending ones are BERT[ 14 ], which uses bidirectional encoder-decoder architecture to create the transformer model, that can do near-perfect classification tasks and next-word predictions for next generations, and GPT-3 models released by OpenAI[ 15 ] that can generate texts almost human-like. However, these are all pre-trained models since they carry huge computation cost. Information Extraction is a generalized concept of retrieving information from a dataset. Information extraction from an image could be retrieving vital feature spaces or targeted portions of an image; information extraction from speech could be retrieving information about names, places, etc[ 16 ]. Information extraction in texts could be identifying named entities and locations or essential data. Topic modeling is a sub-task of NLP and also a process of information extraction. It clusters words and phrases of the same context together into groups. Topic modeling is an unsupervised learning method that gives us a brief idea about a set of text. One commonly used topic modeling is Latent Dirichlet Allocation or LDA[17].

Keyword extraction is a process of information extraction and sub-task of NLP to extract essential words and phrases from a text. TextRank [ 18 ] is an efficient keyword extraction technique that uses graphs to calculate the weight of each word and pick the words with more weight to it.

Word clouds are a great visualization technique to understand the overall ’talk of the topic’. The clustered words give us a quick understanding of the content.

4 Our experiments and Result analysis

We used the wordcloud library^4 to create the word clouds. Figure 1 and 3 presents the word cloud of Covid-News-USA- NNK dataset by month from February to May. From the figures 1,2,3, we can point few information:

In February, both the news paper have talked about China and source of the outbreak.

StarTribune emphasized on Minnesota as the most concerned state. In April, it seemed to have been concerned more.

Both the newspaper talked about the virus impacting the economy, i.e, bank, elections, administrations, markets.

Washington Post discussed global issues more than StarTribune.

StarTribune in February mentioned the first precautionary measurement: wearing masks, and the uncontrollable spread of the virus throughout the nation.

While both the newspaper mentioned the outbreak in China in February, the weight of the spread in the United States are more highlighted through out March till May, displaying the critical impact caused by the virus.

We used a script to extract all numbers related to certain keywords like ’Deaths’, ’Infected’, ’Died’ , ’Infections’, ’Quarantined’, Lock-down’, ’Diagnosed’ etc from the news reports and created a number of cases for both the newspaper. Figure 4 shows the statistics of this series. From this extraction technique, we can observe that April was the peak month for the covid cases as it gradually rose from February. Both the newspaper clearly shows us that the rise in covid cases from February to March was slower than the rise from March to April. This is an important indicator of possible recklessness in preparations to battle the virus. However, the steep fall from April to May also shows the positive response against the attack. We used Vader Sentiment Analysis to extract sentiment of the headlines and the body. On average, the sentiments were from -0.5 to -0.9. Vader Sentiment scale ranges from -1(highly negative to 1(highly positive). There were some cases

where the sentiment scores of the headline and body contradicted each other,i.e., the sentiment of the headline was negative but the sentiment of the body was slightly positive. Overall, sentiment analysis can assist us sort the most concerning (most negative) news from the positive ones, from which we can learn more about the indicators related to COVID-19 and the serious impact caused by it. Moreover, sentiment analysis can also provide us information about how a state or country is reacting to the pandemic. We used PageRank algorithm to extract keywords from headlines as well as the body content. PageRank efficiently highlights important relevant keywords in the text. Some frequently occurring important keywords extracted from both the datasets are: ’China’, Government’, ’Masks’, ’Economy’, ’Crisis’, ’Theft’ , ’Stock market’ , ’Jobs’ , ’Election’, ’Missteps’, ’Health’, ’Response’. Keywords extraction acts as a filter allowing quick searches for indicators in case of locating situations of the economy,

Alaska is a large, geographically diverse region. Although only 1/5 the area of the rest of the United States, its shoreline of 75,000 km equals all of that of the Lower 48. The state remains the last major region of the United States for which a seaweed flora has yet to be written. As a preliminary step to writing that flora, Gayle Hansen and Sandra Lindstrom undertook to document the seaweed specimens that had been collected in the State and which are deposited in herbaria around the world, including type specimens of species reported to occur in Alaska. The Alaska Seaweed Database represents Sandra's contribution to that effort. More than 23,000 specimens were examined, and data from these were entered in the database. Since this National Science Foundation project ended in 2003, only a few additional records have been added. However, many additional specimens from Alaska have been deposited in the University of British Columbia herbarium since 2003. These newer records can be accessed at https://collections.beatymuseum.ubc.ca/dataset/90302970-1bc6-4865-be76-9aef1dd707f9

This dataset is a compilation of available oil and gas pipeline data and is maintained by BSEE. Pipelines are used to transport and monitor oil and/or gas from wells within the outer continental shelf (OCS) to resource collection locations. Currently, pipelines managed by BSEE are found in Gulf of Mexico and southern California waters.

© MarineCadastre.gov This layer is a component of BOEMRE Layers.

This Map Service contains many of the primary data types created by both the Bureau of Ocean Energy Management (BOEM) and the Bureau of Safety and Environmental Enforcement (BSEE) within the Department of Interior (DOI) for the purpose of managing offshore federal real estate leases for oil, gas, minerals, renewable energy, sand and gravel. These data layers are being made available as REST mapping services for the purpose of web viewing and map overlay viewing in GIS systems. Due to re-projection issues which occur when converting multiple UTM zone data to a single national or regional projected space, and line type changes that occur when converting from UTM to geographic projections, these data layers should not be used for official or legal purposes. Only the original data found within BOEM/BSEE’s official internal database, federal register notices or official paper or pdf map products may be considered as the official information or mapping products used by BOEM or BSEE. A variety of data layers are represented within this REST service are described further below. These and other cadastre information the BOEM and BSEE produces are generated in accordance with 30 Code of Federal Regulations (CFR) 256.8 to support Federal land ownership and mineral resource management.

For more information – Contact: Branch Chief, Mapping and Boundary Branch, BOEM, 381 Elden Street, Herndon, VA 20170. Telephone (703) 787-1312; Email: mapping.boundary.branch@boem.gov

The REST services for National Level Data can be found here: http://gis.boemre.gov/arcgis/rest/services/BOEM_BSEE/MMC_Layers/MapServer

REST services for regional level data can be found by clicking on the region of interest from the following URL: http://gis.boemre.gov/arcgis/rest/services/BOEM_BSEE

Individual Regional Data or in depth metadata for download can be obtained in ESRI Shape file format by clicking on the region of interest from the following URL: http://www.boem.gov/Oil-and-Gas-Energy-Program/Mapping-and-Data/Index.aspx

Currently the following layers are available from this REST location:

OCS Drilling Platforms -Locations of structures at and beneath the water surface used for the purpose of exploration and resource extraction. Only platforms in federal Outer Continental Shelf (OCS) waters are included. A database of platforms and rigs is maintained by BSEE.

OCS Oil and Natural Gas Wells -Existing wells drilled for exploration or extraction of oil and/or gas products. Additional information includes the lease number, well name, spud date, the well class, surface area/block number, and statistics on well status summary. Only wells found in federal Outer Continental Shelf (OCS) waters are included. Wells information is updated daily. Additional files are available on well completions and well tests. A database of wells is maintained by BSEE.

OCS Oil & Gas Pipelines -This dataset is a compilation of available oil and gas pipeline data and is maintained by BSEE. Pipelines are used to transport and monitor oil and/or gas from wells within the outer continental shelf (OCS) to resource collection locations. Currently, pipelines managed by BSEE are found in Gulf of Mexico and southern California waters.

Unofficial State Lateral Boundaries - The approximate location of the boundary between two states seaward of the coastline and terminating at the Submerged Lands Act Boundary. Because most State boundary locations have not been officially described beyond the coast, are disputed between states or in some cases the coastal land boundary description is not available, these lines serve as an approximation that was used to determine a starting point for creation of BOEM’s OCS Administrative Boundaries. GIS files are not available for this layer due to its unofficial status.

BOEM OCS Administrative Boundaries - Outer Continental Shelf (OCS) Administrative Boundaries Extending from the Submerged Lands Act Boundary seaward to the Limit of the United States OCS (The U.S. 200 nautical mile Limit, or other marine boundary)For additional details please see the January 3, 2006 Federal Register Notice.

BOEM Limit of OCSLA ‘8(g)’ zone - The Outer Continental Shelf Lands Act '8(g) Zone' lies between the Submerged Lands Act (SLA) boundary line and a line projected 3 nautical miles seaward of the SLA boundary line. Within this zone, oil and gas revenues are shared with the coastal state(s). The official version of the ‘8(g)’ Boundaries can only be found on the BOEM Official Protraction Diagrams (OPDs) or Supplemental Official Protraction described below.

Submerged Lands Act Boundary - The SLA boundary defines the seaward limit of a state's submerged lands and the landward boundary of federally managed OCS lands. The official version of the SLA Boundaries can only be found on the BOEM Official Protraction Diagrams (OPDs) or Supplemental Official Protraction Diagrams described below.

Atlantic Wildlife Survey Tracklines(2005-2012) - These data depict tracklines of wildlife surveys conducted in the Mid-Atlantic region since 2005. The tracklines are comprised of aerial and shipboard surveys. These data are intended to be used as a working compendium to inform the diverse number of groups that conduct surveys in the Mid-Atlantic region.The tracklines as depicted in this dataset have been derived from source tracklines and transects. The tracklines have been simplified (modified from their original form) due to the large size of the Mid-Atlantic region and the limited ability to map all areas simultaneously.The tracklines are to be used as a general reference and should not be considered definitive or authoritative. This data can be downloaded from http://www.boem.gov/uploadedFiles/BOEM/Renewable_Energy_Program/Mapping_and_Data/ATL_WILDLIFE_SURVEYS.zip

BOEM OCS Protraction Diagrams & Leasing Maps - This data set contains a national scale spatial footprint of the outer boundaries of the Bureau of Ocean Energy Management’s (BOEM’s) Official Protraction Diagrams (OPDs) and Leasing Maps (LMs). It is updated as needed. OPDs and LMs are mapping products produced and used by the BOEM to delimit areas available for potential offshore mineral leases, determine the State/Federal offshore boundaries, and determine the limits of revenue sharing and other boundaries to be considered for leasing offshore waters. This dataset shows only the outline of the maps that are available from BOEM.Only the most recently published paper or pdf versions of the OPDs or LMs should be used for official or legal purposes. The pdf maps can be found by going to the following link and selecting the appropriate region of interest. http://www.boem.gov/Oil-and-Gas-Energy-Program/Mapping-and-Data/Index.aspx Both OPDs and LMs are further subdivided into individual Outer Continental Shelf(OCS) blocks which are available as a separate layer. Some OCS blocks that also contain other boundary information are known as Supplemental Official Block Diagrams (SOBDs.) Further information on the historic development of OPD's can be found in OCS Report MMS 99-0006: Boundary Development on the Outer Continental Shelf: http://www.boemre.gov/itd/pubs/1999/99-0006.PDF Also see the metadata for each of the individual GIS data layers available for download. The Official Protraction Diagrams (OPDs) and Supplemental Official Block Diagrams (SOBDs), serve as the legal definition for BOEM offshore boundary coordinates and area descriptions.

BOEM OCS Lease Blocks - Outer Continental Shelf (OCS) lease blocks serve as the legal definition for BOEM offshore boundary coordinates used to define small geographic areas within an Official Protraction Diagram (OPD) for leasing and administrative purposes. OCS blocks relate back to individual Official Protraction Diagrams and are not uniquely numbered. Only the most recently published paper or pdf

This layer was created as part of Esri’s Green Infrastructure Initiative and is one of five newly generated companion datasets that can be used for Green Infrastructure (GI) planning at national, regional, and more local scales. If used together, these layers should have corresponding date-based suffixes (YYYYMMDD). The corresponding layer names are: Intact Habitat Cores, Habitat Connectors, Habitat Fragments, Habitat Cost Surface, and Intact Habitat Cores by Betweenness. These Esri derived data, and additional data central to GI planning from other authoritative sources, are also available as Map Packages for each U.S. State and can be downloaded from the Green Infrastructure Data Gallery.

This layer represents modeled Intact Habitat Cores, or minimally disturbed natural areas at least 100 acres in size and greater than 200 meters wide. Esri created these data following a methodology outlined by the Green Infrastructure Center Inc. These data were generated using 2011 National Land Cover Data. Cores were derived from all “natural” land cover classes and excluded all “developed” and “agricultural” classes including crop, hay and pasture lands. The resulting cores were tested for size and width requirements (at least 100 acres in size and greater than 200 meters wide) and then converted into unique polygons. This process resulted in the generation of over 550,000 cores. Cores were then overlaid with a diverse assortment of physiographic, biologic and hydrographic layers to populate each core with attributes (53 in total) related to the landscape characteristics found within. These data were also compiled to compute a “core quality index”, or score related to the perceived ecological value of each core, to provide users with additional insight related to the importance of each core when compared to all others. See this map image layer for a version that includes popups and ability to query the data. The source data used to derive this attribution is as follows: Number of endemic species (Mammals, Fish, Reptiles, Amphibians, Trees) (Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16)

Priority Index areas: Endemic species, small home range size and low protection status. (Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16)

Unique ecological systems (based upon work by Aycrig, Jocelyn L, et. al. (2013) Representation of Ecological Systems within the Protected Areas Network of the Continental United States. PLos One 8(1):e54689). New data constructed by Esri staff, using TNC Ecological Regions as summary areas.

Ecologically relevant landforms (Theobald DM, Harrison-Atlas D, Monahan WB, Albano CM (2015) Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning. PLoS ONE 10(12): e0143619. doi:10.1371/journal.pone.0143619 ,

Local Landforms (produced 3/2016) by Deniz Basaran and Charlie Frye, Esri, 30 m* resolution. "Improved Hammond’s Landform Classification and Method for Global 250-m Elevation Data" by Karagulle, Deniz; Frye, Charlie; Sayre, Roger; Breyer, Sean; Aniello, Peter; Vaughan, Randy; Wright, Dawn, has been successfully submitted online and is presently being given consideration for publication in Transactions in GIS. *We scaled the neighborhood windows from the 250-meter method described in the paper, and then applied that to 30-meter data in the U.S.

National Elevation Dataset, USGS, 30 m resolution

NWI – National Wetlands Inventory “ Classification of Wetlands and Deepwater Habitats of the United States”. U.S. Department of the Interior, Fish and Wildlife Service, Washington, DC. FWS/OBS-79/31 , U.S. Fish and Wildlife Service, Division of Habitat and Resource Conservation (prepared 10/2015)

NLCD 2011 – National LandCover Database 2011 (downloaded 1/2016) Homer, C.G., et. al. 2015,Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354

NHDPlusV2 Received from Charlie Frye, Esri 3/2016. Produced by the EPA with support from the USGS.

gSSURGO –Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Accessed 3/2016, 30 m resolution

GAP Level 3 Ecological System Boundaries (downloaded 4/ 2016) NOAA CCAP Coastal Change Analysis Program Regional Land Cover and Change–downloaded by state (3/2016) from: C-CAP FTP Tool, see Description of this 30 m resolution, 2010 edition of data

NHD USGS National Hydrography Dataset

TNC Terrestrial Ecoregions (downloaded 3/2016)

2015 LCC Network Areas

Evaluation:

The creation of a national core quality index is a very ambitious objective, given the extreme variability in ecosystem conditions across the United States. The additional attributes were intended to provide flexibility in accommodating regional or local environmental differences across the U.S.

Scripts for constructing local cores and scoring them using the Green Infrastructure Center’s methodology are available on Esri's Green Infrastructure web site.

Two general approaches were used in the developing core quality index values. The first (default) follows the guidance of the Green Infrastructure Center’s scoring approach developed for the southeastern US where size of the core is the primary determinant of quality. The second; Bio-Weights puts more emphasis on bio-diversity and uniqueness ecosystem type and de-emphasizes slightly the importance of core size. This is to compensate for the very large intact core habitat areas in the west and southwest which also have comparatively low biodiversity values.

Scoring values:

Default Weights

0.4, # Acres0.1, # THICKNESS0.05, # TOPOGRAPHIC DIVERSITY (Standard Deviation)0.1, # Biodiversity Priority Index (SPECIES RICHNESS in GIC original version)0.05, # PERCENTAGE WETLAND COVER0.03, # Ecological Land Unit – Shannon-Weaver Index (SOIL VARIETY in GIC original version)0.02, # COMPACTNESS RATIO (AREA RELATIVE TO THE AREA OF A CIRCLE WITH THE SAME PERIMETER LENGTH)0.1, # STREAM DENSITY (LINEAR FEET/ACRE)0.05, # Ecological System Redundancy (RARE/THREATENED/ENDANGERED SPECIES ABUNDANCE (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (RARE/THREATENED/ENDANGERED SPECIES DIVERSITY (Number of unique species in a core) in GIC original version)

Bio-Weights

0.2, # Acres0.1, # THICKNESS0.05, # TOPOGRAPHIC DIVERSITY (Standard Deviation)0.25, # Biodiversity Priority Index (SPECIES RICHNESS in GIC original version)0.05, # PERCENTAGE WETLAND COVER0.03, # Ecological Land Unit – Shannon-Weaver Index (SOIL VARIETY in GIC original version)0.02, # COMPACTNESS RATIO (AREA RELATIVE TO THE AREA OF A CIRCLE WITH THE SAME PERIMETER LENGTH)0.1, # STREAM DENSITY (LINEAR FEET/ACRE)0.1, # Ecological System Redundancy (RARE/THREATENED/ENDANGERED SPECIES ABUNDANCE (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (RARE/THREATENED/ENDANGERED SPECIES DIVERSITY (Number of unique species in a core) in GIC original version)

Source MXD: GICores_03_05_All_Cores.mxd

Context

Information on more than 180,000 Terrorist Attacks

The Global Terrorism Database (GTD) is an open-source database including information on terrorist attacks around the world from 1970 through 2017. The GTD includes systematic data on domestic as well as international terrorist incidents that have occurred during this time period and now includes more than 180,000 attacks. The database is maintained by researchers at the National Consortium for the Study of Terrorism and Responses to Terrorism (START), headquartered at the University of Maryland. [More Information][1]

Content

Geography: Worldwide

Time period: 1970-2017, except 1993

Unit of analysis: Attack

Variables: >100 variables on location, tactics, perpetrators, targets, and outcomes

Sources: Unclassified media articles (Note: Please interpret changes over time with caution. Global patterns are driven by diverse trends in particular regions, and data collection is influenced by fluctuations in access to media coverage over both time and place.)

Definition of terrorism:

"The threatened or actual use of illegal force and violence by a non-state actor to attain a political, economic, religious, or social goal through fear, coercion, or intimidation."

See the [GTD Codebook][2] for important details on data collection methodology, definitions, and coding schema.

Acknowledgements

The Global Terrorism Database is funded through START, by the US Department of State (Contract Number: SAQMMA12M1292) and the US Department of Homeland Security Science and Technology Directorate’s Office of University Programs (Award Number 2012-ST-061-CS0001, CSTAB 3.1). The coding decisions and classifications contained in the database are determined independently by START researchers and should not be interpreted as necessarily representing the official views or policies of the United States Government.

[GTD Team][3]

Publications

The GTD has been leveraged extensively in [scholarly publications][4], [reports][5], and [media articles][6]. [Putting Terrorism in Context: Lessons from the Global Terrorism Database][7], by GTD principal investigators LaFree, Dugan, and Miller investigates patterns of terrorism and provides perspective on the challenges of data collection and analysis. The GTD's data collection manager, Michael Jensen, discusses important [Benefits and Drawbacks of Methodological Advancements in Data Collection and Coding][8].

Terms of Use

Use of the data signifies your agreement to the following [terms and conditions][9].

END USER LICENSE AGREEMENT WITH UNIVERSITY OF MARYLAND

IMPORTANT – THIS IS A LEGAL AGREEMENT BETWEEN YOU ("You") AND THE UNIVERSITY OF MARYLAND, a public agency and instrumentality of the State of Maryland, by and through the National Consortium for the Study of Terrorism and Responses to Terrorism (“START,” “US,” “WE” or “University”). PLEASE READ THIS END USER LICENSE AGREEMENT (“EULA”) BEFORE ACCESSING THE Global Terrorism Database (“GTD”). THE TERMS OF THIS EULA GOVERN YOUR ACCESS TO AND USE OF THE GTD WEBSITE, THE DATA, THE CODEBOOK, AND ANY AUXILIARY MATERIALS. BY ACCESSING THE GTD, YOU SIGNIFY THAT YOU HAVE READ, UNDERSTAND, ACCEPT, AND AGREE TO ABIDE BY THESE TERMS AND CONDITIONS. IF YOU DO NOT ACCEPT THE TERMS OF THIS EULA, DO NOT ACCESS THE GTD.

TERMS AND CONDITIONS

1. GTD means Global Terrorism Database data and the online user interface (www.start.umd.edu/gtd) produced and maintained by the National Consortium for the Study of Terrorism and Responses to Terrorism (START). This includes the data and codebook, any auxiliary materials present, and the user interface by which the data are presented.

2. LICENSE GRANT. University hereby grants You a revocable, non-exclusive, non-transferable right and license to access the GTD and use the data, the codebook, and any auxiliary materials solely for non-commercial research and analysis.

3. RESTRICTIONS. You agree to NOT: a. publicly post or display the data, the codebook, or any auxiliary materials without express written permission by University of Maryland (this excludes publication of analysis or visualization of the data for non-commercial purposes); b. sell, license, sublicense, or otherwise distribute the data, the codebook, or any auxiliary materials to third parties for cash or other considerations; c. modify, hide, delete or interfere with any notices that are included on the GTD or the codebook, or any auxiliary materials; d. use the GTD to draw conclusions about the official legal status or criminal record of an individual, or the status of a criminal or civil investigation; e. interfere with or disrupt the GTD website or servers and networks connected to the GTD website; or f. use robots, spiders, crawlers, automated devices and similar technologies to screen-scrape the site or to engage in data aggregation or indexing of the da...

description: Species occurrence records of the taxonomic class of Bivalvia in oceans within 1000 kilometers of the United States shoreline. This is a subset of the OBIS-USA dataset where Bivalvia records were queried on December 2, 2014. After initial queries, the remaining data were further queried to retain only samples within 1000 kilometers of the U.S. shoreline. Spatial queries were then used to remove samples overlaying land masses. Data are provided in a geodatabase format, as well as a comma seperated values format. OBIS-USA provides aggregated, interoperable biogeographic data collected primarily from U.S. waters and oceanic regions--the Arctic, the Atlantic and Pacific oceans, the Caribbean Sea, Gulf of Mexico and the Great Lakes. It provides access to datasets from state and federal agencies as well as educational and research institutions. OBIS-USA handles both specimen-based data and survey results. Survey data come from recovered archives and current research activities. The datasets document where and when species were observed or collected, bringing together marine biogeographic data that are spatially, taxonomically, and temporally comprehensive. The public OBIS-USA site (http://www.usgs.gov/obis-usa) provides actual data contents as well as summary data about what is contained in each dataset to assist users in evaluating suitability for use. Current functionality allows the user to locate, view, and aggregate the datasets and FGDC compliant metadata as well as to view and search the taxonomic, geographic, and temporal extent. To promote data interoperability, the data are available in accordance with the marine-focused implementation of the Darwin Core data standard. In addition to basic download functions (tab-delimited), OBIS-USA offers web services for query flexibility and a wide range of output formats, such as kml, NetCDF, MATLAB, json, and graph or map output, to enable diverse types of scientific and geospatial data use and analysis platforms and products. OBIS-USA's two web services (ERDDAP and GeoServer) enable integration of OBIS-USA biogeographic data with other data types, such as seafloor geology, physical oceanography, water chemistry, and climate data. The NOAA Environmental Research Division Data Access Program(ERRDDAP) enables users to query scientific data by flexible parameters and obtain output in many formats. Access can be found at http://www1.usgs.gov/erddap/tabledap/AllMBG.html. OBIS-USA uses the tabledap component of ERDDAP to access Darwin-Core-type tabular spatial data; tabledap is a superset of the OPeNDAP DAP constraint protocol. OBIS-USA offers an ESRI REST Service with access to Darwin-Core-type point data at http://gis1.usgs.gov/arcgis/rest/services/OBISUSA/OBIS_USA_All_Marine_Biogeographic_Records/MapServer/ and an OGC compliant Web Mapping Service (wms) http://gis1.usgs.gov/arcgis/services/OBISUSA/OBIS_USA_All_Marine_Biogeographic_Records/MapServer/WMSServer?request=GetCapabilities&service=WMS. OBIS-USA and collaborators are further deploying the Darwin Core standard to capture richer information, such as absence and abundance, observations on effort, individual tracking, and more advanced biogeography capabilities. Data are accepted into OBIS-USA from the data originator or holder, minimizing the burden on the participant. OBIS-USA works with data providers to understand the best process to transfer the data, review the data prior to their release, gather comprehensive metadata, and then allow public access to this information. Becoming part of the OBIS-USA network is intended to have tangible benefits for participants, for example, freeing the participant from responding to requests for data and alleviating security concerns since users do not directly access the participant's computers.; abstract: Species occurrence records of the taxonomic class of Bivalvia in oceans within 1000 kilometers of the United States shoreline. This is a subset of the OBIS-USA dataset where Bivalvia records were queried on December 2, 2014. After initial queries, the remaining data were further queried to retain only samples within 1000 kilometers of the U.S. shoreline. Spatial queries were then used to remove samples overlaying land masses. Data are provided in a geodatabase format, as well as a comma seperated values format. OBIS-USA provides aggregated, interoperable biogeographic data collected primarily from U.S. waters and oceanic regions--the Arctic, the Atlantic and Pacific oceans, the Caribbean Sea, Gulf of Mexico and the Great Lakes. It provides access to datasets from state and federal agencies as well as educational and research institutions. OBIS-USA handles both specimen-based data and survey results. Survey data come from recovered archives and current research activities. The datasets document where and when species were observed or collected, bringing together marine biogeographic data that are spatially, taxonomically, and temporally comprehensive. The public OBIS-USA site (http://www.usgs.gov/obis-usa) provides actual data contents as well as summary data about what is contained in each dataset to assist users in evaluating suitability for use. Current functionality allows the user to locate, view, and aggregate the datasets and FGDC compliant metadata as well as to view and search the taxonomic, geographic, and temporal extent. To promote data interoperability, the data are available in accordance with the marine-focused implementation of the Darwin Core data standard. In addition to basic download functions (tab-delimited), OBIS-USA offers web services for query flexibility and a wide range of output formats, such as kml, NetCDF, MATLAB, json, and graph or map output, to enable diverse types of scientific and geospatial data use and analysis platforms and products. OBIS-USA's two web services (ERDDAP and GeoServer) enable integration of OBIS-USA biogeographic data with other data types, such as seafloor geology, physical oceanography, water chemistry, and climate data. The NOAA Environmental Research Division Data Access Program(ERRDDAP) enables users to query scientific data by flexible parameters and obtain output in many formats. Access can be found at http://www1.usgs.gov/erddap/tabledap/AllMBG.html. OBIS-USA uses the tabledap component of ERDDAP to access Darwin-Core-type tabular spatial data; tabledap is a superset of the OPeNDAP DAP constraint protocol. OBIS-USA offers an ESRI REST Service with access to Darwin-Core-type point data at http://gis1.usgs.gov/arcgis/rest/services/OBISUSA/OBIS_USA_All_Marine_Biogeographic_Records/MapServer/ and an OGC compliant Web Mapping Service (wms) http://gis1.usgs.gov/arcgis/services/OBISUSA/OBIS_USA_All_Marine_Biogeographic_Records/MapServer/WMSServer?request=GetCapabilities&service=WMS. OBIS-USA and collaborators are further deploying the Darwin Core standard to capture richer information, such as absence and abundance, observations on effort, individual tracking, and more advanced biogeography capabilities. Data are accepted into OBIS-USA from the data originator or holder, minimizing the burden on the participant. OBIS-USA works with data providers to understand the best process to transfer the data, review the data prior to their release, gather comprehensive metadata, and then allow public access to this information. Becoming part of the OBIS-USA network is intended to have tangible benefits for participants, for example, freeing the participant from responding to requests for data and alleviating security concerns since users do not directly access the participant's computers.

This feature layer shows the locations of the California State Operations Center and the three regional Emergency Operation Centers.California Office of Emergency ServicesWith over 38 million residents (12% of the population), the State of California is the most populous state in the nation and has the third largest land area among the states (163,695 square miles). California is culturally, ethnically, economically, ecologically, and politically diverse, and maintains the eighth largest economy in the world with 13 percent of the U.S. gross domestic product. California also faces numerous risks and threats to our people, property, economy, environment and is prone to earthquakes, floods, significant wildfires, prolonged drought impacts, public health emergencies, cybersecurity attacks, agricultural and animal disasters, as well threats to homeland security. Cal OES takes a proactive approach to addressing these risks, threats, and vulnerabilities that form the basis of our mission and has been tested through real events, as well as comprehensive exercises that help us maintain our state of readiness and plan for and mitigate impacts.Regional Operations​The California Office of Emergency Services (Cal OES) Agency has three administrative regions, Inland, Coastal and Southern which are located in Sacramento, Fairfield and Los Alamitos, respectively. Cal OES regions have the responsibility to carry out the coordination of information and resources within the region and between the SEMS state and regional levels to ensure effective and efficient support to local response. The regions serve as the conduit for local and regional perspective and provide a physical presence for Cal OES functions at the local level in all phases of emergency management.