The goal of the Arrestee Drug Abuse Monitoring (ADAM) Program is to determine the extent and correlates of illicit drug use in the population of booked arrestees in local areas. Data were collected in 2001 at four separate times (quarterly) during the year in 33 metropolitan areas in the United States. The ADAM program adopted a new instrument in 2000 in adult booking facilities for male (Part 1) and female (Part 2) arrestees. Data from arrestees in juvenile detention facilities (Part 3) continued to use the juvenile instrument from previous years, extending back through the DRUG USE FORECASTING series (ICPSR 9477). The ADAM program in 2001 also continued the use of probability-based sampling for male arrestees in adult facilities, which was initiated in 2000. Therefore, the male adult sample includes weights, generated through post-sampling stratification of the data. For the adult files, variables fell into one of eight categories: (1) demographic data on each arrestee, (2) ADAM facesheet (records-based) data, (3) data on disposition of the case, including accession to a verbal consent script, (4) calendar of admissions to substance abuse and mental health treatment programs, (5) data on alcohol and drug use, abuse, and dependence (6) drug acquisition data covering the five most commonly used illicit drugs, (7) urine test results, and (8) weights. The juvenile file contains demographic variables and arrestee's self-reported past and continued use of 15 drugs, as well as other drug-related behaviors.

Beginning in 1996, the National Institute of Justice (NIJ) initiated a major redesign of its multisite drug-monitoring program, the Drug Use Forecasting (DUF) system (DRUG USE FORECASTING IN 24 CITIES IN THE UNITED STATES, 1987-1997 [ICPSR 9477]). The program was retitled Arrestee Drug Abuse Monitoring (ADAM) (see ARRESTEE DRUG ABUSE MONITORING (ADAM) PROGRAM IN THE UNITED STATES, 1998 [ICPSR 2628] and 1999 [ICPSR 2994]). ADAM extended DUF in the number of sites and improved the quality and generalizability of the data. The redesign was fully implemented in all sites beginning in the first quarter of 2000. The ADAM program implemented a new and expanded adult instrument in the first quarter of 2000, which was used for both the male (Part 1) and female (Part 2) data. The juvenile data for 2000 (Part 3) used the juvenile instrument from previous years. The ADAM program also moved to probability-based sampling for the adult male population during 2000. Therefore, the 2000 adult male sample includes weights, generated through post-sampling stratification of the data. The shift to sampling of the adult male population in 2000 required that all 35 sites move to a common catchment area, the county. The core instrument for the adult cases was supplemented by a facesheet, which was used to collect demographic and charge information from official records. Core instruments were used to collect self-report information from the respondent. Both the adult and juvenile instruments were administered to persons arrested and booked on local or state charges relevant to the jurisdiction (i.e., not federal or out-of-county charges) within the past 48 hours. At the completion of the interview the arrestee was asked to voluntarily provide a urine specimen. An external lab used the Enzyme Multiplied Immunoassay Testing (EMIT) protocols to test for the presence of ten drugs or metabolites of the drug in the urine sample. All amphetamine positives were confirmed by gas chromatography/mass spectrometry (GC/MS) to determine whether methamphetamine was used. For the adult data, variables from the facesheet include arrest precinct, ZIP code of arrest location, ZIP code of respondent's address, respondent's gender and race, three most serious arrest charges, sample source (stock, flow, other), interview status (including reason the individual selected in the sample was not interviewed), language of instrument used, and the number of hours since arrest. Demographic information from the core instrument includes respondent's age, ethnicity, residency, education, employment, health insurance coverage, marital status, housing, and telephone access. Variables from the calendar provide information on inpatient and outpatient substance abuse treatment, inpatient mental health treatment, arrests and incarcerations, heavy alcohol use, use of marijuana, crack/rock cocaine, powder cocaine, heroin, methamphetamine, and other drug (ever and previous 12 months), age of first use of the above six drugs and heavy alcohol use, drug dependency in the previous 12 months, characteristics of drug transactions in past 30 days, use of marijuana, crack/rock cocaine, powder cocaine, heroin, and methamphetamine in past 30 days, 7 days, and 48 hours, heavy alcohol use in past 30 days, and secondary drug use of 15 other drugs in the past 48 hours. Urine test results are provided for 11 drugs -- marijuana, cocaine, opiates, phencyclidine (PCP), benzodiazepines (Valium), propoxyphene (Darvon), methadone, methaqualone, barbiturates, amphetamines, and methamphetamine. The adult data files include several derived variables. The male data also include four sampling weights, and stratum identifications and percents. For the juvenile data, demographic variables include age, race, sex, educational attainment, employment status, and living circumstances. Data also include each juvenile arrestee's self-reported use of 15 drugs (alcohol, tobacco, marijuana, powder cocaine, crack, heroin, PCP, amphetamines, barbiturates, quaaludes, methadone, crystal methamphetamine, Valium, LSD, and inhalants). For each drug type, arrestees reported whether they had ever used the drug, age of first use, whether they had used the drug in the past 30 days and past 72 hours, number of days they used the drug in past month, whether they tried to cut down or quit using the drug, if they were successful, whether they felt dependent on the drug, whether they were receiving treatment for the drug, whether they had received treatment for the drug in the past, and whether they thought they could use treatment for that drug. Additional variables include whether juvenile respondents had ever injected drugs, whether they were influenced by drugs when they allegedly committed the crime for which they were arrested, whether they had been to an emergency room for drug-related incidents, and if so, whether in the past 12 months, and information on arrests and charges in the past 12 months. As with the adult data, urine test results are also provided. Finally, variables covering precinct (precinct of arrest) and law (penal law code associated with the crime for which the juvenile was arrested) are also provided for use by local law enforcement officials at each site.

About this dataset

Context

The dataset tabulates the Adams population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Adams across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Adams was 653, a 1.08% increase year-by-year from 2022. Previously, in 2022, Adams population was 646, an increase of 2.38% compared to a population of 631 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Adams increased by 81. In this period, the peak population was 670 in the year 2019. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Adams is shown in this column.
• Year on Year Change: This column displays the change in Adams population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Adams Population by Year. You can refer the same here

Readme file for ADAM-SDMH: A DAtaset from Manipal for Severity Detection in Tweets related to Mental Health Generated on 2021-02-15Recommended citation for the dataset:P. Surana, M. Yusuf and S. Singh, "Severity Classification of Mental Health-Related Tweets," 2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), 2021, pp. 336-341, DOI: 10.1109/DISCOVER52564.2021.9663651.******************************PROJECT INFORMATION******************************1. Title of dataset: Mental Health Dataset2. Author information:Praatibh Surana, Manipal Institute of Technology,Mirza Yusuf, Manipal Institute of Technology,Sanjay Singh, Manipal Institute of TechnologyPrincipal Investigators Name: Praatibh SuranaAddress: Manipal Institute of TechnologyEmail: praatibhsurana@gmail.comName: Mirza YusufAddress: Manipal Institute of TechnologyEmail: baig.yusuf.cr7@gmail.comCo-InvestigatorName: Sanjay SinghAddress: Manipal Institute of TechnologyEmail: sanjay.singh@manipal.edu3. Date of data collection: Jan 2021 - Feb 2021************************************DATA ACCESS INFORMATION************************************1. Licences/restrictions placed on access to the dataset: CC BY 4.02. Links to publications that use the data:URL: https://ieeexplore.ieee.org/document/9663651,DOI: 10.1109/DISCOVER52564.2021.96636513. Links to a third party or secondary data used in the project (for example, existing datasets, third-party datasets)Pennington, Jeffrey et al. “GloVe: Global Vectors for Word Representation.” EMNLP (2014).DOI: https://doi.org/10.3115/v1/d14-1162*****************************************METHODS OF DATA COLLECTION*****************************************1. Describe the methods for data collection and/or provide links to papers describing data collection methodsPaper DOI :Our research revolved around correctly classifying tweets based on their severity in a mental health context. An effort was also made to make the models detect sarcasm better, as this was something that many models in the past failed to do. Our dataset consists of tweets labeled as ‘0’, ‘1’, and '2' depending on their classes. The labeling rules followed are given in Table 1TABLE 1 - SEVERITY CLASSIFICATION CLASSES AND EXAMPLES-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Class | Rules | Example | |0 | Helping / suggestion for mental health awareness | Are you suffering from anxiety? Check out this page for therapy through Skype! | / positivity / informative | | / motivational | | / questions about mental health | | |1 | Sarcasm/rant/expression of annoyance | Today was so annoying. If my teacher would have called my name, I swear to God I would have killed myself | |2 | Case of slight disturbance | All I am is a burden. I don’t want to live anymore. | / strong indication of disturbance | | / user outright mentions depression | | / anxiety / suicide / self-harm |-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------The following steps were performed for data collection:1) Tweets were extracted with the help of Twitter’s official API using hashtags such as #depression, #mentalhealth, #anxiety, #selfharm, #killmyself, and #kms from users.2) Around 40,000 tweets were extracted from Twitter between January and February 2021, out of which the final dataset comprised of 2460 tweets; 820 tweets were distributed equally amongst the three classes.3) Two authors manually annotated the dataset and cross-verified it to ensure accurate labeling.2. Data processing methods:A. Preprocessing1) Removal of numbers, URLs, usernames, and special characters: The first step after extraction of the tweets was ensuring that they were suitable for further use. The “preprocessor” uses the Python library to eliminate numbers, retweets, URLs, emojis, emoticons, and usernames, followed by duplicate tweets removal from the dataset.2) Stopword removal and expansion of standard abbreviations: We made use of Python’s “nltk” library for the removal of common stopwords such as “for,” “the,” “a,” etc. As our data is sourced from Twitter, lots of common internet abbreviations like “lol,” “kms,” “gn,”etc., were used. It was taken care of by converting these short forms to their corresponding complete forms. Lots of short forms like “wanna” for “want to” and “gonna” for “going to” were used. We ensured that these, too, were taken care of to the best of our abilities. 3) Removal of names, so that anonymity is maintained. Names of people, places, twitter handles anything that could compromise the anonymity has been removed, a token named as ‘[redacted]’ has been used in their place instead.*******************************SUMMARY OF DATA FILE*******************************Filename: MentalHealthTweets.csvShort description: This CSV File contains 2460 tweets annotated ‘0’, ‘1’ or ‘2’ based on the class they belong to.*******************************************************************DATA-SPECIFIC INFORMATION FOR NOTE: This section should be copied and pasted for each file*******************************************************************1. Number of variables: 22. Number of cases rows: 24613. Missing data codes: NA4. Variable listThe variables and their properties have been provided in Table 2TABLE 2 - VARIABLE DESCRIPTION TABLE----------------------------------------------------------------------Variable Name | Variable Description | Variable Type | |tweets | Cleaned up tweet | String | |label | Annotation for tweet | Integer----------------------------------------------------------------------

General Info

This dataset contains monthly output from two 20-year (1979-1998) variable-resolution (VR) CESM2 simulations (HMA_VR7a and HMA_VR7b). The coupled atmosphere-land simulations were run with a newly generated VR grid that has regional grid refinements up to 7 km over High Mountain Asia. The HMA_VR7b simulation was performed with an updated glacier-cover dataset (https://doi.org/10.5281/zenodo.7864689) and includes snow and glacier model modifications. Further, monthly output from a globally uniform 1-degree CESM simulation (NE30), used for evaluation of the HMA VR simulations, is also included. The monthly output have been used for analysis and discussion in the paper “Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia” that is currently under review in the Cryosphere Discussions, https://tc.copernicus.org/preprints/tc-2022-256/.

Contact

René Wijngaard (r.r.wijngaard.uu@gmail.com / r.r.wijngaard@uu.nl)

Raw Data

Raw monthly and daily unstructured HMA VR model output are available on request.

Dataset Contents

NE30.tar

HMA_VR7a.tar

HMA_VR7b.tar 

These files contain atmosphere (CAM) and land (CLM) model output that are regridded to a 1-degree finite volume (0.9 x 1.25 degrees latitude/longitude) grid. The following variables are included: CLDLIQ, OMEGA, Q, STEND_CLUBB, SWCF, T, Z3, EFLX_LH_TOT, FGR, FIRE, FLDS, FSA, FSDS, FSH, FSM, FSNO, FSM, FSR, H2OSNO, PCT_LANDUNIT, QICE_MELT, QSNOFRZ, RAIN, SNOW, and TSA.

SMB_HMA_VR7a.tar

SMB_HMA_VR7b.tar

These files contain unstructured SMB-related CLM model output (i.e., on the HMA VR grid). The following variables are included: PCT_LANDUNIT, QRUNOFF_ICE, QSNOFRZ_ICE, QSNOMELT_ICE, QSOIL_ICE, RAIN_ICE, and SNOW_ICE.

Specification of the variables included in S1 Data.

This data set contains the metadata for periodic variable stars that have been classified by Citizen Scientists using the SuperWASP Variable Stars Zooniverse project.

The data set is in the same format as custom data exports generated via the superwasp.org website. It consists of three files:

• export.csv: The main data export in CSV format, containing one row per folded light curve (i.e. multiple rows per source object).
• fields.yaml: A YAML-format list of the columns included in the CSV export with an English description of each one.
• params.yaml: A YAML-format copy of the search and filtering parameters which were used to generate the export (in this case this is the full data set with no filtering applied). Also includes a data version number which will be incremented with future data releases or changes to the export format.

Photometry data is also available for download in FITS and JSON format, but this is not included here. URLs for the photometry files are included in export.csv for ease of downloading.

Acknowledgements

The SuperWASP project is currently funded and operated by Warwick University and Keele University, and was originally set up by Queen’s University Belfast, the Universities of Keele, St. Andrews and Leicester, the Open University, the Isaac Newton Group, the Instituto de Astrofisica de Canarias, the South African Astronomical Observatory and by STFC.

The Zooniverse project on SuperWASP Variable Stars is led by Andrew Norton (The Open University) and builds on work he has done with his former postgraduate students Les Thomas, Stan Payne, Marcus Lohr, Paul Greer, and Heidi Thiemann, and current postgraduate student Adam McMaster.

The Zooniverse project on SuperWASP Variable Stars was developed with the help of the ASTERICS Horizon2020 project. ASTERICS is supported by the European Commission Framework Programme Horizon 2020 Research and Innovation action under grant agreement n.653477

VeSPA was designed and developed by Adam McMaster as part of his postgraduate work. This work is funded by STFC, DISCnet, and the Open University Space SRA. Server infrastructure was funded by the Open University Space SRA.

A pilot outreach project of the National Intstitute of Justice's Arrestee Drug Abuse Monitoring (ADAM) program, the rural Nebraska ADAM program examined the prevalence and type of arrestee drug use in four rural Nebraska counties and compared the results to those found in Omaha, Nebraska, an established ADAM site. The data were collected in Madison (n=78), Dawson (n=50), Hall (n=53), and Scotts Bluff (n=149) counties, and Omaha, Nebraska, (n=202) in October and November of 1998. The catchment area for Omaha was the central city. The ADAM interview provided demographic and descriptive data, including race, age, marital status, source of income, screens of substance abuse and dependency, treatment history, arrest and incarceration experiences, and participation in local drug markets. At the conclusion of the interview, respondents were asked to provide a urine specimen. The current study included a supplemental questionnaire about methamphetamine use. The methamphetamine addendum included variables on why the respondent began and continued the use of methamphetamines, how often and how much methamphetamine was used, if and why the respondent had ever sought and completed treatment, source of the methamphetamine, and if the respondent had ever made or sold methamphetamine.

Despite the ubiquitous nature of parasitism, the general effects of how parasitism alters the outcome of host species interactions such as competition, mutualism, predation, and reproduction remain unknown. Using a meta-analysis of 178 studies, we examined how the outcomes of diverse species interactions differed between parasitized and non-parasitized hosts. We also evaluated how the effects of parasitism on species interactions varied geographically with latitude. Overall, parasitism had relatively large deleterious effects on the outcome of host species interactions. However, there was considerable variation among interactions in these outcomes, with reproduction severely negatively affected, marginal effects on competition, and muted effects on predation – the latter results emanating because of the surprising tendency of parasitism to frequently reduce the effects of competition and predation. The effects of parasites did not differ between macro- and microparasites, nor did the shared evolutionary histories of hosts and parasites have an effect. Parasites had detrimental effects on the outcomes of species interactions near the equator, but more variable effects at temperate and higher latitudes. These results highlight the need to better understand how parasitism can affect the multitude of complex species interactions that structure biological communities.

Means, sample sizes, and zero-order correlations for key study variables.

About this dataset

Context

The dataset tabulates the North Adams population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of North Adams across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of North Adams was 467, a 0.21% decrease year-by-year from 2022. Previously, in 2022, North Adams population was 468, a decline of 0% compared to a population of 468 in 2021. Over the last 20 plus years, between 2000 and 2023, population of North Adams decreased by 35. In this period, the peak population was 502 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the North Adams is shown in this column.
• Year on Year Change: This column displays the change in North Adams population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 North Adams Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Adams town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Adams town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Adams town was 548, a 0.55% increase year-by-year from 2022. Previously, in 2022, Adams town population was 545, a decline of 0% compared to a population of 545 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Adams town increased by 92. In this period, the peak population was 548 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Adams town is shown in this column.
• Year on Year Change: This column displays the change in Adams town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Adams town Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Adams County population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Adams County across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Adams County was 28,746, a 0.80% increase year-by-year from 2022. Previously, in 2022, Adams County population was 28,517, a decline of 1.14% compared to a population of 28,846 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Adams County decreased by 5,469. In this period, the peak population was 34,215 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Adams County is shown in this column.
• Year on Year Change: This column displays the change in Adams County population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Adams County Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Adams township population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Adams township across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Adams township was 440, a 0% decrease year-by-year from 2022. Previously, in 2022, Adams township population was 440, a decline of 0.68% compared to a population of 443 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Adams township decreased by 42. In this period, the peak population was 492 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Adams township is shown in this column.
• Year on Year Change: This column displays the change in Adams township population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Adams township Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Adams township population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Adams township across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Adams township was 2,318, a 0.30% decrease year-by-year from 2022. Previously, in 2022, Adams township population was 2,325, an increase of 0.13% compared to a population of 2,322 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Adams township decreased by 180. In this period, the peak population was 2,528 in the year 2003. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Adams township is shown in this column.
• Year on Year Change: This column displays the change in Adams township population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Adams township Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Adams population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Adams across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Adams was 389, a 0.51% decrease year-by-year from 2021. Previously, in 2021, Adams population was 391, an increase of 0.51% compared to a population of 389 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Adams increased by 92. In this period, the peak population was 391 in the year 2021. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Adams is shown in this column.
• Year on Year Change: This column displays the change in Adams population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Adams Population by Year. You can refer the same here