This dataset contains genealogical information about migrant glassworkers and their families as well as individuals closely related to the glassworks that operated in Estonia from the 17th-19th century as well as glaziers from the 16th century. This dataset is intended for researchers as a basis to build upon and does not contain a complete list of all migrant glassworkers or their family members who have lived in Estonia. The data in this dataset were collected by Monika Reppo using both records kept and digitized at the National Archives of Estonia, Institute of the Estonian Language, and Estonian History Museum as part of a University of Tartu PhD project ‘Glass and its makers in post-medieval Estonia’ from 2017-2023. This dataset consists of a single table which is one of three datasets compiled as part of this PhD project.

TRIDIS (Tria Digita Scribunt) is a Handwriting Text Recognition model trained on semi-diplomatic transcriptions from medieval and Early Modern Manuscripts. It is suitable for work on documentary manuscripts, that is, manuscripts arising from legal, administrative, and memorial practices more commonly from the Late Middle Ages (13th century and onwards). It can also show good performance on documents from other domains, such as literature books, scholarly treatises and cartularies providing a versatile tool for historians and philologists in transforming and analyzing historical texts.

A paper presenting the first version of the model is available here: Sergio Torres Aguilar, Vincent Jolivet. Handwritten Text Recognition for Documentary Medieval Manuscripts. Journal of Data Mining and Digital Humanities. 2023. https://hal.science/hal-03892163

Transcriptions rules :

Since the majority of the training documents come from diplomatic editions, the transcriptions were normalized to contemporary reading standards, and abbreviations were expanded with the aim of facilitating a more fluid reading of the document.

The following rules were applied:

The abbreviations have been expanded, both those by suspension (facimꝰ ---> facimus) and by contraction (dñi --> domini). Likewise, those using conventional signs (⁊ --> et ; ꝓ --> pro) have been resolved.

The named entities (names of persons, places and institutions) have been capitalized. The beginning of a block of text as well as the original capitals used by the scribe are also capitalized.

The consonantal i and u characters have been transcribed as j and v in both French and Latin.

The punctuation marks used in the manuscript like: . or / or | have not been systematically transcribed as the transcription has been standardized with modern punctuation.

Corrections and words that appear cancelled in the manuscript have been transcribed surrounded by the sign $ at the beginning and at the end.

Versions :

Version 1 of the model was trained on charters and registers dataset from the Late Medieval period (12th-15th centuries). The training and evaluation involved 1855 pages, 120k lines of text, and almost 1M tokens, conducted using three freely available ground-truth corpora:

The Alcar-HOME database: https://zenodo.org/record/5600884

The e-NDP corpus: https://zenodo.org/record/7575693

The Himanis project: https://zenodo.org/record/5535306

Version 2 of the model has added new datasets from feudal books and legal proceedings (14th-16th centuries), incorporating an additional 115k lines and more than 1.2M tokens to the previous version using other corpora like:

Königsfelden Abbey corpus: https://zenodo.org/record/5179361

Monumenta Luxemburgensia.

Accuracy

TRIDIS was trained using a CNN+RNN+CTC architecture within the Kraken suite (https://kraken.re/). This final model operates in a multilingual environment (Latin, Old French, and Old Spanish) and is capable of recognizing several Latin script families (mostly Textualis and Cursiva) in documents produced circa 11th - 16th centuries. During evaluation, the model showed an accuracy of 93.1% on the validation set and a CER (Character Error Ratio) of about 0.11 to 0.15 on four external unseen datasets. Fine-tuning the model with 10 ground-truth pages can improve these results to a CER of between 0.06 to 0.10, respectively.

Other formats

The ground truth used for version 2 was also employed to train a Transformer HTR model that combines TrOCR as the encoder with a RoBERTa medieval model as the decoder. This model exhibits a slighly better performance in terms of CER metrics to the current TRIDIS version and shows an improved WER by about 25%. The model is available on the Hugging Face Hub: magistermilitum/tridis_HTR

The DECM Historical Gazetteer is a digital gazetteer of historical places in Mexico available in different formats and built from detailed research. This includes a 16th century version (The DECM 16th Century Gazetteer) with the toponyms mentioned in primary sources including the Relaciones Geográficas (1577-1585) and the Suma de Visitas de los Pueblos de la Nueva España (1548-1550), as well as information on the political, religious and administrative units of the Viceroyalty of New Spain. The gazetteer is composed of 71 main files with geographic information of colonial provinces, alcaldias, corregimientos, diocesis, among many others, as well as thousands of historical cities, towns, villages, and other places. This is integrated in an interoperable model containing thousands of historical locations with alternative spelling place-name variations. The dataset also includes 30 tables with additional historical information related to toponyms, languages, repositories, maps, etc.There are two versions of the DECM Historical Gazetteer: 1) The DECM Gazetteer, and 2) The DECM_16thC_Gazetteer.This is the 16th Century DECM Gazetteer. This version constitutes a subset of the main DECM Historical Gazetteer, including only the toponyms that were mentioned in the the Suma de Visita de los Pueblos and the RG reports, providing a precise window into the period when these were recorded (1548-1550/1577-1585). This dataset contains: the toponyms with coordinates, mentioned and disambiguated for each RG volume and the Suma; plus, the 30 geographical layers and 32 tables as explained above, as well as the DECM Gazetteer Registry.Content:1. DECM_16thCentury_Gazetteer_RGs: Contains 11 shapefiles (points) with historical geographies. They have been created identifying all the toponyms mentioned in the historical documents within the edited volumes of the Relaciones Geográficas de la Nueva España and Yucatán published by Acuña, De la Garza, and the Suma de Visita de los Pueblos by Del Paso y Troncoso (see sources used) disambiguated and atomized in a spatial database. Each shapefile include the following attributes: ID, Place name, Alternative Names, Modern Name, References, Location, Confidence Degree, ID of a related location, Relationship shared, Location Type, Type Thesaurus URL, Coord X, Coord Y, Time spam URL, Start date, End date. Every shapefile includes metadata.2. DECM_Additional_Information: Contains 49 shapefiles (points and polygons) with additional information on historical geographies. They have been created by digitising historical studies on 16th century Colonial History of Mexico. 3. DECM_Tables: Cointains 2 folders with additional tabular data with relevant information about 16th century colonial Mexico in two formats: csv and xlsx.4. Documents:

a) DECM_Gazetteer_Disambiguation_Percentage.xlsx - Excel document with the total amount of toponyms included in the primary and secondary sources used, the number and percentage of places disambiguated where X and Y coordinates were assigned or not found.

b) Souces_for_the_Disambiguation - Text file with the bibliographical references used to locate the place names in the DECM_Gazetteer_primarysources folder.

-This dataset is replaced by a new version, see below.-Land use plays an important role in the climate system (Feddema et al., 2005). Many ecosystem processes are directly or indirectly climate driven, and together with human driven land use changes, they determine how the land surface will evolve through time. To assess the effects of land cover changes on the climate system, models are required which are capable of simulating interactions between the involved components of the Earth system (land, atmosphere, ocean, and carbon cycle). Since driving forces for global environmental change differ among regions, a geographically (spatially) explicit modeling approach is called for, so that it can be incorporated in global and regional (climate and/or biophysical) change models in order to enhance our understanding of the underlying processes and thus improving future projections.Integrated records of the co-evolving human-environment system over millennia are needed to provide a basis for a deeper understanding of the present and for forecasting the future. This requires the major task of assembling and integrating regional and global historical, archaeological, and paleo-environmental records. Humans cannot predict the future. But, if we can adequately understand the past, we can use that understanding to influence our decisions and to create a better, more sustainable and desirable future.Some researchers suggest that mankind has shifted from living in the Holocene (~emergence of agriculture) into the Anthropocene (~humans capable of changing the Earth’ atmosphere) since the start of the Industrial Revolution. But in the light of the sheer size and magnitude of some historical land use changes (e.g. collapse of the Roman Empire in the 4th century, the depopulation of Europe due to the Black Plague in the 14th century and the aftermath of the colonization of the Americas in the 16th century), some believe that this point might have occurred earlier in time (Ruddiman, 2003; Kaplan et al., 2010). Many uncertainties still remain today and gaps in our knowledge of the Antiquity and its aftermath can only be improved by interdisciplinary research.HYDE presents (gridded) time series of population and land use for the last 12,000 years. It is an update (v 3.2) of the History Database of the Global Environment (HYDE) from Klein Goldewijk et al. (2011, 2013) with new quantitative estimates of the underlying demographic and agricultural developments for the Holocene. Date: 2016-11-28 The datasets consist of different time steps for each period: 10k BCE - 1 CE: 1000 yr, 1 - 1700 CE: 100 yr, 1700 - 2000 CE: 10 yr, 2000 - 2015 CE: 1 yr.A previous version has been made available from May until November 2016. This dataset was deposited and published in November 2016 in order to fix an incompleteness in the data.A newer version was deposited and published in September 2017.See the relations for the new version. The data is offered in ZIPfiles. Be aware that when extracted the total size of the dataset is over 200 GB.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Pollen. The data include parameters of pollen (population abundance) with a geographic location of China, Eastern Asia. The time period coverage is from Unavailable begin date to Unavailable end date in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Century: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 43(5.57%) households where the householder is under 25 years old, 211(27.33%) households with a householder aged between 25 and 44 years, 231(29.92%) households with a householder aged between 45 and 64 years, and 287(37.18%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 65 years and over bracket. This distribution hints at economic disparities within the town of Century, showcasing varying income levels among different age demographics.

Content

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

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Century median household income by age. You can refer the same here

Bp16.bnf.fr aims at the census of printed production in Paris throughout the 16th century. This bibliography was made from the manuscripts of Philippe Renouard (1862-1934) which are kept at the Reserve of rare books of the Bibliothèque nationale de France. It pays particular attention to the resolution of shared editions, the reporting of broadcasts and the identification of typographical material. Metadata specific to the description of old editions is used. The site is continuously updated and enriched with new notices; to date, more than 14,000 notices are displayed on the web. These notices published in bp16.bnf.fr are enriched with links to the authorities of the BnF and to the data.bnf.fr reference sheets: authors, titles of works and printers-library are searchable in the indexes. All descriptions of the editions kept at the BnF will soon be linked to the copies described in the BnF General Catalogue and, if applicable, to their digitisations in Gallica. Links to other digital libraries are also beginning to be reported. This site uses the tools of the Semantic Web. Finally, the site offers a suitable search environment in which it is possible to search full text, search by filter a specific criterion or browse the index authors, printers-library, works and editing dates.

About this dataset

Context

The dataset tabulates the Century household income by age. The dataset can be utilized to understand the age-based income distribution of Century income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Century, FL annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Century, FL household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Century income distribution by age. You can refer the same here

The DECM Historical Gazetteer is a digital gazetteer of historical places in Mexico available in different formats and built from detailed research. This includes a 16th century version (The DECM 16th Century Gazetteer) with the toponyms mentioned in primary sources including the Relaciones Geográficas (1577-1585) and the Suma de Visitas de los Pueblos de la Nueva España (1548-1550), as well as information on the political, religious and administrative units of the Viceroyalty of New Spain. The gazetteer is composed of 71 main files with geographic information of colonial provinces, alcaldias, corregimientos, diocesis, among many others, as well as thousands of historical cities, towns, villages, and other places. This is integrated in an interoperable model containing thousands of historical locations with alternative spelling place-name variations. The dataset also includes 30 tables with additional historical information related to toponyms, languages, repositories, maps, etc.There are two versions of the DECM Historical Gazetteer: 1) The DECM Gazetteer, and 2) The DECM_16thC_Gazetteer.This is the DECM Gazetteer and this version includes all the researched historical toponyms contained in primary and secondary sources. The information is organised by RG volume. It also includes layers and tables of historical information digitised and/or created by the project from secondary sources.This set is composed by GIS shapefiles & Linked Places format information with: a) all toponyms with coordinates, mentioned and disambiguated from the primary sources (13 volumes) and secondary sources (3 volumes); b) 30 geographical layers of additional historical information derived from secondary sources; c) 32 tables with other important historical information related to the RGs; and d) the DECM Gazetteer Registry.Content1. DECM_Gazetteer_primarysources: Contains 17 shapefiles (points and polygons) with historical geographies. They have been created from the indexes of the edited volumes of the Relaciones Geográficas de la Nueva España and Yucatán published by Acuña, De la Garza, and the Suma de Visita de los Pueblos by Del Paso y Troncoso (see sources used) disambiguated and atomized in a spatial database. Each shapefile include the following attributes: ID, Place name, Alternative Names, Modern Name, References, Location, Confidence Degree, ID of a related location, Relationship shared, Location Type, Type Thesaurus URL, Coord X, Coord Y, Time spam URL, Start date, End date. Every shapefile includes metadata.2. DECM_Gazetteer_secondarysources: Contains 4 shapefiles (points and polygons) with historical information mentioned and collected from 4 secondary sources. 3. DECM_Additional_Information: Contains 49 shapefiles (points and polygons) with additional information on historical geographies. They have been created by digitising historical studies on 16th century Colonial History of Mexico.4. DECM_Tables: Cointains 2 folders with additional tabular data with relevant information about 16th century colonial Mexico in two formats: csv and xlsx.5. Documents:

a) DECM_Gazetteer_Disambiguation_Percentage.xlsx - Excel document with the total amount of toponyms included in the primary and secondary sources used, the number and percentage of places disambiguated where X and Y coordinates were assigned or not found.

b) DECM_Gazetteer_Registry.xlsx & CSV: Excel and CSV files with a list of all the information of sources and attibutes included in DECM_Gazetteer_primarysources, DECM_Gazetteer_secondarysources, DECM_Additional_Information, and DECM_Tables.

c) Souces_for_the_Disambiguation - Text file with the bibliographical references used to locate the place names in the DECM_Gazetteer_primarysources folder.

A corpus containing all digitized French novels from the beginning of print (the first entry is from 1473) to the 18th century.

French novels of the period have been identified using the Y2 quote of the French National Library Catalog that has served to classify past and present collections of novels in France from 1730 to 1996. Combined use of digitized sources from Gallica, Google Books, Archive.org and other digital library made it possible to attain a high representativeness: 78% of the novels of the 1450-1600 and 68% of the novels of the 1600-1700 have been retrieved.

The corpus is part of a planned collection of French Fiction (1050-1920) that will also integrate Geste (a medieval corpus curated by Jean-Baptiste Camps) and Fictions littéraires de Gallica (a 1600-1950 corpus extracted from Gallica with Pierre-Carl Langlais, with a strong focus on the 19th century). While it aims to bridge the two pre-existing part of the collection, it is also a more ambitious experiment of systematic collection of existing digital sources.

The project remains very much a work-in-progress at this stage. Occasional errors in the metadata and the identification of the unique work are still possible. Besides, the identification of multi-volumes remain challenging in digital sources beyond Gallica.

The repository includes the following files:

The metadata of available and unavailable file for all novels identified in the 16th century (corpus_roman_metadata_16.tsv) and the 17th century (corpus_roman_metadata_17.tsv). All the editions have been temptatively assigned to a unique work (work_id) based on theo title, the author and additional metadata. This dataset includes both information on a specific digitized volume (volume_file, volume_title, volume_date, volume_edition_id) and on the earliest edition of the work recorded by the French national library (first_edition, first_edition_titre, first_edition_date), as well as the identification of the author (prenom_auteur, nom_auteur) and the complete list of all available edition (list_edition_bnf). When digitized files are not available for a given work, the information on the volume is replaced with a missing data mark (NA).An edition-based dataset was initially contemplated, but it turned out to be much harder than expected: the French National Catalog do not record all the available editions and runs of the period and it would have been necessary to check and create unique edition IDs for numerous Google Books volume.

The complete text of the novels when available (corpus_roman_16_text.tsv and corpus_roman_17_text.tsv). The use of contemporary OCR software on early modern text have long yielded poor results as words, typographies, and even letters were markedly different than the corpus theses software were trained on. Consequently, numerous volumes from Gallica have simply no OCR, as the results were below the quality requirement of the digital library. New historical OCR models will mmake it possible to create a reliable OCR on the entire corpus. The dataset includes all the text at the page-level whenever there is some text on the page. Page numbering is based on the absolute numbering of the file, not on the original numbering of the edition.

A classified dataset of 159 novels from the 17th century in four major genres of the period: chilvaric novel, love novel, historical novel and comic novel. The classification is based on an exceptional source of 1731, the catalog of novels from Nicolas Lenglet du Fresnoy (published as the second volume of De l'usage des romans). The classified dataset include both the text (as in corpus_roman_17_text.tsv) at the page-level and the lemmatization realized with a trained syntaxic model on 17th century French (https://github.com/e-ditiones/LEM17)

A classification model created with the classified dataset. This "Fresnoy" model has a high accuracy (93%) which can be parrtly attributed to overfitting (as there is a limited amount of novels per genre). The model can be reused with Tidysupervise, a small R extension to create supervised text models.

A striking feature of the Finnish language is that a subset of clause subjects behave in various ways like objects. For example, they may be marked with the partitive case in negated clauses, which "normal" subjects never do in Finnish, but objects always do. The subset of subjects in question are those of existential clauses (which broadly correspond to English "there..." clauses, e.g. "there is a book on the table") and possessive clauses (which are similar to existential clauses in Finnish: Finnish does not have a "to have"-verb, and the sentence "I have a book" is expressed like "at me is a book"). In the linked paper, I study whether this phenomenon (which is known as "fluid intransitivity" in recent typological literature) is present in the older Finnish language of the 16th and 17th century. I also want to see whether, if there is variation between subject-like and object-like subjects in clauses that correspond to modern Finnish existential and possessive clauses, I can determine which individual factors (such as, for example, negation) promote subject-like or object-like marking. For this purpose, I gathered a database of relevant clauses from two key Old Finnish texts: Agricola's 1548 New Testament translation, and the New Testament section of the 1642 first Finnish Bible (Biblia). I used the electronic versions published by the Institute for the Languages of Finland.

The relevant clauses in question are clauses which have an intransitive main verb and an indefinite subject NP. I could not select for existential/possessive clauses in a more direct manner as it is precisely in question whether there is an existential clause type in Old Finnish. The resulting corpus (899 clauses from Agricola, 959 clauses from the Biblia) broadly covers the equivalent of modern Finnish existential clauses. These clauses were then coded for variables studied (agreement and case-marking) as well as for several factor groups which I postulate may have affected the variables. The resulting code was then subject to a statistical analysis using the Goldvarb computer program. This analysis included a multivariate analysis which assigned a specific "weight" to individual factor groups.

For further clarification of methodology, etc. I refer to the linked paper. The file "coding instructions.txt" includes a brief explanation of the codes used in the database. The four spreadsheet files both include raw data (the clauses in question) and coding of variables and factor groups. NOTE: The data files have undergone slight corrections and therefore differ slightly from those underlying the 2016 paper.

Maps and plans from the 16th to the 20th century, relating to the whole of Île-de-France, and more specifically to the communes of Hauts-de-Seine. * * * * The collection of maps and plans kept in the Hauts-de-Seine Departmental Archives consists of more than 1,000 documents, from the 16th century to the present day. It allows to describe the territory of the communes of Hauts-de-Seine, Paris and its surroundings, even France, and in some cases, the whole world. In particular, it illustrates the historical stages of the evolution of the Paris region, and especially of the Hauts-de-Seine department, both from an urban and administrative point of view. Entered by extraordinary means (purchases, donations), most of these documents are originals. The dataset concerns two subseries of this collection: * 4Fi Maps and maps of the surroundings of Paris: Series of documents concerning the whole of the current Paris region, from the maps of the “Paris government” of the old regime to the maps of the RATP network, to the maps of the imperial hunts. 415 files online, relating to documents from the 16th to the 20th centuries. * 5Fi Maps and plans of the communes of the department of Hauts-de-Seine: Classified by commune, these documents are mostly dated from the 19th and 20th centuries. Many plans were drawn up by the Bridges and Chaussées towards the end of the 19th century and by the Ministry of Reconstruction and Urbanism after the Second World War. 330 files, relating to documents from the 18th to the 20th century. Additional sources available online: * Plans of the Napoleonic cadastre then renovated (19th-20th century) Other sources, not currently available online: * Underground quarry plans * Various plans in the archives of the State departments, the Department and their establishments (urban planning documents and authorisations, road plans, architectural plans, green cadastre, etc.)

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in New Era: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 4(2.25%) households where the householder is under 25 years old, 35(19.66%) households with a householder aged between 25 and 44 years, 64(35.96%) households with a householder aged between 45 and 64 years, and 75(42.13%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 65 years and over bracket. This distribution hints at economic disparities within the village of New Era, showcasing varying income levels among different age demographics.

Content

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

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 New Era median household income by age. You can refer the same here

About this dataset

Context

The dataset tabulates the New Era household income by age. The dataset can be utilized to understand the age-based income distribution of New Era income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• New Era, MI annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of New Era, MI household incomes: Comparative analysis across 16 income brackets

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/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of New Era income distribution by age. You can refer the same here

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in New Era: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 1(0.63%) households where the householder is under 25 years old, 31(19.50%) households with a householder aged between 25 and 44 years, 45(28.30%) households with a householder aged between 45 and 64 years, and 82(51.57%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 65 years and over bracket. This distribution hints at economic disparities within the village of New Era, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 New Era median household income by age. You can refer the same here

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