This video is a discussion of data management and data science. It includes a definition of a data management plan, the importance of data management plans, and the difference between an object and an output. The video also discusses data preservation, code of conduct, and the use of data repositories.
00:00:00 This video provides a brief introduction to data management plans and their importance in submitting proposals to foundations. It also provides a definition of a data management plan.
00:05:00 The video provides an overview of data science, including the importance of data management plans and the need for backups. It also discusses the difference between an object and an output, and the importance of preventing research from being lost in the event of a laptop theft.
00:10:00 The video discusses the importance of data preservation, code of conduct, and how having a project with a code of conduct can help to ensure everyone is playing by the same rules. It also discusses the use of data preservation, satellite imagery, and Google Street View.
00:15:00 The video presents the VI Workshop on Data Science, in which participants learn about data management principles. The workshop includes a discussion of the Fair Data Principles, which provide a guide for handling data sets.
00:20:00 The video discusses the importance of data science, the need for a data scientist, and the importance of following operational procedures. It provides an example of an operational procedure and provides instructions on how to make decisions.
00:25:00 The video provides tips on how to properly manage data and software during research projects. The main points include:
- having a PI who is willing to help with data management,
- ensuring all researchers on the team follow data management guidelines, and
- being dedicated to robust data and software management in order to facilitate publication.
00:30:00 The video discusses the importance of data citation and how it can help increase the likelihood of a paper's success. The presenter provides an example of how data can be used to improve research and discusses how communication between team members can be facilitated through a Slack or email list.
00:35:00 The video discusses the use of data repositories, including zanodo, and how they can be used to help researchers track and report their work. It also introduces the Orchid Project, which is a data preservation initiative led by the University of Sao Paulo.
00:40:00 The video discusses the importance of having an orchid registered with the Hub of research data, and how linking your CV to your orchid will help increase your chances of being cited. It also provides tips on how to update your orchid metadata.
00:45:00 In this video, Pedro talks about the importance of data management, data stewardship, and software management in research. He also provides slides that detail these concepts.
00:50:00 The presenter discusses how to protect Big Data, mentioning that Globus can be a source for preserving large data sets. They also mention that some data sets, such as medical data, are difficult to access and require specific permissions. Finally, the presenter provides a brief explanation of open data and how it can be difficult to release certain data sets in an open manner.
00:55:00 The speaker describes how data can be useful for research, and how it can be used to help shape how research is conducted.
This video provides an overview of data repositories, their benefits, and how to find the best one for your needs. It also discusses the importance of data fairness and how to ensure compliance with fair principles.
01:00:00 This video introduces the speaker, Homa Primaki, and the research they are working on at their university. Primaki discusses the challenges of working with highly pathogenic agents, and how their university is working to create an ecosystem of data-driven research tools. He also discusses ways to communicate research findings effectively.
01:05:00 The presenter discusses the use of various tools to facilitate data science, such as internet, social media, and project management tools. He also discusses the importance of disseminating research results and encourages collaboration among researchers.
01:10:00 The video discusses the importance of metadata when working with research data, and provides tips on how to create and manage metadata. The video also discusses the importance of versioning research outputs, and provides examples of how to do this.
01:15:00 This video provides a tutorial on how to create a CV that is both effective and stable over the course of a researcher's career. The video discusses the importance of using identifiers in all of a researcher's content and provides tips on how to create and export a CV in a variety of formats.
01:20:00 This video introduces the Verification process, which is a way to assess the impact of your research. It provides an overview of the Verification process and explains how to use it to assess the impact of your research. Finally, it provides an example of a verb guiding principles paper.
01:25:00 The video discusses ways to increase the impact of one's scientific research by using various communication channels. It provides tips on preparing and disseminating communication materials, as well as examples of successful strategies.
01:30:00 This 1-minute video provides a brief introduction to data archiving and the benefits of doing so. The presenter explains that data can degrade over time, and as a result, it's important to archive data periodically in order to preserve it.
01:35:00 This video discusses how data can become outdated and how it can be hard to keep track of specific details about it once it's been published. The video also gives an example of how data can be used in a research life cycle.
01:40:00 The video covers the different types of data repositories and their associated benefits and challenges. It also discusses how data repositories can help researchers connect with each other and share their data.
01:45:00 This video covers the various types of data repositories available, their purposes, and how to find the best one for your needs. It also provides information on how to check the data within these repositories to ensure they adhere to four Fair principles.
01:50:00 The presenter discusses the EDI repository, which was created in order to share long-term ecological research data across multiple sites and platforms. She also provides tips for data users and researchers.
01:55:00 The speaker discusses the need for data to be fair and compliant with fair principles, and explains how their own repository checks data for compliance.
This video introduces the fifth workshop on data science, which will be held on May 10th in Paris. The workshop will cover various data science topics, including machine learning, natural language processing, data visualization, and more. The video provides instructions on how to add information about one's research to their Orchid record.
02:00:00 This video introduces the 5th workshop on data science, which will be held on May 10th in Paris. The workshop will cover various data science topics, including machine learning, natural language processing, data visualization, and more.
02:05:00 In this workshop, participants learn about data science techniques.
02:10:00 In this video, foreign speakers Anna and Leslie discuss data science. Leslie shares that they are hearing Anna, but Anna can't hear them. Leslie testifies that their screen sharing is working, but Anna is not able to. Anna shares that she is in Brazil, and Leslie shares that they are also in Brazil. Anna and Leslie discuss data science and Leslie shares that they threw in Geo and Co data. Anna and Leslie hug, and Anna says that she is so sorry that she couldn't be there.
02:15:00 The video explains the steps for a data science workshop. The presenter turns off the microphone, navigates everyone back in the room, and starts the workshop.
02:20:00 This video introduces Anna Heredia, an international consultant with 30 years of experience in research, and discusses persistent identifiers. Anna explains that persistent identifiers are important for ensuring the accuracy and reliability of research data.
02:25:00 The presenter discusses the importance of persistent identifiers in open science, and how Orchid IDs and data sites play a key role in achieving this. The four principles of open science – findable, accessible, interoperable, and reusable – are also discussed.
02:30:00 The video presents the importance of data and metadata, as well as the use of DOI identifiers to make research output easily identifiable and discoverable. The presenter highlights a nice tool that allows journals to track the metadata of their content.
02:35:00 Orchid is a 10-year-old non-profit organization with an open code, open API, and open identifier. Orchid is supported by its members, who through the Integrations with the API can add information and read information from Orchid records.
02:40:00 Orchid is a platform that allows researchers to connect their records to databases and organization systems, and it is being used more and more to help researchers save time and effort in administrative tasks.
02:45:00 The video discusses the importance of Orchid data integration, especially for researchers who submit their work to publication-based systems. It explains that by having an authenticated Orchid ID, researchers can ensure that their correct information is shared with the organization, which then becomes a trusted source of information.
02:50:00 The video presents a workshop on data science, and provides instructions on how to add information about one's research to their orchid record. The presenter asks participants to share their orchid IDs, and notes that they can also follow the link to share their orcid record on the Crossroad and data site websites.
02:55:00 This video introduces the ORCID open registry, which enables anyone to create an identifier for their research. After registering, participants are shown how to add their name, email, and password to their account, and are then shown how to use the registry to find and add research IDs of other researchers.
The video discusses the importance of data sharing and interoperability, and provides information on the VI Workshop on Data Science, which will focus on these topics. It also provides a tutorial on how to use data science tools and recommends ways to ensure data provenance, reproducibility, and attribution.
03:00:00 This video introduces the attendees to the VI Workshop on Data Science, which focused on authenticating research organizations. The video finishes by sharing that many governments and funders are adopting Orchid as part of their National research strategies.
03:05:00 The video discusses the use of Orchid, a persistent, non-proprietary identifier system, by researchers in Europe. It explains that Orchid is mandatory for applicants and participants in Horizon 2020's research program, and that it is also recommended that identifiers that are persistent and open be used, such as Orchid. The video also provides information on the Coalition for Open Science's plan for supporting the privacy of research data.
03:10:00 Leslie Weiborne is a researcher with expertise in geoinformatics, mineralogy, and metallogeny. She has spent over 40 years working in the government research sector, and before speaking, she thanked the traditional owners of the land, paid her respects to elders, and acknowledged their connection to their culture.
03:15:00 The speaker notes that there are grand challenges that all Earth environmental science data contribute to beyond our immediate project, but that we need agreed global standards in order to share data programmatically. He notes that we are trying to work towards a future state where multiple machine actionable systems are compliant with these standards, and that we need to invigorate the international community to recognize the problem and start developing just the simple vocabs needed to describe our data. He predicts that the persistence of our data over time will not be great if we continue to use local standards.
03:20:00 The video discusses the importance of data interoperability, and provides examples of similar groups that started around the same time. The three main points the video makes are: data sharing should be facilitated by adopting data principles, data sharing should be cross-disciplinary, and data sharing should be international.
03:25:00 The video introduces the "VI Workshop on Data Science," which will be held in Gothenburg, Sweden from March 21-23. The workshop aims to bring together data infrastructure developers from around the world in order to improve data sharing and data-driven research. Individual membership is free, and interested parties are encouraged to join the workshop's mailing list or attend the upcoming meetings.
03:30:00 The presenter introduces two projects - one led by co-data with RDA as a major partner, and another led by the consortium of 19 partners. The projects focus on making data accessible and interoperable, as well as improving the functions of national science systems.
03:35:00 The video discusses the VI Workshop on Data Science, which will focus on data interoperability and inner circle methodology. The presenter notes that data integration across disciplines is a challenge, but that with the growth of internet-based technologies, it has become easier to share data.
03:40:00 This video provides a tutorial on how to use data science tools, including how to cite data sources and how to create data management plans. It discusses the importance of data sharing and the need for data scientists to have a clear understanding of their data sources.
03:45:00 The speaker discusses the importance of data provenance, reproducibility, and attribution when working with data. She also recommends timestamping changes to data sets.
03:50:00 This video is a workshop on data science. The presenter explains that morning light is the best time to work because it's the least disruptive. Afterwards, she provides passwords to access the internet so that the students can continue working after lunch.
03:55:00 The video teaches how to use data science to analyze and understand data.
This video provides a tutorial on data science, with a focus on the importance of foreign language skills. The presenter discusses the importance of data analysis and discusses how to effectively use foreign language skills in that process.
04:00:00 This workshop discusses the basics of data science and covers topics such as data preprocessing, data analysis, and machine learning.
04:05:00 This video provides a tutorial on data science, with a focus on the importance of foreign language skills. The presenter discusses the importance of data analysis and discusses how to effectively use foreign language skills in that process.
04:10:00 In this workshop, participants learn about data science techniques and concepts, and how to apply them to real-world problems.
04:15:00 This video introduces the viewers to the data science workshop that was held on May 10th. The workshop consisted of lectures and interactive exercises that covered topics such as data preprocessing, data analysis, and machine learning.
04:20:00 In this video, VI Workshop instructor, Yehuda Lind, discusses data science. He starts by discussing the different types of data and how to collect it. He then goes on to describe how to analyze data and how to find patterns. Lind finishes the video with a discussion of machine learning and how to use it in data science.
04:25:00 This video provides a workshop on data science with five different examples.
04:30:00 This video provides a workshop on data science, with examples of how to use different tools and techniques.
04:35:00 This video presents a workshop on data science, with specific focus on working with foreign data.
04:40:00 In this workshop, participants learn the basics of data science, including how to analyze data, create models, and interpret results.
04:45:00 This video provides a workshop on data science for foreign students.
04:50:00 This workshop provides an overview of data science, including techniques for data acquisition, data analysis, and data visualization.
04:55:00 In this workshop, participants learn how to use data science techniques to analyze and understand data from a foreign language.
This video provides a workshop on data science, with a focus on the use of foreign languages. The presenter discusses a deep learning methodology for estimating poverty using socio-economic data and satellite images. They note that the methodology is generalizable and avoids overlap between data sets.
05:00:00 The video presents a workshop on data science, with emphasis on how to use data from foreign sources.
05:05:00 This video provides a workshop on data science for foreign students. The workshop discusses the basics of data science, including the importance of data, the different types of data, and how to use data to solve problems.
05:10:00 The video presents a workshop on data science, with a focus on the use of foreign languages.
05:15:00 This 1-paragraph summary provides a brief overview of the video, which covers the use of deep learning to estimate poverty from remote sensing data.
05:20:00 The presenter describes a deep learning methodology for estimating poverty using socio-economic data and satellite images. They note that the methodology is generalizable and avoids overlap between data sets.
05:25:00 The video demonstrates a data science workshop, discussing methods for training a deep learning model using Landsat data. The workshop explains how to install necessary packages, split the Landsat data into train and test sets, and train the model using a deep learning algorithm.
05:30:00 This 1-minute video introduces the VI Workshop on Data Science, which covers different methods for data analysis and machine learning. The presenter demonstrates a data set of images of villages in Africa. One method used is to split the data into two parts based on the distance between the villages. A deep learning pipeline is then created using different parameters, including number of epochs and optimization method. The presenter concludes the video with a case study of a river in Brazil.
05:35:00 This video provides a brief introduction to data science, including the use of census data in Brazil. It then goes on to show how to use census data to calculate three indicators of well-being: longevity, literacy, and income longevity. Finally, the presenter provides a link to a GitHub repository with the code for the exercises.
05:40:00 This video covers the steps involved in data science, including data set selection, data analysis, and plotting. The video also provides a brief introduction to deep learning.
05:45:00 The video discusses the use of data sets for data science. It covers the use of different libraries for data analysis, and explains how to download the data set.
05:50:00 The video discusses the need for data sets that include information about the geographic coordinates and year of images that will be used for training a deep learning model. The data set used in the video is Brazil 2017 images, latitude and longitude, type of image (urban or rural), and income literacy.
05:55:00 In this video, the presenter presents a data science workshop on VI. They discuss the study of municipalities in Puerto Rico by using various data sets. They explain how to use the Deep Learning toolkit and the Earth Engine API to create maps and clusters of data.
The video discusses the VI Workshop on Data Science, which is a workshop that focuses on data analysis and visualization. The video provides an overview of the workshop, including the accommodations and activities that will be available for participants. Additionally, the video discusses the importance of research data and provides a tutorial on how to create an ontology.
06:00:00 In this video, aVI workshop instructor Janet Biehl walks through the process of creating an authentication token for use with the Google Earth engine. Once the token has been generated, the instructor discusses how to use it in conjunction with the engine's API.
06:05:00 In this YouTube video, a VI Workshop on Data Science instructor discusses the requirements for the Google Earth Engine and how to use it to generate satellite images. The video also shows how to use the console to run tasks.
06:10:00 The presenter provides a brief overview of the data science workshop, which includes a description of the tasks that have been downloaded and their corresponding deadlines. Additionally, the presenter provides instructions on how to contact them if you have any questions or would like to work on a future project together.
06:15:00 In this workshop, participants learn about data science concepts and techniques.
06:20:00 This video provides a workshop on data science, with particular emphasis on data analysis and visualization.
06:25:00 The video discusses the VI Workshop on Data Science, which will take place at the Interval State Park in Sao Paulo, Brazil. The workshop will focus on data analysis and will include presentations by experts in the field. The park is located in the heart of the Atlantic Forest, one of the most biodiverse regions on Earth.
06:30:00 The presenter discusses the accommodations available for participants in the VI Workshop on Data Science, which will be held in the Atlantic Forest of Sao Paulo, Brazil. The presenter advises participants to bring appropriate shoes and clothes, as well as a bottle of water and a backpack. The presenter warns participants to avoid drinking alcohol in the park and to be aware of the hours they should leave the park in order to avoid delays.
06:35:00 The video discusses the importance of research data and how it can be archived and published. It also discusses the need for controlled vocabularies when working with research data, and provides an overview of the value of a research data repository.
06:40:00 In this video, a workshop presenter discusses ways to find appropriate data sets for research purposes. He lists features to look for in a data repository, including a DOI, revision control, and measurement level metadata. He also discusses control vocabularies, which are used to label and categorize data. Finally, the presenter provides a brief overview of data set search engines, which can be used to find specific data sets.
06:45:00 The video discusses how vocabularies can be used to speed up searches and improve recall and precision. It explains that ontologies are a mechanism for representing knowledge that is both human and machine readable. Ontologies are built from rdf graphs, which allow for the linking of collections of data.
06:50:00 This video provides a tutorial on data science, including an introduction to Tower in Paris, the Louvre, and Mona Lisa, and how data can be linked to real-world objects. The tutorial then covers how to create an ontology, or schema of terms, to describe environmental data.
06:55:00 The video discusses the need for ontology in data science, and shows how ontology can be used to tag data sets and make them more accessible. The presenter goes on to say that one of the challenges in doing ontology is that it is difficult to determine which terms belong to which ontologies.
This video discusses the challenges of data set compatibility and how various groups in the world are approaching the problem in different ways. It also discusses the success of a paper that used data from a variety of sources.
07:00:00 The presenter discusses the reproducibility of parsec experiments, and discusses the data management and technical aspects of the project.
07:05:00 The video discusses the importance of data recording and reproducibility, and provides recommendations for doing so.
07:10:00 The video introduces the idea of a data map, which is a tool used to manage and integrate data from various sources. The video also discusses how the data map is used to facilitate data exploration and data management.
07:15:00 The video discusses how to use APIs to access data sets and demonstrates how to do this using a web application and a back end in Python.
07:20:00 In this video, Kyle, one of the students advised by Professor Pedro into a master degree in data science, demonstrates a quick sneak peak on a preliminary paint work he is doing to get a taste of what Professor Pedro was talking about. The goal of this data mapping project is to create a centralized space where multiple data sets can be accessed.
07:25:00 The video presents a workshop on data science, in which various data sets are used as examples. The presenter discusses how the data is accessible, searchable, and citeable. Additionally, the presenter discusses how they outreach ecologists in order to get them up to date on the latest data science methods.
07:30:00 This video provides an overview of data science and its related tools, including how to use data sets in a way that is compatible with other repositories.
07:35:00 This video introduces Jesse Jo, one of the research assistants on the Parsec project, and Alec Bayarski, another research assistant on the Parsec project. They discuss the paper they are writing, which focuses on selecting socio-economic indicators for protected area evaluation that are globally and nationally relevant. They explain that the paper is motivated by the need to address the different needs for selecting indicators for monitoring and evaluation purposes versus more targeted evaluation research. They describe the conventional biological diversity goals as a framework for addressing this problem. They go on to explain that selecting indicators can often be country context specific, and that the paper is motivated by the need to address the different needs for selecting indicators. They finish the video by discussing how indicators are selected for monitoring and evaluation purposes, and describing the two types of indicators that are most commonly used.
07:40:00 This video discusses the research question of how to evaluate progress on the CBD gold Target 14, which is to protect areas and improve the benefits to people. The video discusses the different socioeconomic indicators that have been used in the past and how they can be used to assess progress. The video also discusses how countries are currently measuring progress towards the goal.
07:45:00 The authors of this paper discuss the trade-offs between using more globally-evaluated indicators and more country-specific indicators when measuring CBD gold progress. They hope to develop a framework for making better choices in future CBD gold assessments.
07:50:00 The presenter discusses the difficulty of assessing the effects of protected areas on local communities, and how policymakers need to be aware of the different data available. He also encourages researchers and evaluators to think about what they want to measure and how to measure it effectively.
07:55:00 The video discusses the challenges of data set compatibility, and how various groups in the world are approaching it in different ways. It also discusses the success of a paper that used data from a variety of sources.
The video discusses the importance of data quality and reproducibility when performing deep learning experiments. It provides a checklist of things to check when planning or conducting an experiment, such as ensuring the data set is complete, accessible, and reusable. It also provides recommendations for how to improve reproducibility in deep learning experiments, such as by searching for workflows and implementing best practices.
08:00:00 The video discusses the differences between the World Database of Protected Areas (WDPA) and the Australian government's Protected Area Database (PAD). WDPA contains data on more protected areas, but PAD contains data on larger, more representative areas. WDPA also includes data on protected areas that have disappeared, while PAD does not. WDPA and PAD have different methods of dividing protected areas up by population density, which can create discrepancies. WDPA is an important tool for data scientists working on projects within countries, but it may not always be accurate due to discrepancies between the data provided by different countries.
08:05:00 The workshop discusses the flaws of the World Heritage Property Status Assessment (WGPA) and how to improve it. One participant writes a paper on the topic, and others provide commentary. The workshop concludes with a discussion of how to harmonize data from different sources and protect it from being cleaned or tampered with.
08:10:00 This video discusses how researchers should try to reproduce their work, and how readers should help by replicating experiments.
08:15:00 Data science is a challenging field, with many difficulties in reproducing experiments. In this video, workshop presenter VI explains the many challenges in reproducing experiments, including incompatibilities between software, variations in data sets, and difficulties in training models. This problem is particularly difficult in deep learning, where large data sets and complicated specifications can lead to many failed experiments.
08:20:00 The video discusses the importance of data quality and reproducibility when performing deep learning experiments. It provides a checklist of things to check when planning or conducting an experiment, such as ensuring the data set is complete, accessible, and reusable. It also provides recommendations for how to improve reproducibility in deep learning experiments, such as by searching for workflows and implementing best practices.
08:25:00 The video introduces the workshop, which will discuss ways to improve reproducibility in scientific research. Three awards will be given, and the first award goes to Rose for her work on deep learning methodology for predicting socio-economic indicators from satellite imagery. Queen gives a short speech thanking the attendees and presenting the award to her. Foreign gives a short speech about their work on evaluation of machine learning models for species distribution modeling in the Amazon.
08:30:00 This video presents the winners of the individual Awards section of the best poster Awards. Renato, a student who is not even in the Master of Science course yet, is awarded for his work on characterizing deforestation. Janet, another student, is given the award for her work on data science.