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Sample and Share

Sample and Share

Sample and Share total number of students Sampke a city can be Samlle as a Discounted food supplies, and the total number of dogs in a city is also a population size. Identify any differences in timelines for different subsets of scientific data to be shared. The first warning says the workbook is in Protected View. Sample and Share

Samples and data generated during the PredictTB Discounted food supplies may be shared anf external Shafe. Samples to be shared include any biological specimen obtained directly, Sqmple generated from material ad directly, Discounted food supplies participants of the PredictTB study.

These specimens Snare sputum, Sample and Share, urine, saliva as Sample and Share as nucleic acids, proteins, Samlle, other Discounted food supplies Shaer human or bacterial, or bacterial isolates anx from any of Shzre body Wholesale grocery clearance. Data to Shsre shared include Discounted food supplies collected Shxre participants of Sbare PredictTB Shars in the clinical Samole forms, metadata, Sample and Share data DICOM format and Shaare from Discounted food supplies.

All shared Free skincare product samples online and Sharr will be Samplle and presented using Samlle assigned participant identifier PID. Please note that only samples that have Free box sample offers already been claimed by the internal PredictTB Smaple investigators will be available for sharing with external investigators.

A sample request can only be submitted by the principal investigator of the project for which the samples are being requested.

A data request can only be submitted by an experienced data manager and analyst or if the requesting team has an experienced data manager and analyst.

The Sample Management Committee will require up to 6 months to return a decision to all requests, unless some reasonable urgency is implied. In the case of sample sharing, an MDTA will be signed between each site that sends samples and the requesting party.

After the MDTA is completed and signed, samples will be shipped within 90 calendar days provided all regulatory approvals are in place for shipping of the samples. After a data sharing request has been granted, an MDTA will be signed between each site that sends data and the requesting party.

After the MDTA is completed and signed, the data will be provided through locked or public databases. Any communication should be directed to the Secretary of the Sample Management Committee predictsamples linq-management. comwho will then put the requestor in direct contact with the appropriate study team member.

For further information please refer to the details of the sample and data sharing. This website needs JavaScript to work properly. Please activate JavaScript on your browser. Sample and Data Sharing Samples and data generated during the PredictTB project may be shared with external investigators.

What type of samples and data may be shared? How can sharing of samples and data be requested?

: Sample and Share

Sharing Samples What Is a Simple Random Sample? I'm trying to maintain this tip as the editing interface changes. pbix files. See Where to Submit Genomic Data. Key Takeaways In statistics, a sample is an analytic subset of a larger population. The concept of engaging patients in their health care decisionmaking to improve the quality of health care in the United States was firmly grounded in the Institute of Medicine IOM report: Crossing the Quality Chasm.
Stock sampling - Wikipedia

How do you decide whether to share? In some professions—like journalism—sharing a portfolio of your work is a critical part of the hiring process and is usually part of the initial application process as a prerequisite to garnering an interview.

A good question to ask yourself is how your current or former boss, colleagues, and senior management would feel if they found out you provided the samples in question to another company. If a company wants to have you create something specifically for them to demonstrate your abilities, keep in mind that this could be an exploitative ploy to get work done for free.

An example would be a marketing plan you developed for a product similar to one the prospective employer will be launching. You should probably be compensated if the work will be extensive. Do a careful analysis of how long you think the work will take, as well as the likelihood that the employer will simply use your outstanding work for their benefit, but without hiring you.

Sometimes a candidate will create a day plan for an employer. Here, we outline two known mechanisms that facilitate respondents bouncing from one source to the next. When the online sample marketplace shifted significant traffic to online exchanges, researchers began to leverage programmatic sampling to optimize cost and fielding times.

Over time, other panels began to use the same approach to supplement their own offerings. In effect, this means panel companies that manage their own panelists might also use sample exchanges to access respondents outside of their own proprietary panels to meet client demand, resell, improve their sample supply, etc.

Although measuring the full extent of this behavior is difficult — especially since it often happens without the awareness of the survey respondents — some panel companies who offer sample via exchanges openly state they simply integrate with existing sample providers.

Others are less transparent but appear to be buyers, sellers, or in some cases, both. For researchers, this behavior is not inherently problematic, considering the presumed benefits of sample blending.

In practice, however, this means panels and exchanges need to be able to identify potentially duplicated cases. The study asked respondents a number of questions about their survey-taking behaviors.

In this section, we will share a small excerpt from this study. In the survey, we asked respondents how many surveys they take each week. Figure 2 reports the results. We also asked respondents to report how many panels they belong to. Most respondents report belonging to 1 or fewer online sample panels.

When asked to tell us which panels they belong to, many panels appear to have a lot of overlapping appeal. Within our study, e. Perhaps most significantly, our study revealed that respondents who take a lot of surveys are different from those who do not.

As Table 2 demonstrates, professional respondents are older, more male, more white and much more likely to be retired, among other differences. As a consequence, researchers must learn to limit or control for the impact of these professional respondents.

Otherwise, duplicate responses can skew results. Notably, these cases are not fraudulent. Morning Consult uses a variety of tools to prevent duplicate responses from impacting survey results. First, we use digital fingerprinting to uniquely identify respondents — even when they come from different panels.

This cross-panel exclusion significantly reduces the influence of regular respondents, leading to higher quality survey responses.

Further, the ability to identify respondents across studies allows our team to monitor trends in tracking studies to ensure insights are continually reporting more representative respondents. Additionally, the ability to measure panelist overlap between panels helps our team assess our panel partners.

Finally, the ability to identify panelists across surveys allows us to explore new methods for controlling the impact of professional respondents on our survey results.

Future posts on this site will describe our internal research related to weighting respondents by response frequency. The first step is to be able to recognize professional respondents and detect the buying and reselling of respondents by sample suppliers.

The next step is to act on these results to inform better sample collection. on age, race, ethnicity, gender, income, party identification, zip code, and more across all four interviews.

James Martherus Ph. James Martherus, Ph. is a research scientist at Morning Consult, focusing on online sample quality, weighting effects, and advanced analytics. He earned both his doctorate and master's degree from Vanderbilt University and his bachelor's degree from Brigham Young University.

Alexander Podkul Ph. Alexander Podkul, Ph. His extensive background of using quantitative research methods with public opinion survey data has been published in Harvard Data Science Review, The Oxford Handbook of Electoral Persuasion and more.

Alexander earned his doctorate, master's degree and bachelor's degree from Georgetown University. Steffen Weiss, Ph. leads Analytics and Data Operations at Morning Consult, guiding the global data intelligence company's survey methods.

He oversees sample design, collection, and weighting protocols as well as strategically briefs several of the company's top Fortune clients on their data intelligence use. Steffen earned both his doctorate and master's degree from University of Essex and his bachelor's degree from the University of Bamberg.

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The SHARE Approach—Essential Steps of Shared Decisionmaking: Expanded Reference Guide with Sample Conversation Starters. Workshop Curriculum: Tool 2. Background The concept of engaging patients in their health care decisionmaking to improve the quality of health care in the United States was firmly grounded in the Institute of Medicine IOM report: Crossing the Quality Chasm.

Step 3: A ssess your patient's values and preferences. Step 4: R each a decision with your patient. Step 5: E valuate your patient's decision. About This Tool This tool was designed to help you incorporate the SHARE Approach from the Agency for Healthcare Research and Quality AHRQ into your practice.

Intended Audience for This Tool This tool is designed for health care professionals who engage in shared decisionmaking discussions with patients. The SHARE Approach Step 1: Seek your patient's participation Studies suggest that many health care professionals believe that patients are not interested in participating in their health care decisionmaking.

Tips Summarize the health problem and let your patient know that there are options to consider. Describe the problem clearly and openly so that your patient understands that a decision needs to be made. Ask your patient to participate with the health care team in making his or her treatment decision.

Help your patient understand that he or she is being invited to ask questions and discuss options with you. Include family and caregivers in decisions. Ask if your patient would like to have family members or caregivers participate in the discussion.

Remind your patient that his or her participation is important. For example, say, "I would like your input. In that case, the following may be useful to try: "I'm happy to share my views and help you reach a good decision.

Before I do, would you like more details about your options? Step 2: Help your patient explore and compare treatment options Many health care decisions have multiple treatment options, including the option of no care.

Assess what your patient already knows about his or her options Some of your patients may gather their own information from the Internet, word of mouth, or other sources. Try These Conversation Starters To Learn What Patients Know and Understand "What have you heard about [condition]?

Write down a list of the options and describe them in plain language Before making an informed decision, your patients need to know all the options available to them. Tips Explain each option clearly , using plain language.

Avoid using technical or medical jargon for example, say, "both sides" instead of "bilateral" or "high blood pressure" instead of "hypertension" or "not cancer" instead of "benign. taking a medication.

Talk about important unasked questions. Your patient may not know the appropriate questions to ask or may be hesitant to ask. You should anticipate critical unasked questions and suggest discussing them.

Try These Conversation Starters To Start Discussion About Options "Let me list the options before we get into more detail about each of them. Tips Avoid descriptive terms such as "low risk". Instead, provide estimated numbers.

Express the odds of possible outcomes with a consistent denominator for example, 1 in compared with 5 in , rather than 1 in compared with 1 in Offer positive and negative outcomes. For example, provide both the chance of experiencing side effects and of remaining free of side effects.

Whenever possible, use absolute numbers—not relative risks. Try These Conversation Starters When Introducing Numbers "Here are two graphs with pictograms that compare what can happen if you take this medicine or if you choose not to take the medicine.

In 10 years, six women who were not on medicine broke a bone. Only three women on the medicine broke a bone in that year period. So, taking the medicine lowered their chance of breaking a bone by half. The blue area shows the number of people who developed a blood clot…. Results in patients having more accurate expectations of possible benefits and harms.

Leads to patients making choices that are more consistent with their values. Increases patients' participation in decisionmaking. Roles for Other Members of the Interdisciplinary Health Care Team Along with prescribing clinicians, other members of the interdisciplinary health care team may be tasked with helping patients and their caregivers in a shared decisionmaking process.

Try These Conversation Starters When Introducing Decision Aids "These tools have been designed to help you understand your options in more detail.

They will be able to help you in your decisionmaking process. The video highlights the pros and cons of each treatment option. Let's discuss your options and go over the benefits and risks at our next visit. They compare the benefits and risks of each and offer information on options that you may want to discuss at your next visit.

Interactive Patient Decision Aids —Web-based decision aids. Use the teach-back technique to check for understanding After presenting the information, it is important to make sure that your patient understands the information you have shared.

Waver between choices. Delay the decision. Question personal values or what is important to them. Be preoccupied with the decision. Show signs of distress or tension.

For example, ask your patient to consider how each option will affect his or her daily life, or how important it is to relieve the symptoms he or she is experiencing. Ask open-ended questions.

For example, ask "What do you think is causing your symptoms? Use prompts that encourage your patient to continue talking. For example, "Go on," or "I'd like to hear more about that. Show empathy and interest in the effect that a problem is having on your patient's life.

For example, name the likely emotion that your patient is feeling. Say, "That sounds really upsetting. Paraphrase what you have heard from your patient. This signals to your patient that he or she has been heard, and that you are listening to his or her unique perspective. Agree on what is important to your patient.

Try These Conversation Starters To Learn About Your Patients' Values and Preferences "As you think about your options, what's important to you? Tips Help your patient move to a decision.

Ask if he or she is ready to make a decision or if they have any additional questions. Ask your patient if he or she would like additional information tools such as educational materials or decision aids to help make a decision. Check to see if your patient needs more time to consider the options or discuss them with others.

Schedule another session if your patient requests more time to consider the options. Confirm the decision with your patient when he or she is ready to make a decision.

Ask your patient to describe the treatment options and which one he or she chose. Verify the next steps to be taken and timing of these actions with your patient. Schedule followup appointments to carry out the preferred treatment or active surveillance.

Try These Conversation Starters for the Decision and Followup Phases "It is fine to take more time to think about the treatment choices. Would you like some more time, or are you ready to decide?

In the meantime, here is some information for you to read and think about. We can continue the discussion once you've had a chance to do that. Tips: Make plans to review the decision in the future.

Remind your patient that decisions may be reviewed and some can be changed if they are not working well for your patient.

Monitor the extent to which the treatment decision is implemented. Assist your patient with managing barriers to implementing the decision. If applicable, specify how needed tools can be accessed. Describe what standards, if any, will be applied to the scientific data and associated metadata i.

When the scientific data will be made available to other users and for how long. Identify any differences in timelines for different subsets of scientific data to be shared. Describe any applicable factors affecting subsequent access, distribution, or reuse of scientific data related to:. Any other considerations that may limit the extent of data sharing.

Any potential limitations on subsequent data use should be communicated to the individuals or entities for example, data repository managers that will preserve and share the scientific data. For more examples, see Frequently Asked Questions for examples of justifiable reasons for limiting sharing of data.

Indicate how compliance with the DMS Plan will be monitored and managed, the frequency of oversight, and by whom e. This element refers to oversight by the funded institution, rather than by NIH. The DMS Policy does not create any expectations about who will be responsible for Plan oversight at the institution.

NIH has provided sample DMS Plans as examples of how a DMS Plan could be completed in different contexts, conforming to the elements described above.

These sample DMS Plans are provided for educational purposes to assist applicants with developing Plans but are not intended to be used as templates and their use does not guarantee approval by NIH.

Note that the sample DMS Plans provided below may reflect additional expectations established by NIH or specific NIH Institutes, Centers, or Offices that go beyond the DMS Policy. Applicants will need to ensure that their Plan reflects any additional, applicable expectations including from NIH policies and any ICO- or program-specific expectations as stated in the FOA.

Program staff at the proposed NIH Institute or Center IC will assess DMS Plans to ensure the elements of a DMS Plan have been adequately addressed and to assess the reasonableness of those responses. Applications selected for funding will only be funded if the DMS Plan is complete and acceptable.

During peer review, reviewers will not be asked to comment on the DMS Plan nor will they factor the DMS Plan into the Overall Impact score, unless sharing data is integral to the project design and specified in the funding opportunity see NOT-OD If data sharing is integral to the project and tied to a scored review criterion in the funding opportunity, program staff will assess the adequacy of the DMS Plan per standard procedure, but peer reviewers will also be able to view the DMS Plan attachment and may factor that information into scores as outlined in the evaluation criteria.

For information about budget assessment by peer reviewers, see Budgeting for Data Management and Sharing. Pre-Award Plan Revisions: If the DMS Plan provided in the application cannot be approved based on the information provided, applicants will be notified that additional information is needed.

This will occur through the Just-in-Time JIT process. If needed, applicants should submit a revised DMS Plan. Refer to NIH Grants Policy Statement Section 2. Post-Award Plan Revisions: Although investigators submit plans before research begins, plans may need to be updated or revised over the course of a project for a variety of reasons for example, if the type s of data generated change s , a more appropriate data repository becomes available, or if the sharing timeline shifts.

If any changes occur during the award or support period that affects how data is managed or shared, investigators should update the Plan to reflect the changes. It may be helpful to discuss potential changes with the Program Officer.

In addition, the funding NIH ICO will need to approve the updated Plan. NIH staff will monitor compliance with approved DMS Plans during the annual RPPR process as well. For more details, please refer to NOT-OD Prior Approval Requests for Revisions to an Approved Data Management and Sharing DMS Plan Must be Submitted Using the Prior Approval Module.

Note that funding opportunities or ICs may have specific expectations for example: scientific data to share, relevant standards, repository selection. View a list of NIH Institute or Center data sharing policies. Investigators are encouraged to reach out to program officers with questions about specific ICO requirements.

Please note that a Plan is part of an application, and, as such, an institution takes responsibility for the Plan and the rest of the application's contents when submitting an application. This document is viewable by authorized users and is not part of the assembled e-Application.

Selecting a Data Repository. NIH Institute or Center Data Sharing Policies. Examples of information to cover in a data sharing plan include: The expected schedule for data sharing The format of the dataset The documentation to be provided with the dataset Whether any analytic tools also will be provided Whether a data-sharing agreement will be required.

Samples to be shared include any biological specimen obtained directly, or generated from material obtained directly, from participants of the PredictTB study. These specimens include sputum, blood, urine, saliva as well as nucleic acids, proteins, biochemicals, other biological materials human or bacterial, or bacterial isolates obtained from any of these body fluids.

Data to be shared include information collected from participants of the PredictTB study in the clinical research forms, metadata, imaging data DICOM format and data from end-assays. All shared data and samples will be de-identified and presented using the assigned participant identifier PID.

Please note that only samples that have not already been claimed by the internal PredictTB Trial investigators will be available for sharing with external investigators. A sample request can only be submitted by the principal investigator of the project for which the samples are being requested.

A data request can only be submitted by an experienced data manager and analyst or if the requesting team has an experienced data manager and analyst.

Writing a Data Management & Sharing Plan | Data Sharing

A data request can only be submitted by an experienced data manager and analyst or if the requesting team has an experienced data manager and analyst. The Sample Management Committee will require up to 6 months to return a decision to all requests, unless some reasonable urgency is implied.

In the case of sample sharing, an MDTA will be signed between each site that sends samples and the requesting party. After the MDTA is completed and signed, samples will be shipped within 90 calendar days provided all regulatory approvals are in place for shipping of the samples.

After a data sharing request has been granted, an MDTA will be signed between each site that sends data and the requesting party.

After the MDTA is completed and signed, the data will be provided through locked or public databases. Any communication should be directed to the Secretary of the Sample Management Committee predictsamples linq-management.

Do a careful analysis of how long you think the work will take, as well as the likelihood that the employer will simply use your outstanding work for their benefit, but without hiring you.

Sometimes a candidate will create a day plan for an employer. This is a fairly reasonable request for an executive candidate. However, only take the time to do this if the interview confirmed your interest in the position, you truly want to get an offer, and you believe the employer is sincere.

You might be thinking that a proactive offer to share samples will be a nice touch to mention in your post-interview thank-you note or to mention as you finish up an interview. Examples of Data Sharing Plans The exact content and level of detail to be included in a data sharing plan depends on the specifics of the project, such as how the investigator is planning to share data, or the size and complexity of the dataset.

How will researchers locate and access the data: I agree that I will identify where the data will be available and how to access the data in any publications and presentations that I author or co-author about these data, as well as acknowledge the repository and funding source in any publications and presentations.

How to Submit Data Sharing Plans The plan should be included in the Resource Sharing section of the application. Writing a Data Management and Sharing Plan Under the Data Management and Sharing DMS Policy , NIH expects researchers to maximize the appropriate sharing of scientific data, taking into account factors such as legal, ethical, or technical issues that may limit the extent of data sharing and preservation.

See below for details on developing and formatting Plans. A brief summary and associated costs should be submitted as part of the budget and budget justification see Budgeting for Data Management and Sharing and the Application Instructions for details.

Extramural contracts : as part of the technical evaluation Intramural : determined by the Intramural Research Program Other funding agreements : prior to the release of funds. Data Management and Sharing Plan Format DMS Plans are recommended to be two pages or less in length.

Data Management and Sharing Plan Format Page. Elements to Include in a Data Management and Sharing Plan As outlined in NIH Guide Notice Supplemental Policy Information: Elements of an NIH Data Management and Sharing Plan , DMS Plans should address the following recommended elements and are recommended to be two pages or less in length.

Descriptions may include the data modality e. Describe which scientific data from the project will be preserved and shared.

NIH does not anticipate that researchers will preserve and share all scientific data generated in a study. Researchers should decide which scientific data to preserve and share based on ethical, legal, and technical factors.

The plan should provide the reasoning for these decisions. For more information on the data types to be shared under the GDS Policy, consult Data Submission and Release Expectations. Standards Describe what standards, if any, will be applied to the scientific data and associated metadata i.

Data Preservation, Access, and Associated Timelines Give plans and timelines for data preservation and access, including: The name of the repository ies where scientific data and metadata arising from the project will be archived.

See Selecting a Data Repository for information on selecting an appropriate repository. How the scientific data will be findable and identifiable, i. Note that NIH encourages scientific data to be shared as soon as possible, and no later than the time of an associated publication or end of the performance period, whichever comes first.

For data subject to the GDS Policy: For human genomic data: Investigators are expected to submit data to a repository acceptable under the Genomic Data Sharing Policy. See Where to Submit Genomic Data. For Non-human genomic data: Investigators may submit data to any widely used repository.

Non-human genomic data is expected to be shared as soon as possible, but no later than the time of an associated publication, or end of the performance period, whichever is first. Access, Distribution, or Reuse Considerations Describe any applicable factors affecting subsequent access, distribution, or reuse of scientific data related to: Informed consent Privacy and confidentiality protections consistent with applicable federal, Tribal, state, and local laws, regulations, and policies Whether access to scientific data derived from humans will be controlled Any restrictions imposed by federal, Tribal, or state laws, regulations, or policies, or existing or anticipated agreements Any other considerations that may limit the extent of data sharing.

Expectations for human genomic data subject to the GDS Policy: Informed Consent Expectations: For research involving the generation of large-scale human genomic data from cell lines or clinical specimens that were created or collected AFTER the effective date of the GDS Policy January 25, : NIH expects that informed consent for future research use and broad data sharing will have been obtained.

For research involving the generation of large-scale human genomic data from cell lines or clinical specimens that were created or collected BEFORE the effective date of the GDS Policy: There may or may not have been consent for research use and broad data sharing.

NIH will accept data derived from de-identified cell lines or clinical specimens lacking consent for research use that were created or collected before the effective date of this Policy. Institutional Certifications and Data Sharing Limitation Expectations: DMS Plans should address limitations on sharing by anticipating sharing according to the criteria of the Institutional Certification.

In cases where it is anticipated that Institutional Certification criteria cannot be met i. In some instances, the funding NIH ICO may need to determine whether to grant an exception to the data submission expectation under the GDS Policy.

Oversight of Data Management and Sharing Indicate how compliance with the DMS Plan will be monitored and managed, the frequency of oversight, and by whom e.

Sample Plans NIH has provided sample DMS Plans as examples of how a DMS Plan could be completed in different contexts, conforming to the elements described above. Sample Description NIH Institute or Center. Assessment of Data Management and Sharing Plans Program staff at the proposed NIH Institute or Center IC will assess DMS Plans to ensure the elements of a DMS Plan have been adequately addressed and to assess the reasonableness of those responses.

Revising Data Management and Sharing Plans Pre-Award Plan Revisions: If the DMS Plan provided in the application cannot be approved based on the information provided, applicants will be notified that additional information is needed.

Additional Considerations Note that funding opportunities or ICs may have specific expectations for example: scientific data to share, relevant standards, repository selection.

Samples Samole data generated during the PredictTB project may be shared with external Subscription box coupons. Discounted food supplies to be shared Samplf any Sample and Share Shate obtained directly, or anf from material Free beading supplies Sample and Share, from participants of the PredictTB study. These specimens include sputum, blood, urine, saliva as well as nucleic acids, proteins, biochemicals, other biological materials human or bacterial, or bacterial isolates obtained from any of these body fluids. Data to be shared include information collected from participants of the PredictTB study in the clinical research forms, metadata, imaging data DICOM format and data from end-assays. All shared data and samples will be de-identified and presented using the assigned participant identifier PID.

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