This document summarizes a study that analyzed descriptive metadata from digitized historical newspaper collections. Key points:
- Over 850,000 newspaper pages from 6 French titles spanning 1814-1945 were analyzed. Optical layout recognition was used to extract descriptive metadata.
- Data mining and visualization techniques were applied to the metadata to gain insights into the history of newspapers and press. Trends in layout, illustrations, word count and more over time were examined.
- The analyses revealed information like periods of structural changes in newspapers, the impact of events like wars on content volume, and outliers that indicated special issues like illustrated supplements. The automated analyses of descriptive metadata provided new perspectives on historical newspaper collections.
Pablo de Pedraza: Labor market matching, economic cycle and online vacanciesTextkernel
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Pablo de Pedraza's presentation at Textkernel's Conference Intelligent Machines and the Future of Recruitment on 2 June 2016.
The number of job openings, or vacancies, is an important indicator of the state of the economy and the labour market. They are extensively used by institutions and in academic papers to calculate the Beveridge Curve or estimate the matching function, center pieces of macroeconomic models studying labor markets. Vacancies can be measured using administrative registers, surveys to employers, advertisements in printed press or using online advertising.
This presentation is divided into two sections. In the first one we study the Dutch Beveridge curve and the matching function using the number of vacancies inferred from a survey to employers conducted by the Dutch Central Bureau of Statistics (CBS) from 1997 until the end of 2014. We obtain conclusion about matching process before and after the Great Recession.
In the second section we compare number of vacancies inferred from CBS vacancy data with the number of vacancies posted online. According to CBS data, the number of vacancies increases during positive shocks and goes down during negative ones. We can observe the number of web vacancies posted online from 2006 until today and compare them with CBS data during a complete economic cycle.
Results show a positive time trend in the number of online vacancies and negative time trend in the number of vacancies inferred from a survey. We show that both series reflect very similar economic reality once we account for both trends. We settle our future research lines focusing on exploring the sources behind both trends and how they compare across sectors.
What is the major power linking statistics & data miningIJDKP
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In the recent years, numerous scientific research studies which stand for the intersecting disciplines
between statistics and data mining (DM) are obtained [17, 18, 19, 24, 27, 30, 35]. This paper is devoted to answer
the titled suggested question which is based on five reply trends, the 1st trend based on an updated
historical vision for each of statistics and DM. The 2nd trend is concerned with modern theoretical
significant reply between statistics and DM. The major power linking statistics and DM is established in
the 3rd trend. Lastly, the 4th trend represents a significant comparison between statistics & DM. A
conceptual classification about Statistical Data Mining (SDM) process in Egypt will be represented in the
5th reply trend. Finally, the conclusion and the future work are represented.
Abstractâ Agriculture is the basic need of human being to survive. Increase in human population, increases the food production. Largest areas are under rice cultivation. Rice plants were prone to attack by insect and pest. So, for its survival use of pesticide is necessary, but this had lead risk behaviour among rice farmers. The study was based on the farmers of Bargarh and Sundargarh District of Odisha regarding pesticide usage condition .100 farmers were interviewed from both the district, using questionnaire methods from February to April 2015. A questionnaire survey on personal history regarding agricultural labour, pesticide use and health history was conducted. Descriptive statistics was used for analysis of quantitative data. The most frequently used pesticides included organophosphates, carbamates. 2-3 times pesticide was applied after 15 days, after 1 month and also before the production time. Demographic data shows 87 respondents were male farmers rests were female farmers out of 100 respondents. Only 85 respondents were using sprayer for spraying pesticides out of 100 but 36 respondents were only using protective covers. 12 farmers only follow the instruction given on the pesticide container. 33 respondents have the knowledge of colour coding present in the pesticide bottle. Health symptom showed less frequently, in farmers using protective covers. Out of 100 respondents 58 had skin contact, 12 respondents suffer from eye irritation, and 28 respondents feel drowsiness after strong smell of pesticides while 31 farmers suffer from headache. Major factors of pesticide poisoning are due to lack of attention to safety precautions and lack of training before using of pesticide. So, training programme is necessary to improve safer pesticide behaviours, create more awareness among the farmers and also introduction of using bio pesticide instead of using pesticide.
Pablo de Pedraza: Labor market matching, economic cycle and online vacanciesTextkernel
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Pablo de Pedraza's presentation at Textkernel's Conference Intelligent Machines and the Future of Recruitment on 2 June 2016.
The number of job openings, or vacancies, is an important indicator of the state of the economy and the labour market. They are extensively used by institutions and in academic papers to calculate the Beveridge Curve or estimate the matching function, center pieces of macroeconomic models studying labor markets. Vacancies can be measured using administrative registers, surveys to employers, advertisements in printed press or using online advertising.
This presentation is divided into two sections. In the first one we study the Dutch Beveridge curve and the matching function using the number of vacancies inferred from a survey to employers conducted by the Dutch Central Bureau of Statistics (CBS) from 1997 until the end of 2014. We obtain conclusion about matching process before and after the Great Recession.
In the second section we compare number of vacancies inferred from CBS vacancy data with the number of vacancies posted online. According to CBS data, the number of vacancies increases during positive shocks and goes down during negative ones. We can observe the number of web vacancies posted online from 2006 until today and compare them with CBS data during a complete economic cycle.
Results show a positive time trend in the number of online vacancies and negative time trend in the number of vacancies inferred from a survey. We show that both series reflect very similar economic reality once we account for both trends. We settle our future research lines focusing on exploring the sources behind both trends and how they compare across sectors.
What is the major power linking statistics & data miningIJDKP
Â
In the recent years, numerous scientific research studies which stand for the intersecting disciplines
between statistics and data mining (DM) are obtained [17, 18, 19, 24, 27, 30, 35]. This paper is devoted to answer
the titled suggested question which is based on five reply trends, the 1st trend based on an updated
historical vision for each of statistics and DM. The 2nd trend is concerned with modern theoretical
significant reply between statistics and DM. The major power linking statistics and DM is established in
the 3rd trend. Lastly, the 4th trend represents a significant comparison between statistics & DM. A
conceptual classification about Statistical Data Mining (SDM) process in Egypt will be represented in the
5th reply trend. Finally, the conclusion and the future work are represented.
Abstractâ Agriculture is the basic need of human being to survive. Increase in human population, increases the food production. Largest areas are under rice cultivation. Rice plants were prone to attack by insect and pest. So, for its survival use of pesticide is necessary, but this had lead risk behaviour among rice farmers. The study was based on the farmers of Bargarh and Sundargarh District of Odisha regarding pesticide usage condition .100 farmers were interviewed from both the district, using questionnaire methods from February to April 2015. A questionnaire survey on personal history regarding agricultural labour, pesticide use and health history was conducted. Descriptive statistics was used for analysis of quantitative data. The most frequently used pesticides included organophosphates, carbamates. 2-3 times pesticide was applied after 15 days, after 1 month and also before the production time. Demographic data shows 87 respondents were male farmers rests were female farmers out of 100 respondents. Only 85 respondents were using sprayer for spraying pesticides out of 100 but 36 respondents were only using protective covers. 12 farmers only follow the instruction given on the pesticide container. 33 respondents have the knowledge of colour coding present in the pesticide bottle. Health symptom showed less frequently, in farmers using protective covers. Out of 100 respondents 58 had skin contact, 12 respondents suffer from eye irritation, and 28 respondents feel drowsiness after strong smell of pesticides while 31 farmers suffer from headache. Major factors of pesticide poisoning are due to lack of attention to safety precautions and lack of training before using of pesticide. So, training programme is necessary to improve safer pesticide behaviours, create more awareness among the farmers and also introduction of using bio pesticide instead of using pesticide.
Paper presented in the research methodology workshop. The error if any is regretted and suggestions most welcome. Good for students and researchers alike, enjoy.
Paper presented in the research methodology workshop. The error if any is regretted and suggestions most welcome. Good for students and researchers alike, enjoy.
Creating and Using a Gannt Chart A Gantt chart is a t.docxwillcoxjanay
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Creating and Using a Gannt Chart
A Gantt chart is a type of bar chart that illustrates a project schedule. Gantt charts illustrate the start and finish dates of the terminal elements
and summary elements of a project and the dependency relationships between activities. Gantt charts can be used to show current schedule
status using percent-complete shadings and a vertical "TODAY" line.
The first known Gantt chart was developed in 1896 by Karol Adamiecki, who called it a harmonogram. Adamiecki did not publish his chart until
1931, however, and then only in Polish. The chart is commonly known after Henry Gantt (1861â1919), who designed his chart sometime
between 1910 and 1915.
Although now regarded as a common charting technique, Gantt charts were considered revolutionary when they were introduced. In
recognition of Henry Ganttâs contributions, the Henry Laurence Gantt prize is awarded for distinguished achievement in management and in
community service. This chart is used also in Information Technology to represent data that has been collected. (first three paragraphs appear in
Wikipedia Gantt chart entry, citing these three sources:
H.L. Gantt, Work, Wages and Profit, published by The Engineering Magazine, New York, 1910; republished as Work, Wages and
Profits, Easton, Pennsylvania, Hive Publishing Company, 1974, ISBN 0879600489.
Blokdijk, Gerard (2007). Project Management 100 Success Secrets. Lulu.com. p. 76. ISBN HYPERLINK
"http://en.wikipedia.org/wiki/Special:BookSources/0980459907" 0980459907.
http://books.google.com/books?id=dgB-
QWHlnrUC&pg=PA76&dq=Adamiecki+Gantt&as_brr=3&sig=Jp- mgVODNRJpxqBRM1PYJbs7mOU.
Peter W. G. Morris, The Management of Projects, Thomas Telford, 1994, ISBN 0727725939, Google Print, p.18)
There are two easy ways to create a Gantt chart to incorporate in the status report for a project in this class: (1) use free software, or (2) create a
table in MS Word or Corel WordPerfect.
http://en.wikipedia.org/wiki/Special:BookSources/0879600489
http://en.wikipedia.org/wiki/International_Standard_Book_Number
http://books.google.com/books?id=dgB-QWHlnrUC&pg=PA76&dq=Adamiecki+Gantt&as_brr=3&sig=Jp-mgVODNRJpxqBRM1PYJbs7mOU
http://books.google.com/books?id=dgB-QWHlnrUC&pg=PA76&dq=Adamiecki+Gantt&as_brr=3&sig=Jp-mgVODNRJpxqBRM1PYJbs7mOU
http://books.google.com/books?id=dgB-QWHlnrUC&pg=PA76&dq=Adamiecki+Gantt&as_brr=3&sig=Jp-mgVODNRJpxqBRM1PYJbs7mOU
http://en.wikipedia.org/wiki/Special:BookSources/0727725939
http://books.google.com/books?id=5ekyoWaeZ1UC&pg=PA18-IA7&dq=Adamiecki+Gantt&as_brr=3&sig=xe_RAipoqlvhnu0xLkIsxx-8OAQ
CREATING A GANTT CHART USING FREE SOFTWARE:
One free Gantt chart creator can be found on http://www.ganttproject.biz/ and an example of its output appears below:
This chart depicts milestones in completing âThe Johnson Genealogy Project.â The goal of the project is âto provide a written history and genealogy
of the Johnso.
Information Art is a diverse range of human activities in creating visual, auditory or performing artifacts (artworks), expressing the author's imaginative, conceptual ideas, or technical skill, intended to be appreciated for their beauty or emotional power. Other activities related to the production of works of art include the criticism of art, the study of the history of art, and the aesthetic dissemination[clarification needed] of art.
7th BL Labs Symposium (2019): 08_An update on the âLiving with machinesâ projectlabsbl
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Mia Ridge, Digital Curator and Co-Investigator for Living with machines, British Library
The 'Living with machines' project is a collaboration between the British Library and the Alan Turing Institute for Data Science and Artificial Intelligence.
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Event Report - SAP Sapphire 2024 Orlando - lots of innovation and old challengesHolger Mueller
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Holger Mueller of Constellation Research shares his key takeaways from SAP's Sapphire confernece, held in Orlando, June 3rd till 5th 2024, in the Orange Convention Center.
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Cracking the Workplace Discipline Code Main.pptxWorkforce Group
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Cultivating and maintaining discipline within teams is a critical differentiator for successful organisations.
Forward-thinking leaders and business managers understand the impact that discipline has on organisational success. A disciplined workforce operates with clarity, focus, and a shared understanding of expectations, ultimately driving better results, optimising productivity, and facilitating seamless collaboration.
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Digital Transformation and IT Strategy Toolkit and TemplatesAurelien Domont, MBA
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Building Your Employer Brand with Social MediaLuanWise
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Presented at The Global HR Summit, 6th June 2024
In this keynote, Luan Wise will provide invaluable insights to elevate your employer brand on social media platforms including LinkedIn, Facebook, Instagram, X (formerly Twitter) and TikTok. You'll learn how compelling content can authentically showcase your company culture, values, and employee experiences to support your talent acquisition and retention objectives. Additionally, you'll understand the power of employee advocacy to amplify reach and engagement â helping to position your organization as an employer of choice in today's competitive talent landscape.
Company Valuation webinar series - Tuesday, 4 June 2024FelixPerez547899
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This session provided an update as to the latest valuation data in the UK and then delved into a discussion on the upcoming election and the impacts on valuation. We finished, as always with a Q&A
In the Adani-Hindenburg case, what is SEBI investigating.pptxAdani case
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Adani SEBI investigation revealed that the latter had sought information from five foreign jurisdictions concerning the holdings of the firmâs foreign portfolio investors (FPIs) in relation to the alleged violations of the MPS Regulations. Nevertheless, the economic interest of the twelve FPIs based in tax haven jurisdictions still needs to be determined. The Adani Group firms classed these FPIs as public shareholders. According to Hindenburg, FPIs were used to get around regulatory standards.
Implicitly or explicitly all competing businesses employ a strategy to select a mix
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2. not encourages some caution? The criterion of the volume is irrelevant, if we believe Viktor
Mayer-Schoenberger and Kenneth Cukier : â(âŠ) big data refers to things one can do at a
large scale that cannot be done at a smaller one, to extract new insights or create new forms
of value (âŠ)â [1]. On a large scale, but set against the activity (âmy big data is not your big
dataâ [2]), with methods different from those satisfying the nominal business needs, and with
the aim to âcreate something newâ: new links (auteur, lieu, date, etc.) are built on top of
catalogs (OPAC) [3]; libraries managment can be backed by the analysis of attendance
and reading data [4]; a history of newspaper front pages can be written on data extracted from
digital libraries [5],[6].
E.g., does it make sense to data mine descriptive metadata of the daily newspapers digitized
during the Europeana Newspapers project [7]? We attempt to answer this hypothesis by first
presenting the process of creating a set of descriptive metadata (§II); then some methods of
analysis and interpretation of data (§III); and finally data quality issues (§ IV).
2 CREATING NEW DATA
2.1 The Europeana Newspapers dataset
Six national and regional newspapers (1814-1945, 880,000 pages, 150,000 issues) of BnF
collections are part of the data set OLRâed (Optical Layout Recognition) by the project
Europeana Newspapers. The OLR refinement consists of the description of the structure of
each issue and article (spatial extent, title and subtitle, classification of content types) using
the METS/ALTO formats [8].
2.2 From the digital documents to the derived data
From each digital document is derived a set of bibliographical and descriptive metadata
relating to content (date of publication, number of pages, articles, words, illustrations, etc.).
Shell and XSLT scripts called with Xalan-Java (Fig. 1) are used to extract some metadata
from METS manifest (e.g. number of articles) or OCR files (e.g. number of words). The
complete set of derived data contains about 4,500,000 atomic metadata values.
Fig. 1. Derived data production process
This process produces XML, JSON and CSV data, the latter then imported into a spreadsheet
template to produce other data (e.g. means), or to compile data over a time period (year), and
finally to generate interactive web graphs (with Highcharts).
More effective technical solutions exist: ETL frameworks, APIs to access content [6],[9],
high level languages with SAX/DOM API, XML databases, statistical environments like R,
etc. but a choice was made to only apply common library formats and tools (XML, XSLT,
spreadsheet) and to reuse an in-house toolbox dedicated to the statistical analysis of OCR
contents.
2
3. 3 WHEN THE DATA TALK
3.1 Producing knowledge
Some data describe a reality of which the analyst has prior knowledge or intuition. This is the
case of statistical information helping to pilot digitization or curation actions. The data
set could then be a representative sample of the collection, because the information sought
are mostly statistical measures.
âą Digitization programs: What is the density in articles of these newspapers (Fig. 2)? What
is the potential impact on OLR processing costs?
Fig. 2. Average number of articles per issue
âą Text correction: What is the average density in words of these newspapers (Fig. 3)? What
text correction efforts will be required?
Fig. 3. Average number of words per page
âą Image bank: What titles contain illustrations (Fig. 4)? What is the total number of images
one can expect?
3
9. 4 DATA QA
The quality of derived data affects the validity of the analysis and interpretation [4],[13].
Irregular data in nature or discontinuous in time may introduce bias. A qualitative assessment
should be conducted prior to any interpretative analysis.
This press corpus is characterized by the relative homogeneity of its shape over time, which
induces consistency and constant granularity of the derived metadata (issue, page, article...).
Moreover, its large size and the option to apply the analysis to the entire data set and not a
subset of it also guarantees its representativity [14].
The data itself can sometimes contribute to their own QA. A calendar display of available
data for a title (JDPL, Fig. 13) shows rare missing issues, which suggests that the digital
collection is representative of the reality [15].
Fig. 13. JDPL missing issues (1814-1944)
Or, before starting a study on daily stock market quotes based on the content typed âtableâ
[11], one can empirically validate this hypothesis by the sudden inflections recorded in 1914
and 1939 for all titles (Fig. 14), being known and established the historical fact of the virtual
halt of trading during the two World wars.
Fig. 14. Average number of tables per issue
9
10. CONCLUSION
This experiment showed that even meaningless descriptive metadata can give new insights
into a collection through the use of basic data mining methods and tools. This surprising
finding is explained by the target corpus, daily press, ideal subject for OLR structural
enrichment and hence the production of consistent metadata over a large period of time.
Its results could be followed up in various ways:
âą Apply the same data mining process to the other Europeana Newspapers OLRâed datasets
to expand the scope of the analysis to the entire European press and to the BnF press
digitization program, which also uses OLR [16].
âą Experiment with other types of materials having the desired consistency characteristic
and a temporal dimension (e.g. long life magazines or revues, early printed books).
âą Provide the analysis to curators and digital mediators for editorialization purposes:
introduction to the collection, timelines (fig. 15), and set up a BaseX toolkit (Web
client/server architecture and JSON serialization of XML data) to empower them with
data mining skills and methods.
Fig 15. Timeline example (JDPL)
âą Provide the derived datasets to researchers in digital humanities, history of press,
information science. Such data, possibly crossed with the OCRed text transcription,
usually provide a fertile ground for research hypotheses.
âą Assess the opportunity of setting up a data mining framework in the BnF to be feed with
Gallicaâs collections.
The last two issues will be addressed during the BnF research project « Corpus » (2016-
2019), which aims to study the data mining and text mining services a library can provide for
researchers.
10