The "2014 Census of Open Access Repositories in Germany, Austria and Switzerland” (2014 Census) is a study on the green open access landscape conducted in the course of a project seminar at the Berlin School of Library and Information Science (BSLIS) at Humboldt-Universität zu Berlin. The 2014 Census not only succeeds the "2012 Census of Open Access Repositories in Germany"[1] but enhances it by adding an online survey to the qualitative analysis of the open access repository websites and the automatic validation of its metadata. Like in 2012 the 2014 Census gives insights into the development of open access repositories and current trends in repository design being of substantial use to open access repository operators.

This 2014 Census data set represents the data collected in three different ways:

qualitative analysis of the open access repository websites

automatic validation of the metadata via OAI-PMH using the DINI-Validator [2]

online survey of repository operators

As in 2012 [3] the data set is provided in XLSX as well as in CSV format. The columns represent the criteria and the rows represent the analyzed open access repositories. In the XLSX file the header row gives the definition of each criterion in English and German. In the CSV "content" file the header row is in English short terms. The respective English and German definition can be found in the CSV "readme" file.

[1] Vierkant, P. (2013). 2012 Census of Open Access Repositories in Germany: Turning Perceived Knowledge Into Sound Understanding. D-Lib Magazine, 19. http://dx.doi.org/10.1045/november2013-vierkant

[2] http://oanet.cms.hu-berlin.de/validator/pages/validation_dini.xhtml

[3] Vierkant, Paul; Voigt, Michaela; Dupski, Jens; David, Sammy; Lösch, Mathias (2013): 2012 Census of Open Access Repositories in Germany. figshare. http://dx.doi.org/10.6084/m9.figshare.677099

About this dataset

Context

The dataset tabulates the David City population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of David City.

Content

The dataset constitues the following two datasets across these two themes

• David City, NE Population Breakdown by Gender
• David City, NE Population Breakdown by Gender and Age

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/.

About this dataset

Context

The dataset tabulates the St. David population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of St. David. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 342 (62.41% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the St. David population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in St. David is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the St. David is shown in the following column.

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 St. David Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the David City population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for David City. The dataset can be utilized to understand the population distribution of David City by age. For example, using this dataset, we can identify the largest age group in David City.

Key observations

The largest age group in David City, NE was for the group of age 40 to 44 years years with a population of 250 (8.30%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in David City, NE was the 80 to 84 years years with a population of 45 (1.49%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the David City is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of David City total population. 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 David City Population by Age. You can refer the same here

A collection of DAOs, proposals and votes from aragon, daohaus, daostack, governor, realms and snapshot

About this dataset

Context

The dataset tabulates the St. David population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of St. David.

Content

The dataset constitues the following two datasets across these two themes

• St. David, IL Population Breakdown by Gender
• St. David, IL Population Breakdown by Gender and Age

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/.

Boundary Shapes for the US Census 'Places' 2021

These data comprise Census records relating to the Alaskan people's population demographics for the State of Alaskan Salmon and People (SASAP) Project. Decennial census data were originally extracted from IPUMS National Historic Geographic Information Systems website: https://data2.nhgis.org/main (Citation: Steven Manson, Jonathan Schroeder, David Van Riper, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 12.0 [Database]. Minneapolis: University of Minnesota. 2017. http://doi.org/10.18128/D050.V12.0). A number of relevant tables of basic demographics on age and race, household income and poverty levels, and labor force participation were extracted. These particular variables were selected as part of an effort to understand and potentially quantify various dimensions of well-being in Alaskan communities. The file "censusdata_master.csv" is a consolidation of all 21 other data files in the package. For detailed information on how the datasets vary over different years, view the file "readme.docx" available in this data package. The included .Rmd file is a script which combines the 21 files by year into a single file (censusdata_master.csv). It also cleans up place names (including typographical errors) and uses the USGS place names dataset and the SASAP regions dataset to assign latitude and longitude values and region values to each place in the dataset. Note that some places were not assigned a region or location because they do not fit well into the regional framework. Considerable heterogeneity exists between census surveys each year. While we have attempted to combine these datasets in a way that makes sense, there may be some discrepancies or unexpected values. The RMarkdown document SASAPWebsiteGraphicsCensus.Rmd is used to generate a variety of figures using these data, including the additional file Chignik_population.png. An additional set of 25 figures showing regional trends in population and income metrics are also included.

An appreciation of historical landuse and its effects is crucial when interpreting the structure, composition, and spatial characteristics of modern forests. The Harvard Forest has compiled many different historical data sources in an ongoing effort to understand how anthropogenic disturbances have shaped our modern landscapes. Estimates of town land use and land cover were gathered from a variety of sources, including tax valuations (1801-1860) and state agricultural census records (1865-1905). Data prior to 1801 rarely cover the entire state and are excluded from these datasets. Data on forest structure are available for several time periods, including 1885 and 1895 (Agricultural Censuses) and 1916-1920s (State Forester’s reports).

Plan Description: This is an adaptation of the People’s Bloc plan, #012.The main difference is that this plan gives some diversity of representation to the North County area, which is very large, and from which the commission heard several requests for diversity of representation. The part of North County that District 3 here includes is largely Latinx and working class, in kinship with much of the population in the rest of that District.Plan Objectives:This is an adaptation of the People’s Bloc plan, #012.The main difference is that this plan gives some diversity of representation to the North County area, which is very large, and from which the commission heard several requests for diversity of representation, particularly from unincorporated areas contained within or east-south east of Palmdale. [I have a friend in the electoral reform community who lives in a different California county, in the foothills of the Sierra Nevada. A sizeable minority of residents in the area shares her political perspective. But because of where they live, and because districts are drawn to be compact, those residents do not get representation to their liking in the State Legislature or U.S. House of Representatives. My friend often complains about that and longs to have at least one district that stretches from elsewhere to include her residence or a nearby sympatico area, and that might elect a representative to her liking. Just like with North Los Angeles County, a little diversity in representation of the Sierra foothills could be a good thing.] Now that LA County CRC software users can no longer assign official U.S. Census areas (blocks, block groups, or tracts) to districts, and are offered “RDUs” (made-up ReDistricting Units) instead, this is the best I could do. RDU3096 gets in the way of better connections from North County to the southern part of the county. And we would need to use census blocks to “break through” to the unincorporated “hole” in the incorporated city of Palmdale, from which the commission heard at least one request for separate representation (rescue from the city?), without dividing the incorporated city’s population. (RDU3006 is too big to allow that.) The software/database bait-and-switch to RDUs only from the demo version also makes it necessary to take a small part of the populated area of the City of Santa Clarita to draw District 3 here.The part of North County that District 3 here includes is, as the commission heard, largely Latinx and working class, in kinship with much of the population in the rest of District 3 here, as a community of interest. At this point, it would not be practicable for this district to be geographically compact and keep that community connected.

The dataset includes data from various Decentralized Autonomous Organizations (DAOs) platforms, namely Aragon, DAOHaus, DAOstack, Realms, Snapshot and Tally. DAOs are a new form of self-governed online communities deployed in the blockchain. DAO members typically use governance tokens to participate in the DAO decision-making process, often through a voting system where members submit proposals and vote on them.

The description of the methods used for the generation of data, for processing it and the quality-assurance procedures performed on the data can be found here:https://doi.org/10.1145/3589335.3651481

Recommended citation for this dataset:Peña-Calvin, A., Arroyo, J., Schwartz, A., & Hassan, S. (2024). Concentration of Power and Participation in Online Governance: the Ecosystem of Decentralized Autonomous Organizations. Companion Proceedings of the ACM Web Conference, 13–17, 2024, Singapore, doi: https://doi.org/10.1145/3589335.3651481

The dataset comprises three CSV files: deployments.csv, proposals.csv, and votes.csv, each containing essential information regarding DAOs deployments, theirproposals, and the corresponding votes.

The file deployments.csv provides insights into the general aspects of DAO deployments, including the platform it is deployed in, the number of proposals, unique voters, votes cast, and estimated voting power.

The proposals.csv file contains comprehensive information about all proposals associated with the deployments, including their date, the number of votes they received, and the total voting power voters employed on that proposal.

In votes.csv, data regarding the votes cast for the deployment proposals is recorded. It includes the voter's blockchain address, the vote's weight in voting power, and the day it was cast.

This is the final dataset and R script used for the analysis for the paper titled All Ridership Is Local: Accessibility, Competition, and Stop-Level Determinants of Daily Bus Boardings in Portland, Oregon. The .csv and .RDS files contain the same final dataset with all the variables used in the final models.

These data comprise Census records relating to the Alaskan people's population demographics for the State of Alaskan Salmon and People (SASAP) Project. Decennial census data were originally extracted from IPUMS National Historic Geographic Information Systems website: https://data2.nhgis.org/main(Citation: Steven Manson, Jonathan Schroeder, David Van Riper, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 12.0 [Database]. Minneapolis: University of Minnesota. 2017. http://doi.org/10.18128/D050.V12.0). A number of relevant tables of basic demographics on age and race, household income and poverty levels, and labor force participation were extracted.

  These particular variables were selected as part of an effort to understand and potentially quantify various dimensions of well-being in Alaskan communities.
  The file "censusdata_master.csv" is a consolidation of all 21 other data files in the package. For detailed information on how the datasets vary over different years, view the file "readme.docx" available in this data package.

  The included .Rmd file is a script which combines the 21 files by year into a single file (censusdata_master.csv). It also cleans up place names (including typographical errors) and uses the
  USGS place names dataset and the SASAP regions dataset to assign latitude and longitude values and region values to each place in the dataset. Note that some places were not assigned a region or
  location because they do not fit well into the regional framework.

  Considerable heterogeneity exists between census surveys each year. While we have attempted to combine these datasets in a way that makes sense, there may be some discrepancies or unexpected values.
  Please send a description of any unusual values to the dataset contact.

The main archival source of MapRom database is the “Census of the population or Statistics of the Principalities (Wallachia and Moldova) of 1838”, ANIC, Fond Catagrafii, part I, Inventory number 501, volumes numbered I/8 to I/107 from the Historical National Archives, Bucharest, mostly unpublished and now digitized and automatized for the first time, in regard to Romani population. This Census was conducted by the Interior Ministry in February 1838. It is the first preserved Romanian modern census of population and dwellings, which introduced new techniques of reviewing, such as the nominal lists and the great number (24) of demographic variables, including ethnicity. The unit of observation was the “household”, and the scope was to obtain detailed information about: 1) the settled population by age group, and sex; 2) the number of households and their structure; 3) the number of residential buildings and their distribution according to the material from which they are built; 4) population distribution according to their participation in economic activity; 5) distribution of population by skills and occupations. Based on this information, the MapRom database presents: statistics of the Roma households per village, number of Roms per village, number of different Romani ethnic sub-groups (Lăieș, Vătraș, Rudar, Zlătar, etc), average Romani household size per village, age distribution, sex distribution, number of Roms per skill, geographic distribution of the Roms (in uplands and lowlands), cultivated land area by Roms per village, etc. We found insignificant number of free Roms, rest of them were slaves. For more than 50% of them, we have reconstructed, from other (unpublished) sources the name of the owners (private noblemen, or Monasteries and churches). We searched for other statistical documents to complete our data from 1838 Census, such as the Statistics of the Turkish Gypsies (1833, manuscript ANIC, Vornicia temnitelor, file 354/1833), Statistics of Boyar Gypsy slaves (1832, manuscript ANIC, Diplomatice, dos. 147/1832) and Statistics of Monastery Gypsy slaves (1844, manuscript ANIC, Logofeția Pricinilor Bisericești, dos. 25/1844). etc. When we compared these statistics, we found major discrepancies leading to the important source critical conclusion that the 1838 census concerns mostly the permanently settled Gypsies and included few of the nomadic people. Further examination showed that the number of nomadic Gypsies was relatively small. We also found documents that indicated that Gypsies of the Muslim faith were not either registered in the 1838 census. Over time the nominal lists of the 1838 Census for four counties (Ialomiţa, Gorj, Mededinţi and Vâlcea) as well as some two rural sub-districts and two towns has been lost, but that for 14 Wallachian counties has been preserved and entered in MapRom. The five volumes of nominal material for the capital city Bucharest (with a very large and multi-ethnic population) was not researched in this project as the census there was complexly different from that of the rural provinces in form and execution, but it is hoped that it can be researched at a later date. We estimate that MapRom gathers information on between two-thirds and three-fourths of Wallachia’s Romani population.

Assumptions: Costs were obtained in US Dollars (USD), British Pounds (GBP), and Gambian Dalasi (GMD). GMD and GBP costs were converted to USD using a historic currency conversion of an average of 366 days from the 01st January 2009 to the 1st of January 2010 (http://www.oanda.com/currency/historical-rates/). For this time period, 1GMD = 0.0377 USD, and 1GBP = 1.5665 USD.For training, the following assumptions were made: Two days’ training.Training was done at the Regional Eye Care Centre, so there are no facility costs.Training was done by the manager of the NECP, who has no per diem.For census taking, the following assumptions were made: One NECP census takers on a motorcycle per EAOne census taker can census 1 EA/day (based on PRET)The census taker would not do a first separate trip to make a household head listCosts were obtained in US Dollars (USD), British Pounds (GBP), and Gambian Dalasi (GMD). GMD and GBP costs were converted to USD using a historic currency conversion of an average of 366 days from the 01st January 2009 to the 1st of January 2010 (http://www.oanda.com/currency/historical-rates/). For this time period, 1GMD = 0.0377 USD, and 1GBP = 1.5665 USD.For training, the following assumptions were made: Two days’ training.Training was done at the Regional Eye Care Centre, so there are no facility costs.Training was done by the manager of the NECP, who has no per diem.Two days’ training.Training was done at the Regional Eye Care Centre, so there are no facility costs.Training was done by the manager of the NECP, who has no per diem.For census taking, the following assumptions were made: One NECP census takers on a motorcycle per EAOne census taker can census 1 EA/day (based on PRET)The census taker would not do a first separate trip to make a household head listOne NECP census takers on a motorcycle per EAOne census taker can census 1 EA/day (based on PRET)The census taker would not do a first separate trip to make a household head listCensus costs by item.

These data show languages spoken in the household for people over the age of 5 in Alaska, in addition to the total population, by community. These data come from census surveys, both from the American Community Survey and the decennial census Population and language use data were originally extracted from IPUMS National Historic Geographic Information Systems website: https://data2.nhgis.org/main (Citation: Steven Manson, Jonathan Schroeder, David Van Riper, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 12.0 [Database]. Minneapolis: University of Minnesota. 2017. http://doi.org/10.18128/D050.V12.0 ). The file "household_language.csv" is a consolidation of a number of tables downloaded from this system (see methods for more information). The "language.Rmd" file is a script which combines the files by year into a single file. It also cleans up place names (including typographical errors) and uses the USGS place names dataset and the SASAP regions dataset to assign latitude and longitude values and region values to each place in the dataset. Additionally, the "language_vis.Rmd" file is a script that uses this data to visualize Native language use by community, displayed in the "language_vis.html" file.

Age and gender distribution of the survey sample and census population.

The FORCIS (Foraminifera Response to Climatic Stress) database is a synthesis grouping datasets on living planktonic foraminifera. We assembled foraminiferal diversity and distribution data in the global oceans from 1910 until 2018, curating published and unpublished datasets. This database includes data collected using plankton tows, continuous plankton recorder, sediment traps and plankton pump from the global ocean.

The FORCIS database version 01 is composed of 5 files (“.csv” format). All data coming from different sampling devices were put into separate “.csv” files. Only the data of the CPR from the Southern Hemisphere have been separated from the Northern Hemisphere CPR data as the data structure is not the same (species counts resolved vs. binned total counts, respectively).

Apart from the file of CPR data from the Northern Hemisphere that contains only metadata and binned total counts, all the remaining four files contain 4 blocks:

• Block 1: metadata (from column 1 to 71)

• Block 2: original counts (from column 72 to 274)

• Block 3: generated counts based on the validated taxonomy (from column 275 to 331). We added “_VT” to each species name to distinguish it from other taxonomy levels. E.g. “g_bulloides” became “g_bulloides_VT”. The number of species counted per subsample is also reported in the column “number_of_species_counted_VT”

• Block 4: generated counts based on the lumped taxonomy (from column 332 to 379). In this case, we added “_LT” to each species name. E.g. “n_dutertrei” became “n_dutertrei_VT”. We also calculated the number of species counted per subsample and reported it in the column “number_of_species_counted_LT”

Foraminifera abundance data counts are reported in different categories in the blocks 1,2 and 3 and described in the table below:

For more details about the FORCIS database column description, please check the data descriptor paper Chaabane et al. (2023) (https://doi.org/10.1038/s41597-023-02264-2).

The database is kept open for any new entries and the updated version will be released in csv format. The labels of updated versions of the released “.csv” files will contain the date of their publication and versioning number.

Census/projection-disaggregated gridded population datasets, adjusted to match the corresponding UNPD 2020 estimates, for 51 countries across sub-Saharan Africa using building footprints. Source of building footprints "Ecopia Vector Maps Powered by Maxar Satellite Imagery" © 2020.

The widespread existence of dominance hierarchies has been a central puzzle in social evolution, yet we lack a framework for synthesizing the vast empirical data on hierarchy structure in animal groups. We applied network motif analysis to compare the structures of dominance networks from data published over the past 80 years. Overall patterns of dominance relations, including some aspects of non-interactions, were strikingly similar across disparate group types. For example, nearly all groups exhibited high frequencies of transitive triads, whereas cycles were very rare. Moreover, pass-along triads were rare, and double-dominant triads were common in most groups. These patterns did not vary in any systematic way across taxa, study settings (captive or wild) or group size. Two factors significantly affected network motif structure: the proportion of dyads that were observed to interact and the interaction rates of the top-ranked individuals. Thus, study design (i.e. how many interactions were observed) and the behaviour of key individuals in the group could explain much of the variations we see in social hierarchies across animals. Our findings confirm the ubiquity of dominance hierarchies across all animal systems, and demonstrate that network analysis provides new avenues for comparative analyses of social hierarchies.