Machine-readable data is data (or metadata) which is in a format that can be understood by a computer.
There are two types; human-readable data that is marked up so that it can also be read by machines (examples; microformats, RDFa) or data file formats intended principally for processing by machines (RDF, XML, JSON).
Machine readable is not synonymous with digitally accessible. A digitally accessible document may be online, making it easier for a human to access it via a computer, but unless the relevant data is available in a machine readable format, it will be much harder to use the computer to extract, transform and process that data.
For purposes of implementation of the Government Performance and Results Act (GPRA) Modernization Act, the Office of Management and Budget (OMB) defines "machine readable" as follows: "Format in a standard computer language (not English text) that can be read automatically by a web browser or computer system. (e.g.; xml). Traditional word processing documents, hypertext markup language (HTML) and portable document format (PDF) files are easily read by humans but typically are difficult for machines to interpret. Other formats such as extensible markup language (XML), (JSON), or spreadsheets with header columns that can be exported as comma separated values (CSV) are machine readable formats. It is possible to make traditional word processing documents and other formats machine readable but the documents must include enhanced structural elements."
Data Literacy 101 Episode 2: Machine-readable data
BNHR Data Literacy 101 - https://bnhr.xyz/data-literacy-101
Welcome to Data Literacy 101 - a series of short videos about fundamental topics around data literacy such as open data, data ethics, and working with data.
You don't need any technical background for the videos to be useful. This is for you if you want to know how data and data literacy can be applied in your everyday life and in the work that you do—especially if you're a journalist, working for a civil society organization, a civil servant looking to upskill yourself, or just a regular citizen interested in data.
But first, a word of caution. The fields of data and data literacy are vast and nuanced and the videos can only provide a fraction of all the available knowledge and information about a topic. However, we can still...
published: 20 Oct 2022
How to transform govt financials to machine-readable format?
published: 20 Sep 2023
Standards Machine Applicable Readable and Transferable (SMART) A Comprehensive Guide
published: 13 Feb 2024
Beyond Machine Readability - FAIR Principles and Machine Actionability
published: 21 Jan 2024
Strategies for Human-Machine Readable Data Curation
Francis Chemorion (InsilicoTrials)
Strategies for Human-Machine Readable Data Curation
Abstract: Data curation stands at the forefront of enhancing interoperability and utility for datasets across scientific and technological domains, ensuring both human and machine readability. The intricate process involves management, preservation, and annotation of data, underpinned by a strong emphasis on metadata quality, standardized formats, and data integrity. Addressing the dual challenge of making data machine-readable through the adoption of ontologies and data schemas while also ensuring human readability with intuitive documentation and visualization, is critical for bridging the gap between raw data and actionable insights. The integration of human-centric and computational approaches in da...
published: 04 Jul 2024
API Terms of Service : From Creative commons to Machine readability - Mehdi Medjaoui, ALIAS.dev
API Terms of Service : From Creative commons to Machine readability - Mehdi Medjaoui, ALIAS.dev
Speakers: Mehdi Medjaoui
How a Creative Commons model for API Terms of Service for API ecosystems participates in the creation of open, safe and sustainable digital infrastructure?
How to include API ToS into OpenAPI specifications?
In order to scale technical, business and legal interoperability between digital infrastructures as APIs enable, APITos-CC build a “Creative Commons” framework for API terms of Service, as a contract to automatically read, control and enforce APIs Terms of service between digital infrastructure and applications. The terms of service for APIs represent a boundary object whose identification of specific clauses and degree of ""openness"" (on the model of Creative Comm...
published: 08 Oct 2021
Q&A - TDM and Machine Readability
"TDM and Machine Readability of Open Access research" with Nancy Pontika (24.10.2018)
published: 26 Nov 2018
Human 👉 Machine 👉 Human: Understanding Human-Readable Quality Signals - Presented by Ruth Burr
Download Ruth's presentation ►https://mz.cm/31fHjPe
The push and pull of making decisions for searchers versus search engines is an ever-present SEO conundrum. How do you tackle industry changes through the lens of whether something is good for humans or for machines? Ruth will take us through human-readable quality signals and their machine-readable equivalents and how to make SEO decisions accordingly, as well as how to communicate the change to clients and bosses.
- Presented by Ruth Burr Reedy at Mozcon 2019
Register for MozCon 2021 ► https://mz.cm/3dB2MaA
***************************************
Additional Moz Resources:
Beginner's Guide to SEO ► https://mz.cm/2SGOGdV
The Keyword Research Master Guide ► https://mz.cm/3jPdPio
30-day Moz Pro Free Trial ► https://mz.cm/3jZq3p3 ...
published: 21 Oct 2020
Evaluation Metrics for Machine Reading Comprehension Prerequisite Skills and Readability | ACL 2017
🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Language Processing (NLP) 📝, and Large Language Models (LLMs) 🤖. Follow (https://twitter.com/mtnayeem) on Twitter 🐦 for real-time updates, news, and discussions in the field.
Check out the following interesting papers. Happy learning!
Paper Title: "On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction"
Paper: https://aclanthology.org/2023.findings-eacl.125/
Dataset: https://huggingface.co/datasets/tafseer-nayeem/review_helpfulness_prediction
Paper Title: "Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion"
Paper: https://aclanthology.org/C18-1102/
Paper Title: "Extract with Order for Coherent Multi-Document Summarization"
Paper: https://aclan...
published: 31 Mar 2018
RDMbites | Machine and human readable file naming
__________________
Relevant links:
https://rdmkit.elixir-europe.org/data_organisation.html
__________________
Creators: Sara Morsy (https://orcid.org/0000-0002-2477-1139 ), Robert Andrews (https://orcid.org/0000-0002-3491-2361 ), Branka Franicievic (https://orcid.org/0000-0003-2163-6868 ),
Reviewers: Munazah Andrabi (https://orcid.org/0000-0002-7718-5109 )
___________________
The RDMbites are funded by the ELIXIR-UK: FAIR Data Stewardship training UKRI award (MR/V038966/1) https://elixiruknode.org/about/projects/data-stewardship-training-project/
___________________
00:00 Learning objectives
00:38 Number at the beginning of files
01:30 Dates at the beginning of files
01:57 Human-readable example
02:54 Machine-readable example
03:49 File versioning example
04:22 Hierarchical directory nam...
BNHR Data Literacy 101 - https://bnhr.xyz/data-literacy-101
Welcome to Data Literacy 101 - a series of short videos about fundamental topics around data litera...
BNHR Data Literacy 101 - https://bnhr.xyz/data-literacy-101
Welcome to Data Literacy 101 - a series of short videos about fundamental topics around data literacy such as open data, data ethics, and working with data.
You don't need any technical background for the videos to be useful. This is for you if you want to know how data and data literacy can be applied in your everyday life and in the work that you do—especially if you're a journalist, working for a civil society organization, a civil servant looking to upskill yourself, or just a regular citizen interested in data.
But first, a word of caution. The fields of data and data literacy are vast and nuanced and the videos can only provide a fraction of all the available knowledge and information about a topic. However, we can still provide a general overview and a good foundation for you to start on.
Introduction - 00:00
Data Literacy 101 - 01:21
What does it mean to be machine-readable? - 01:34
Common data formats (1) - 02:57
More about CSVs - 03:36
Common data formats (2) - 05:04
What about PDFs - 06:33
Converting to machine-readable formats - 07:20
Importance of machine-readability - 08:51
Machine-readability and open data - 10:00
Next episode! - 11:41
BNHR Data Literacy 101 - https://bnhr.xyz/data-literacy-101
Welcome to Data Literacy 101 - a series of short videos about fundamental topics around data literacy such as open data, data ethics, and working with data.
You don't need any technical background for the videos to be useful. This is for you if you want to know how data and data literacy can be applied in your everyday life and in the work that you do—especially if you're a journalist, working for a civil society organization, a civil servant looking to upskill yourself, or just a regular citizen interested in data.
But first, a word of caution. The fields of data and data literacy are vast and nuanced and the videos can only provide a fraction of all the available knowledge and information about a topic. However, we can still provide a general overview and a good foundation for you to start on.
Introduction - 00:00
Data Literacy 101 - 01:21
What does it mean to be machine-readable? - 01:34
Common data formats (1) - 02:57
More about CSVs - 03:36
Common data formats (2) - 05:04
What about PDFs - 06:33
Converting to machine-readable formats - 07:20
Importance of machine-readability - 08:51
Machine-readability and open data - 10:00
Next episode! - 11:41
Francis Chemorion (InsilicoTrials)
Strategies for Human-Machine Readable Data Curation
Abstract: Data curation stands at the forefront of enhancing interoperab...
Francis Chemorion (InsilicoTrials)
Strategies for Human-Machine Readable Data Curation
Abstract: Data curation stands at the forefront of enhancing interoperability and utility for datasets across scientific and technological domains, ensuring both human and machine readability. The intricate process involves management, preservation, and annotation of data, underpinned by a strong emphasis on metadata quality, standardized formats, and data integrity. Addressing the dual challenge of making data machine-readable through the adoption of ontologies and data schemas while also ensuring human readability with intuitive documentation and visualization, is critical for bridging the gap between raw data and actionable insights. The integration of human-centric and computational approaches in data curation fosters the creation of datasets that are not only rich and versatile but also accessible to a broad spectrum of analyses and applications. Through illustrative case studies from various fields such as biomedical data management, the transformative power of effectively curated datasets is showcased. The discussion also ventures into the emerging trends and future directions in data curation, highlighting the increasing importance of collaborative and interdisciplinary efforts in enhancing the data ecosystem for a more inclusive, reliable, and insightful exploration of data. The evolving landscape calls for innovative curation practices that seamlessly blend technology and user engagement, paving the way for advancements in data-driven research and applications.
Biosketch: Francis Kiptengwer Chemorion is an experienced data professional, specializing in data curation, science, engineering, modeling, analytics, stewardship and crafting machine learning models for intricate data interpretation. Currently a PhD Candidate in Information Technology at UPF, Chemorion has led pioneering research focused on elevating data accessibility and utility within the realm of intervertebral disc degeneration studies. With a rich background as an AI Engineer at InSilicoTrials Technologies and as a seasoned consultant, he has contributed several preprints targeting leading journals. Chemorion is a vocal proponent of cutting-edge data management techniques and fervently supports the seamless integration of technology into data curation processes.
Francis Chemorion (InsilicoTrials)
Strategies for Human-Machine Readable Data Curation
Abstract: Data curation stands at the forefront of enhancing interoperability and utility for datasets across scientific and technological domains, ensuring both human and machine readability. The intricate process involves management, preservation, and annotation of data, underpinned by a strong emphasis on metadata quality, standardized formats, and data integrity. Addressing the dual challenge of making data machine-readable through the adoption of ontologies and data schemas while also ensuring human readability with intuitive documentation and visualization, is critical for bridging the gap between raw data and actionable insights. The integration of human-centric and computational approaches in data curation fosters the creation of datasets that are not only rich and versatile but also accessible to a broad spectrum of analyses and applications. Through illustrative case studies from various fields such as biomedical data management, the transformative power of effectively curated datasets is showcased. The discussion also ventures into the emerging trends and future directions in data curation, highlighting the increasing importance of collaborative and interdisciplinary efforts in enhancing the data ecosystem for a more inclusive, reliable, and insightful exploration of data. The evolving landscape calls for innovative curation practices that seamlessly blend technology and user engagement, paving the way for advancements in data-driven research and applications.
Biosketch: Francis Kiptengwer Chemorion is an experienced data professional, specializing in data curation, science, engineering, modeling, analytics, stewardship and crafting machine learning models for intricate data interpretation. Currently a PhD Candidate in Information Technology at UPF, Chemorion has led pioneering research focused on elevating data accessibility and utility within the realm of intervertebral disc degeneration studies. With a rich background as an AI Engineer at InSilicoTrials Technologies and as a seasoned consultant, he has contributed several preprints targeting leading journals. Chemorion is a vocal proponent of cutting-edge data management techniques and fervently supports the seamless integration of technology into data curation processes.
API Terms of Service : From Creative commons to Machine readability - Mehdi Medjaoui, ALIAS.dev
Speakers: Mehdi Medjaoui
How a Creative Commons model for API Te...
API Terms of Service : From Creative commons to Machine readability - Mehdi Medjaoui, ALIAS.dev
Speakers: Mehdi Medjaoui
How a Creative Commons model for API Terms of Service for API ecosystems participates in the creation of open, safe and sustainable digital infrastructure?
How to include API ToS into OpenAPI specifications?
In order to scale technical, business and legal interoperability between digital infrastructures as APIs enable, APITos-CC build a “Creative Commons” framework for API terms of Service, as a contract to automatically read, control and enforce APIs Terms of service between digital infrastructure and applications. The terms of service for APIs represent a boundary object whose identification of specific clauses and degree of ""openness"" (on the model of Creative Commons licenses with different degrees of conditions of use) can work towards a better understanding and vigilance regarding the constitution of open, secure, safe and sustainable digital infrastructure ecosystems. The goal is to present the project and how it could be implemented into OpenAPI documents.
The project is part of the $1,3M grant for Digital Infrastructure from Ford Foundation/Mozilla/Sloan/Open Society foundations.
API Terms of Service : From Creative commons to Machine readability - Mehdi Medjaoui, ALIAS.dev
Speakers: Mehdi Medjaoui
How a Creative Commons model for API Terms of Service for API ecosystems participates in the creation of open, safe and sustainable digital infrastructure?
How to include API ToS into OpenAPI specifications?
In order to scale technical, business and legal interoperability between digital infrastructures as APIs enable, APITos-CC build a “Creative Commons” framework for API terms of Service, as a contract to automatically read, control and enforce APIs Terms of service between digital infrastructure and applications. The terms of service for APIs represent a boundary object whose identification of specific clauses and degree of ""openness"" (on the model of Creative Commons licenses with different degrees of conditions of use) can work towards a better understanding and vigilance regarding the constitution of open, secure, safe and sustainable digital infrastructure ecosystems. The goal is to present the project and how it could be implemented into OpenAPI documents.
The project is part of the $1,3M grant for Digital Infrastructure from Ford Foundation/Mozilla/Sloan/Open Society foundations.
Download Ruth's presentation ►https://mz.cm/31fHjPe
The push and pull of making decisions for searchers versus search engines is an ever-present SEO conundrum....
Download Ruth's presentation ►https://mz.cm/31fHjPe
The push and pull of making decisions for searchers versus search engines is an ever-present SEO conundrum. How do you tackle industry changes through the lens of whether something is good for humans or for machines? Ruth will take us through human-readable quality signals and their machine-readable equivalents and how to make SEO decisions accordingly, as well as how to communicate the change to clients and bosses.
- Presented by Ruth Burr Reedy at Mozcon 2019
Register for MozCon 2021 ► https://mz.cm/3dB2MaA
***************************************
Additional Moz Resources:
Beginner's Guide to SEO ► https://mz.cm/2SGOGdV
The Keyword Research Master Guide ► https://mz.cm/3jPdPio
30-day Moz Pro Free Trial ► https://mz.cm/3jZq3p3
Check out Moz Local ► https://mz.cm/36Pbz7h
Learn about STAT ► https://mz.cm/2IiqTzf
***************************************
STAY IN TOUCH:
Moz ► https://mz.cm/30QvHCm
Facebook ► https://www.facebook.com/moz
Twitter ► https://twitter.com/Moz
LinkedIn ► https://www.linkedin.com/company/moz
Download Ruth's presentation ►https://mz.cm/31fHjPe
The push and pull of making decisions for searchers versus search engines is an ever-present SEO conundrum. How do you tackle industry changes through the lens of whether something is good for humans or for machines? Ruth will take us through human-readable quality signals and their machine-readable equivalents and how to make SEO decisions accordingly, as well as how to communicate the change to clients and bosses.
- Presented by Ruth Burr Reedy at Mozcon 2019
Register for MozCon 2021 ► https://mz.cm/3dB2MaA
***************************************
Additional Moz Resources:
Beginner's Guide to SEO ► https://mz.cm/2SGOGdV
The Keyword Research Master Guide ► https://mz.cm/3jPdPio
30-day Moz Pro Free Trial ► https://mz.cm/3jZq3p3
Check out Moz Local ► https://mz.cm/36Pbz7h
Learn about STAT ► https://mz.cm/2IiqTzf
***************************************
STAY IN TOUCH:
Moz ► https://mz.cm/30QvHCm
Facebook ► https://www.facebook.com/moz
Twitter ► https://twitter.com/Moz
LinkedIn ► https://www.linkedin.com/company/moz
🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Language Processing (NLP) 📝, and Large Language Models (LLMs) 🤖. Follow (ht...
🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Language Processing (NLP) 📝, and Large Language Models (LLMs) 🤖. Follow (https://twitter.com/mtnayeem) on Twitter 🐦 for real-time updates, news, and discussions in the field.
Check out the following interesting papers. Happy learning!
Paper Title: "On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction"
Paper: https://aclanthology.org/2023.findings-eacl.125/
Dataset: https://huggingface.co/datasets/tafseer-nayeem/review_helpfulness_prediction
Paper Title: "Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion"
Paper: https://aclanthology.org/C18-1102/
Paper Title: "Extract with Order for Coherent Multi-Document Summarization"
Paper: https://aclanthology.org/W17-2407.pdf
Paper Title: "Paraphrastic Fusion for Abstractive Multi-Sentence Compression Generation"
Paper: https://dl.acm.org/doi/abs/10.1145/3132847.3133106
Paper Title: "Neural Diverse Abstractive Sentence Compression Generation"
Paper: https://link.springer.com/chapter/10.1007/978-3-030-15719-7_14
🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Language Processing (NLP) 📝, and Large Language Models (LLMs) 🤖. Follow (https://twitter.com/mtnayeem) on Twitter 🐦 for real-time updates, news, and discussions in the field.
Check out the following interesting papers. Happy learning!
Paper Title: "On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction"
Paper: https://aclanthology.org/2023.findings-eacl.125/
Dataset: https://huggingface.co/datasets/tafseer-nayeem/review_helpfulness_prediction
Paper Title: "Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion"
Paper: https://aclanthology.org/C18-1102/
Paper Title: "Extract with Order for Coherent Multi-Document Summarization"
Paper: https://aclanthology.org/W17-2407.pdf
Paper Title: "Paraphrastic Fusion for Abstractive Multi-Sentence Compression Generation"
Paper: https://dl.acm.org/doi/abs/10.1145/3132847.3133106
Paper Title: "Neural Diverse Abstractive Sentence Compression Generation"
Paper: https://link.springer.com/chapter/10.1007/978-3-030-15719-7_14
__________________
Relevant links:
https://rdmkit.elixir-europe.org/data_organisation.html
__________________
Creators: Sara Morsy (https://orcid.org/0000-0002...
__________________
Relevant links:
https://rdmkit.elixir-europe.org/data_organisation.html
__________________
Creators: Sara Morsy (https://orcid.org/0000-0002-2477-1139 ), Robert Andrews (https://orcid.org/0000-0002-3491-2361 ), Branka Franicievic (https://orcid.org/0000-0003-2163-6868 ),
Reviewers: Munazah Andrabi (https://orcid.org/0000-0002-7718-5109 )
___________________
The RDMbites are funded by the ELIXIR-UK: FAIR Data Stewardship training UKRI award (MR/V038966/1) https://elixiruknode.org/about/projects/data-stewardship-training-project/
___________________
00:00 Learning objectives
00:38 Number at the beginning of files
01:30 Dates at the beginning of files
01:57 Human-readable example
02:54 Machine-readable example
03:49 File versioning example
04:22 Hierarchical directory naming
04:44 Acknowledgements
__________________
Relevant links:
https://rdmkit.elixir-europe.org/data_organisation.html
__________________
Creators: Sara Morsy (https://orcid.org/0000-0002-2477-1139 ), Robert Andrews (https://orcid.org/0000-0002-3491-2361 ), Branka Franicievic (https://orcid.org/0000-0003-2163-6868 ),
Reviewers: Munazah Andrabi (https://orcid.org/0000-0002-7718-5109 )
___________________
The RDMbites are funded by the ELIXIR-UK: FAIR Data Stewardship training UKRI award (MR/V038966/1) https://elixiruknode.org/about/projects/data-stewardship-training-project/
___________________
00:00 Learning objectives
00:38 Number at the beginning of files
01:30 Dates at the beginning of files
01:57 Human-readable example
02:54 Machine-readable example
03:49 File versioning example
04:22 Hierarchical directory naming
04:44 Acknowledgements
BNHR Data Literacy 101 - https://bnhr.xyz/data-literacy-101
Welcome to Data Literacy 101 - a series of short videos about fundamental topics around data literacy such as open data, data ethics, and working with data.
You don't need any technical background for the videos to be useful. This is for you if you want to know how data and data literacy can be applied in your everyday life and in the work that you do—especially if you're a journalist, working for a civil society organization, a civil servant looking to upskill yourself, or just a regular citizen interested in data.
But first, a word of caution. The fields of data and data literacy are vast and nuanced and the videos can only provide a fraction of all the available knowledge and information about a topic. However, we can still provide a general overview and a good foundation for you to start on.
Introduction - 00:00
Data Literacy 101 - 01:21
What does it mean to be machine-readable? - 01:34
Common data formats (1) - 02:57
More about CSVs - 03:36
Common data formats (2) - 05:04
What about PDFs - 06:33
Converting to machine-readable formats - 07:20
Importance of machine-readability - 08:51
Machine-readability and open data - 10:00
Next episode! - 11:41
Francis Chemorion (InsilicoTrials)
Strategies for Human-Machine Readable Data Curation
Abstract: Data curation stands at the forefront of enhancing interoperability and utility for datasets across scientific and technological domains, ensuring both human and machine readability. The intricate process involves management, preservation, and annotation of data, underpinned by a strong emphasis on metadata quality, standardized formats, and data integrity. Addressing the dual challenge of making data machine-readable through the adoption of ontologies and data schemas while also ensuring human readability with intuitive documentation and visualization, is critical for bridging the gap between raw data and actionable insights. The integration of human-centric and computational approaches in data curation fosters the creation of datasets that are not only rich and versatile but also accessible to a broad spectrum of analyses and applications. Through illustrative case studies from various fields such as biomedical data management, the transformative power of effectively curated datasets is showcased. The discussion also ventures into the emerging trends and future directions in data curation, highlighting the increasing importance of collaborative and interdisciplinary efforts in enhancing the data ecosystem for a more inclusive, reliable, and insightful exploration of data. The evolving landscape calls for innovative curation practices that seamlessly blend technology and user engagement, paving the way for advancements in data-driven research and applications.
Biosketch: Francis Kiptengwer Chemorion is an experienced data professional, specializing in data curation, science, engineering, modeling, analytics, stewardship and crafting machine learning models for intricate data interpretation. Currently a PhD Candidate in Information Technology at UPF, Chemorion has led pioneering research focused on elevating data accessibility and utility within the realm of intervertebral disc degeneration studies. With a rich background as an AI Engineer at InSilicoTrials Technologies and as a seasoned consultant, he has contributed several preprints targeting leading journals. Chemorion is a vocal proponent of cutting-edge data management techniques and fervently supports the seamless integration of technology into data curation processes.
API Terms of Service : From Creative commons to Machine readability - Mehdi Medjaoui, ALIAS.dev
Speakers: Mehdi Medjaoui
How a Creative Commons model for API Terms of Service for API ecosystems participates in the creation of open, safe and sustainable digital infrastructure?
How to include API ToS into OpenAPI specifications?
In order to scale technical, business and legal interoperability between digital infrastructures as APIs enable, APITos-CC build a “Creative Commons” framework for API terms of Service, as a contract to automatically read, control and enforce APIs Terms of service between digital infrastructure and applications. The terms of service for APIs represent a boundary object whose identification of specific clauses and degree of ""openness"" (on the model of Creative Commons licenses with different degrees of conditions of use) can work towards a better understanding and vigilance regarding the constitution of open, secure, safe and sustainable digital infrastructure ecosystems. The goal is to present the project and how it could be implemented into OpenAPI documents.
The project is part of the $1,3M grant for Digital Infrastructure from Ford Foundation/Mozilla/Sloan/Open Society foundations.
Download Ruth's presentation ►https://mz.cm/31fHjPe
The push and pull of making decisions for searchers versus search engines is an ever-present SEO conundrum. How do you tackle industry changes through the lens of whether something is good for humans or for machines? Ruth will take us through human-readable quality signals and their machine-readable equivalents and how to make SEO decisions accordingly, as well as how to communicate the change to clients and bosses.
- Presented by Ruth Burr Reedy at Mozcon 2019
Register for MozCon 2021 ► https://mz.cm/3dB2MaA
***************************************
Additional Moz Resources:
Beginner's Guide to SEO ► https://mz.cm/2SGOGdV
The Keyword Research Master Guide ► https://mz.cm/3jPdPio
30-day Moz Pro Free Trial ► https://mz.cm/3jZq3p3
Check out Moz Local ► https://mz.cm/36Pbz7h
Learn about STAT ► https://mz.cm/2IiqTzf
***************************************
STAY IN TOUCH:
Moz ► https://mz.cm/30QvHCm
Facebook ► https://www.facebook.com/moz
Twitter ► https://twitter.com/Moz
LinkedIn ► https://www.linkedin.com/company/moz
🔔 Stay Connected! Get the latest insights on Artificial Intelligence (AI) 🧠, Natural Language Processing (NLP) 📝, and Large Language Models (LLMs) 🤖. Follow (https://twitter.com/mtnayeem) on Twitter 🐦 for real-time updates, news, and discussions in the field.
Check out the following interesting papers. Happy learning!
Paper Title: "On the Role of Reviewer Expertise in Temporal Review Helpfulness Prediction"
Paper: https://aclanthology.org/2023.findings-eacl.125/
Dataset: https://huggingface.co/datasets/tafseer-nayeem/review_helpfulness_prediction
Paper Title: "Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion"
Paper: https://aclanthology.org/C18-1102/
Paper Title: "Extract with Order for Coherent Multi-Document Summarization"
Paper: https://aclanthology.org/W17-2407.pdf
Paper Title: "Paraphrastic Fusion for Abstractive Multi-Sentence Compression Generation"
Paper: https://dl.acm.org/doi/abs/10.1145/3132847.3133106
Paper Title: "Neural Diverse Abstractive Sentence Compression Generation"
Paper: https://link.springer.com/chapter/10.1007/978-3-030-15719-7_14
__________________
Relevant links:
https://rdmkit.elixir-europe.org/data_organisation.html
__________________
Creators: Sara Morsy (https://orcid.org/0000-0002-2477-1139 ), Robert Andrews (https://orcid.org/0000-0002-3491-2361 ), Branka Franicievic (https://orcid.org/0000-0003-2163-6868 ),
Reviewers: Munazah Andrabi (https://orcid.org/0000-0002-7718-5109 )
___________________
The RDMbites are funded by the ELIXIR-UK: FAIR Data Stewardship training UKRI award (MR/V038966/1) https://elixiruknode.org/about/projects/data-stewardship-training-project/
___________________
00:00 Learning objectives
00:38 Number at the beginning of files
01:30 Dates at the beginning of files
01:57 Human-readable example
02:54 Machine-readable example
03:49 File versioning example
04:22 Hierarchical directory naming
04:44 Acknowledgements
Machine-readable data is data (or metadata) which is in a format that can be understood by a computer.
There are two types; human-readable data that is marked up so that it can also be read by machines (examples; microformats, RDFa) or data file formats intended principally for processing by machines (RDF, XML, JSON).
Machine readable is not synonymous with digitally accessible. A digitally accessible document may be online, making it easier for a human to access it via a computer, but unless the relevant data is available in a machine readable format, it will be much harder to use the computer to extract, transform and process that data.
For purposes of implementation of the Government Performance and Results Act (GPRA) Modernization Act, the Office of Management and Budget (OMB) defines "machine readable" as follows: "Format in a standard computer language (not English text) that can be read automatically by a web browser or computer system. (e.g.; xml). Traditional word processing documents, hypertext markup language (HTML) and portable document format (PDF) files are easily read by humans but typically are difficult for machines to interpret. Other formats such as extensible markup language (XML), (JSON), or spreadsheets with header columns that can be exported as comma separated values (CSV) are machine readable formats. It is possible to make traditional word processing documents and other formats machine readable but the documents must include enhanced structural elements."
... a format readable by machines ... Barcode reading software, or scanners, capture the barcode pattern and decode the data into a machine-readable format, facilitating fast and accurate data processing.
How is this salience and sentiment reciprocated in the Hellenic Parliament? In other words, how have Greek MPs viewed the US over time? Here we can use the machine-readable data provided by Dritsa and Loridas (2018).
In its test approach, Chainlink transformed corporate reports into machine-readable texts, allowing LLM to extract specific business process events, such as dividend payments.
But through this process of conversion to machine-readable data, there’s a different type of accessibility and understanding that’s really interesting to engage with.”.
Drone captured audit data can be combined with various alternative sources of data such as QR (quick-response) machine-readable codes, satellite imagery, sensor networks and Geographical Information...
They say the price estimator tool does not provide actual prices, and the machine-readable files are often riddled with incomplete, incorrect or non-functioning data ... And federal regulators would determine the format for reporting the data.
A machine-readable barcode with applicable class, endorsements and restrictions are printed on the back of the card ... New year-over-year data by Vaco offers a snapshot of worker sentiment amid economic uncertainty.
This includes elaborate overlapping of data and graphics and laser engraved ... A machine-readable barcode with applicable class, endorsements, and restrictions are printed on the back of the card.
Others are reevaluating the alphabets that earlier scholars created to convert the Voynichese letterforms into machine-readable ASCII text—the raw data for computational studies of the language.
Some have turned to technology for converting paper into machine readable data ... What Hyperscience does uniquely is if the machine is a little bit not confident, right? So I’ve got 100 field, ...
Putting all that data ...Introducing machine-readable tools like SsODNet can dramatically speed up the time it takes to produce new research on SSOs, enabling those researchers to do better-quality work.
Converts spoken language into machine-readable text using complex algorithms trained on vast amounts of speech data ... Dialog managers rely on pre-defined rules, decision trees, or machine learning models to determine the most appropriate response.