Data Management Lab: Data mapping exercise exampleIUPUI
Spring 2014 Data Management Lab: Session 1 Data mapping exercise example (more details at http://ulib.iupui.edu/digitalscholarship/dataservices/datamgmtlab)
What you will learn:
1. Build awareness of research data management issues associated with digital data.
2. Introduce methods to address common data management issues and facilitate data integrity.
3. Introduce institutional resources supporting effective data management methods.
4. Build proficiency in applying these methods.
5. Build strategic skills that enable attendees to solve new data management problems.
Data Management Lab: Data mapping exercise instructionsIUPUI
Spring 2014 Data Management Lab: Session 1 Data mapping exercise instructions (more details at http://ulib.iupui.edu/digitalscholarship/dataservices/datamgmtlab)
What you will learn:
1. Build awareness of research data management issues associated with digital data.
2. Introduce methods to address common data management issues and facilitate data integrity.
3. Introduce institutional resources supporting effective data management methods.
4. Build proficiency in applying these methods.
5. Build strategic skills that enable attendees to solve new data management problems.
Data Management Lab: Data mapping exercise exampleIUPUI
Spring 2014 Data Management Lab: Session 1 Data mapping exercise example (more details at http://ulib.iupui.edu/digitalscholarship/dataservices/datamgmtlab)
What you will learn:
1. Build awareness of research data management issues associated with digital data.
2. Introduce methods to address common data management issues and facilitate data integrity.
3. Introduce institutional resources supporting effective data management methods.
4. Build proficiency in applying these methods.
5. Build strategic skills that enable attendees to solve new data management problems.
Data Management Lab: Data mapping exercise instructionsIUPUI
Spring 2014 Data Management Lab: Session 1 Data mapping exercise instructions (more details at http://ulib.iupui.edu/digitalscholarship/dataservices/datamgmtlab)
What you will learn:
1. Build awareness of research data management issues associated with digital data.
2. Introduce methods to address common data management issues and facilitate data integrity.
3. Introduce institutional resources supporting effective data management methods.
4. Build proficiency in applying these methods.
5. Build strategic skills that enable attendees to solve new data management problems.
For more classes visit
www.snaptutorial.com
Exam 1 Psych 355
3. A p level of 0.05 corresponds to a confidence level of __________%
4. In a within-groups design where one group is measured twice over time, the appropriate hypothesis test is an:
7. Why do we divide by N-1 rather than by N when estimating a population standard deviation from the sample standard deviation?
For more classes visit
www.snaptutorial.com
Exam 1 Psych 355
3. A p level of 0.05 corresponds to a confidence level of __________%
4. In a within-groups design where one group is measured twice over time, the appropriate hypothesis test is an:
7. Why do we divide by N-1 rather than by N when estimating a population standard deviation from the sample standard deviation?
Big Data: Big Opportunities or Big Trouble?Shea Swauger
Big data is changing how research is being conducted and allowing new kinds of questions to be asked. Meanwhile, data management has enabled a rapid increase in the dissemination and preservation of research products and many funding agencies like the National Science Foundation and National Institute of Health now require data management plans in their grant applications. The combination of big data applications and data management processes has created new opportunities and pitfalls for researchers. In the past year, prominent scientists including the Director of the NIH have suggested that inappropriate methodology for data acquisition, analysis and storage has led to a gap in the translation of basic research findings to clinical cures. In this session we will track data through all research stages, describe best practices and university resources available to faculty grappling with these important issues.
This presentation presents for the following purposes
1: It covers the chapter of Research Problem formulation in the subject Research methodology
2: Defining the research problem
3: Significance of the research problem
4: Necessity of the research problem
5: How to find out the research problem
6: Why research problem is very important
7: How a bad formulation of the research problem affects the project or research study
For more classes visit
www.snaptutorial.com
Exam 1 Psych 355
3. A p level of 0.05 corresponds to a confidence level of __________%
4. In a within-groups design where one group is measured twice over time, the appropriate hypothesis test is an:
7. Why do we divide by N-1 rather than by N when estimating a population standard deviation from the sample standard deviation?
For more classes visit
www.snaptutorial.com
Exam 1 Psych 355
3. A p level of 0.05 corresponds to a confidence level of __________%
4. In a within-groups design where one group is measured twice over time, the appropriate hypothesis test is an:
7. Why do we divide by N-1 rather than by N when estimating a population standard deviation from the sample standard deviation?
Big Data: Big Opportunities or Big Trouble?Shea Swauger
Big data is changing how research is being conducted and allowing new kinds of questions to be asked. Meanwhile, data management has enabled a rapid increase in the dissemination and preservation of research products and many funding agencies like the National Science Foundation and National Institute of Health now require data management plans in their grant applications. The combination of big data applications and data management processes has created new opportunities and pitfalls for researchers. In the past year, prominent scientists including the Director of the NIH have suggested that inappropriate methodology for data acquisition, analysis and storage has led to a gap in the translation of basic research findings to clinical cures. In this session we will track data through all research stages, describe best practices and university resources available to faculty grappling with these important issues.
This presentation presents for the following purposes
1: It covers the chapter of Research Problem formulation in the subject Research methodology
2: Defining the research problem
3: Significance of the research problem
4: Necessity of the research problem
5: How to find out the research problem
6: Why research problem is very important
7: How a bad formulation of the research problem affects the project or research study
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Normative Research Director Rebecca Pardo gave a series of presentations as an internal "workshop" with the Normative team to discuss research in a design context. This is the first of the series.
Reply to students Reply to other classmates’ threads, providing .docxchris293
Reply to students
Reply to other classmates’ threads, providing commentary, feedback, suggested reading, or questions for consideration. Reply must be 250 words and provide 1 reference in APA format.
Student 1 Response
Let’s say that you are researching a topic that concerns all fifth-grade students in the United States. Trying to gather data for every fifth-grade student in the United States is not a feasible task. A researcher would not even have access to that many students. A generalization will be concluded about the population that is being studied and that generalization will need an adequate sampling in order to be valid. “The sample is the group of elements or a single element, from which data are obtained” (McMillan, 1996, p. 86). Sampling allows for the study of a part that represents a whole of a population and techniques are needed to ensure that the sample will lead to a valid conclusion. “Sampling techniques tell us how to select cases that can lead to valid generalizations about a population, or the entire group you wish to learn about” Check & Schutt, 2012, p. 91). The sample must be an ideal representation of the population that is being studied. The sample must share the same characteristics of those of the total population (Check & Schutt, 2012).
Before determining the sample, it is important to have a detailed description of the population characteristics that is to form the focus of the study. There are a variety of methods in determining sampling for educational research and the caliber of representatives is based on the sampling procedures used (McMillan, 1996). Check and Schutt (2012) state that an important distinction about samples is whether they are based on a probability or a nonprobability sampling method. When using the probability sampling, the researcher knows in advance the likelihood the any element of a population will be selected for the study (Check & Schutt, 2012). “Probability sampling is a method of sampling in which the subjects are selected randomly in such a way that the researcher knows the probability of selecting each member of the population” (McMillan, 1996, p. 87). The random selection reduces the chance of having systematic bias in the selection elements. Sampling methods that do not let the researcher know in advance the likelihood of selection is called nonprobability sampling methods (Check & Schutt, 2012). “Nonprobability sampling methods can be useful when random sampling is not possible, when a research question does not concern a larger population, and when a preliminary exploratory study is appropriate” (Check & Schutt, 2012, p. 112).
Probability sampling methods are further broken down into types of random sampling. There are four types of random sampling: simple random sampling, systematic random sampling, cluster sampling, and stratified random sampling. Some examples of nonprobability sampling methods include availability sampling, quota sampling, purposive sampling, and sno.
Presented at WiLSWorld Workshop Wednesday on August 3rd, 2016 by Joshua Morrill, Senior Information Processing Consultant, UW-Madison
Libraries gather and interpret data for a variety of purposes: to evaluate the content and accessibility of products bought for users, to understand community dynamics and demographics, to identify new services or improvements to existing ones, and much more. In the haystack of numbers available to library professionals, how do we identify the needles, and how do we polish them? This workshop will help you evaluate data quality and communicate it effectively to a variety of stakeholders.
Dataset Codebook BUS7105, Week 8 Name Source RepreseOllieShoresna
Dataset Codebook
BUS7105, Week 8
Name Source Representation Measurement Meaning
Subject’s Identification
Number
Qualtrics Identification
Number. Auto generated
by Qualtrics software.
Anonymous identification
of survey taker
N/A Sequential numbers in order
of survey taker completion.
Dataset organization
purposes only.
Gender Self-reported by survey-
taker:
Survey Question #1
Survey-taker gender
affiliation
Categorical,
Dichotomous
1 = Female
2 = Male
Age Self-reported by survey-
taker:
Survey Question #2
Survey-taker reported age
in years
Continuous, Scale Age in whole years.
Education Self-reported by survey-
taker:
Survey Question #3
Survey-taker education
level
Categorical, Nominal 1 = High School Completion
2 = Bachelor’s degree
Completion
3 = Master’s Degree
Completion
Personality Self-reported by survey-
taker:
Average of Survey
Questions: #4(Reverse
Scored), 5, 6, 7 (Reverse
Scored), 8, 9(Reverse
Scored)
Composite score of
Survey-taker degree of
introversion to
extroversion personality
traits.
Likert scale 1 – 7,
Interval*
1 = Survey Response: Highly
Disagree (Introvert)
To
7 = Highly Agree (Extrovert)
Job Satisfaction Self-reported by survey-
taker:
Average of Survey
Questions: #10, 11, 12, 13
Composite score of
Survey-taker satisfaction
with their current job.
Likert scale 1 – 10,
Interval
1 = Very Dissatisfied
To
10 = Very Satisfied
Engagement Self-reported by survey-
taker:
Average of Survey
Questions: #18, 19,
22(Reverse Scored)
Composite score of
Survey-taker engagement
in their current job.
Likert scale 1 – 7,
Interval*
1 = Survey Response: Almost
None of the Time (Very Low
Engagement)
To
7 = Survey Response: Almost
All of the Time (Very High
Engagement)
Trust in Leader Self-reported by survey-
taker:
Average of Survey
Questions: # 15, 16, 17,
21
Composite score of
Survey-taker trust in
direct leader in their
current job.
Likert scale 1 – 7,
Interval*
1 = Survey Response: Almost
None of the Time (Very Little
Trust in Leader)
To
7 = Survey Response: Almost
All of the Time (Great Deal of
Trust in Leader)
Motivation Self-reported by survey-
taker:
Average of Survey
Questions: #14 (Reverse
Scored), 20 (Reverse
Scored), 23, 24, 25
Composite score of
Survey-taker motivation
in performing their
current job.
Likert scale 1 – 7,
Interval*
1 = Survey Response: Almost
None of the Time (Not
Motivated At All)
To
7 = Survey Response: Almost
All of the Time (Highly
Motivation)
Intent to Quit Job Self-reported by survey-
taker:
Composite score of
Survey-taker intent to quit
their current job.
Likert scale 1 – 7,
Interval*
1 = Survey Response: Almost
None of the Time (High
Intent to Quit Job)
Average of Survey
Questions: #26, 27, 28
To
7 = Survey Response: Almost
All of the Time (Low Intent to ...
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StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
2. 1. Driven by a question or problem that
then guides the process.
2. Seeking information with a clear goal in
mind.
3. A process, which works best when done
step- by-step. The steps may need to be
repeated, as the process is reiterative.
4. Collection and interpretation of data in
an attempt to resolve the problem or
answer the question.
5. Going beyond facts and old ideas.
6. Taking a new look at the information and
taking a stand.
rpc.elm4you.org
3. 1. Just gathering information
2. Rearranging facts
3. Combining a paragraph from an
encyclopedia with a couple of
paragraphs from websites. That's
plagiarism.
4. Rewording each phrase and citing each
source. That's just a summary of facts
with someone else's name on them.
rpc.elm4you.org
5. 1. What is the overriding problem (in one sentence)?
2. What is the population and sample that are affected
by this problem?
3. What type of study will this be?
4. Will this study be qualitative or quantitative or
other?
5. What type of methodology will be used?
6. What type of data will be collected?
7. What possible outcomes are expected?
(Walden University)
6. The purpose section should be a simple
paragraph that describes the intent of your
study. It should flow directly from the
problem statement. Two to three sentences
are sufficient.
(Walden University)
7. The purpose of this _____ (quantitative, qualitative or
mixed-design) study is to (understand, describe,
develop, discover) the _____ (central focus for the
study) for ______ (the unit of analysis, person,
processes, groups, site).
Example: Study on Sickle Cell Disease and Attention
Deficits
The purpose of this quantitative study is to identify
attention deficits in children with sickle cell disease.
For this experimental study, attention deficits will be
assessed using measures of vigilance and impulsivity.
(Walden University)
8. The following slide is a template to help draft
the problem statement. It is a good place to
start and you can edit to meet your unique
needs. Remember problem statements have
many credible sources.
(Walden University)
9. There is a problem in ___________(societal organization). Despite
_________________ (something that should be happening)
___________ is occurring. This problem has negatively impacted
____________(victims of problem) because _________________.
(sources) A possible cause of this problem is ___________. (sources)
Perhaps a study which investigates ___________ by
________(method) could remedy the situation. _______ is
becoming an increasingly significant issue in education (reference(s))
(reference(s)) has demonstrated that ______ has become a more
significant issue in recent years, but the solution is unresolved.
In order to address _________, it is necessary to know more about
________. A study that uses ________ can help ____________.
(Walden University)
11. 1. Use mind mapping to narrow down
your topic.
2. Draw a line from the main issue
making that your sub topic
3. Draw a line from the subtopic to
make that your sub sub topic.