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Introduction: Although data sharing includes various advantages and benefits, researchers avoid sharing their data. The present study aims at investigating the barriers to and challenges of data sharing. Methodology: This study analyzed the literature of data sharing by searching related keywords in the field in scientific databases such as WoS, Scopus, PubMed, JSTOR, EBSCO and ProQuest without any time limitation till 1 July 2016. Findings: Barriers to and challenges of research data sharing are categorized into five groups: individual barriers including time, cost and devoted effort to manage and share data, losing publication opportunities, misuse and misinterpretation of data, motivational and behavioral barriers including the lack of required rewards and stimuli for data sharing and the absence of proper acknowledgment on behalf of those who reuse data, organizational barriers involving the lack of appropriate organizational support, the lack of proper financial and human resources, and the lack of and/or strict sharing policies of organizations, technical barriers including the lack of proper repositories, disuse of suitable data and metadata standards, as well as the low quality of data and language barriers, and  legal and legitimate barriers including data ownership, the right and level of use and accessibility of data, data sensitivity and considerations and the lack of knowledge about legal issues. Conclusion: In order to promote and enhance the research data sharing, identifying the barriers to and limitations in data sharing can be used as a framework for promoting and increasing data sharing.

In this project, we propose the Secure Data Sharing in Clouds sedasc methodology that provides data confidentiality and integrity access control data sharing forwarding without using compute intensive re encryption insider threat security and forward and backward access control. The Sedasc methodology encrypts a file with a single encryption key. Two different key shares for each of the users are generated, with the user only getting one share. The possession of a single share of a key allows the Sedasc methodology to counter the insider threats. The other key share is stored by a trusted third party, which is called the cryptographic server. We implement a working prototype of the Sedasc methodology and evaluate its performance based on the time consumed during various operations. Varsha K N | Prof Ganeshan M "A New Approach for Securely Sharing Data between Cloud Users with Dual Keys" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49459.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/49459/a-new-approach-for-securely-sharing-data-between-cloud-users-with-dual-keys/varsha-k-n

This study builds upon the cognitive model of post traumatic stress disorder developed by Ehlers and Clark (2000) and dual-representation memory theories expounded by Brewin and Burgess (2014) and Lau-Zhu et al. (2019; 2021). Perceptual, or data-driven, processing refers to bottom-up sensory impressions of the stimuli, such as its form and structure. In a traumatic memory, this processing captures the emotional valence and sensory detail in a ‘here and now’ perspective which is preserved and carried forward into the experience of intrusions, where it retraumatises the individual. Conceptual processing refers to organized top-down processing of the event and its subsequent integration into autobiographical context through elaboration and organization. It is this process that allows the individual to attach higher-order meaning to the event. The predominant processing style during the experience of trauma is thought to determine how the memories are encoded, and is therefore a pivotal factor in whether PTSD will develop. Trait and state anxiety are thought to play a role in determining affective and cognitive processing style at the time of trauma; however, research exploring this link is limited. This study examines the relationship between state anxiety and DDP. It is not known whether cognitive processing style is determined by heightened anxiety, or if cognitive processing style causes heightened anxiety, or if the relationship is bidirectional. Two experimental procedures have been devised with the aim of identifying this relationship. The first experimental phase tests the influence of induced processing style on anxiety levels, and the second phase is a test of the influence of induced anxiety on cognitive processing style. Brewin, C. R. & Burgess, N. (2014). Contextualisation in the revised dual representation theory of PTSD: A response to Pearson and colleagues. Journal of Behavior Therapy and Experimental Psychiatry, 45(1) 217–219. doi:10.1016/j.jbtep.2013.07.011. Ehlers, A. & Clark, D. M. (2000). A cognitive model of posttraumatic stress disorder. Behaviour research and therapy, 38(4) 319–345. Doi:10.1016/s0005-7967(99)00123-0. Lau-Zhu, A., Henson, R. N. & Holmes, E. A. (2019). Intrusive memories and voluntary memory of a trauma film: Differential effects of a cognitive interference task after encoding. Journal of Experimental Psychology: General, 148(12) 2154–2180. Doi:10.1037/xge0000598. Lau-Zhu, A., Henson, R. N. & Holmes, E. A. (2021). Selectively interfering with intrusive but not voluntary memories of a trauma film: Accounting for the role of associative memory. Clinical Psychological Science, 9(6) 1128–1143. doi:10.1177/2167702621998315.

This study builds upon the cognitive model of post traumatic stress disorder developed by Ehlers and Clark (2000) and dual-representation memory theories expounded by Brewin and Burgess (2014) and Lau-Zhu et al. (2019; 2021). Perceptual, or data-driven, processing refers to bottom-up sensory impressions of the stimuli, such as its form and structure. In a traumatic memory, this processing captures the emotional valence and sensory detail in a ‘here and now’ perspective which is preserved and carried forward into the experience of intrusions, where it retraumatises the individual. Conceptual processing refers to organized top-down processing of the event and its subsequent integration into autobiographical context through elaboration and organization. It is this process that allows the individual to attach higher-order meaning to the event. The predominant processing style during the experience of trauma is thought to determine how the memories are encoded, and is therefore a pivotal factor in whether PTSD will develop. Trait and state anxiety are thought to play a role in determining affective and cognitive processing style at the time of trauma; however, research exploring this link is limited. This study examines the relationship between state anxiety and DDP. It is not known whether cognitive processing style is determined by heightened anxiety, or if cognitive processing style causes heightened anxiety, or if the relationship is bidirectional. Two experimental procedures have been devised with the aim of identifying this relationship. The first experimental phase tests the influence of induced processing style on anxiety levels, and the second phase is a test of the influence of induced anxiety on cognitive processing style. Brewin, C. R. & Burgess, N. (2014). Contextualisation in the revised dual representation theory of PTSD: A response to Pearson and colleagues. Journal of Behavior Therapy and Experimental Psychiatry, 45(1) 217–219. doi:10.1016/j.jbtep.2013.07.011. Ehlers, A. & Clark, D. M. (2000). A cognitive model of posttraumatic stress disorder. Behaviour research and therapy, 38(4) 319–345. Doi:10.1016/s0005-7967(99)00123-0. Lau-Zhu, A., Henson, R. N. & Holmes, E. A. (2019). Intrusive memories and voluntary memory of a trauma film: Differential effects of a cognitive interference task after encoding. Journal of Experimental Psychology: General, 148(12) 2154–2180. Doi:10.1037/xge0000598. Lau-Zhu, A., Henson, R. N. & Holmes, E. A. (2021). Selectively interfering with intrusive but not voluntary memories of a trauma film: Accounting for the role of associative memory. Clinical Psychological Science, 9(6) 1128–1143. doi:10.1177/2167702621998315.

Traditional statistical methods for confidentiality protection of statistical databases do not scale well to deal with GWAS (genome-wide association studies) databases especially in terms of guarantees regarding protection from linkage to external information. The more recent concept of differential privacy, introduced by the cryptographic community, is an approach which provides a rigorous definition of privacy with meaningful privacy guarantees in the presence of arbitrary external information, although the guarantees come at a serious price in terms of data utility. Building on such notions, we propose new methods to release aggregate GWAS data without compromising an individual's privacy. We present methods for releasing differentially private minor allele frequencies, chi-square statistics and p-values. We compare these approaches on simulated data and on a GWAS study of canine hair length involving 685 dogs. We also propose a privacy-preserving method for finding genome-wide associations based on a differentially-private approach to penalized logistic regression.

This research intends to improve information dominance in the maritime domain by optimizing tactical mobile ad hoc network (MANET) systems for wireless sharing of biometric data in maritime interdiction operations (MIO). Current methods for sharing biometric data in MIO are unnecessarily slow and do not leverage wireless networks at the tactical edge to maximize information dominance. Field experiments allow students to test wireless MANETs at the tactical edge. Analysis is focused on determining optimal MANET design and implementation. It considers various implementations with varied antenna selection, radio power, and frequency specifications, and two specific methods of integrating Department of Defense biometric collection devices to the wireless MANET, which utilizes a single (WR) MPU4 802.11 Wi-Fi access point to connect secure electronic enrollment kit II (SEEK II) biometric devices to the MANET, and tethers each SEEK device to a dedicated WR using a personal Ethernet connection. Biometric data is shared across the tactical network and transmitted to remote servers. Observations and analysis regarding network performance demonstrate that wireless MANETs can be optimized for biometric reach back and integrated with biometric devices to improve biometric data sharing in MIO.

This research intends to improve information dominance in the maritime domain by optimizing tactical mobile ad hoc network (MANET) systems for wireless sharing of biometric data in maritime interdiction operations (MIO). Current methods for sharing biometric data in MIO are unnecessarily slow and do not leverage wireless networks at the tactical edge to maximize information dominance. Field experiments allow students to test wireless MANETs at the tactical edge. Analysis is focused on determining optimal MANET design and implementation. It considers various implementations with varied antenna selection, radio power, and frequency specifications, and two specific methods of integrating Department of Defense biometric collection devices to the wireless MANET, which utilizes a single (WR) MPU4 802.11 Wi-Fi access point to connect secure electronic enrollment kit II (SEEK II) biometric devices to the MANET, and tethers each SEEK device to a dedicated WR using a personal Ethernet connection. Biometric data is shared across the tactical network and transmitted to remote servers. Observations and analysis regarding network performance demonstrate that wireless MANETs can be optimized for biometric reach back and integrated with biometric devices to improve biometric data sharing in MIO.

University of Washington's Human Subjects Division Guidance on Genomic Data Sharing.

Our video today discusses when health and wellness–related companies share personal information about their customers, without first obtaining the customer’s permission. We'll also share some questions and tips that the FTC offers to consumers when considering using websites and/or downloading health and wellness-related apps. All our info for today’s video comes from the FTC, and the links to learn more are available below. https://consumer.ftc.gov/consumer-alerts/2024/04/when-companies-share-your-personal-information-without-your-permission?utm_source=govdelivery

This is a frozen registration of the project after having submitted it to Meta-Psychology and done the first round of revisions.

This is a frozen registration of the project after having submitted it to Meta-Psychology and done the first round of revisions.

A protocol for a cross sectional analysis of published re-use from 3 major repositories

Sharing data has many benefits. However, data sharing rates remain low, for the most part well below 50%. A variety of interventions encouraging data sharing have been proposed. We focus here on two studies about editorial policies. Kidwell et al. (2016) assessed the impact of the introduction of badges in Psychological Science; Hardwicke et al. (2018) assessed the impact of Cognition’s mandatory data sharing policy. Both studies found policies to improve data sharing practices, but only assessed the impact of the policy for up to 25 months after its implementation. We aim to examine the effect of these policies over a longer term, by reusing their data and collecting a follow-up sample including articles published up until December 31st, 2019. Our analysis will use generalized additive models as these allow for a more flexible assessment of the effect of time, which would facilitate the identification of non-linear changes in the trend. We will compare these models to generalized linear models to examine whether the non-linearity is needed.

Third-party data sharing has become more prevalent in digital economy. This practice has triggered more privacy concerns among consumers. Yet, few research has focused on understanding how third-party data sharing affect consumer attitudes and behaviors. In this project, we aim to seek for answers for our research question: how targeted advertisements in which third-party information dissemination is made salient affect consumers' attitudes toward the focal firm and the affiliated firm.

Document number CIA-RDP86M00612R000100090022-1 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP86M00612R000100090022-1 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Document number CIA-RDP86M00612R000100090022-1 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

This study protocol outlines a randomized controlled trial (RCT) aimed at improving data-sharing practices among authors submitting manuscripts to academic journals. The trial tests an intervention that informs authors about the benefits of data sharing and provides guidance on how to include direct links to trusted repositories in Data Availability Statements (DAS). Manuscripts are randomized into two groups: an intervention group, receiving data-sharing instructions during peer review, and a control group, undergoing standard review without added guidance. Primary outcomes measure whether DASs include a link to a repository, with secondary outcomes assessing alternative DAS types, such as "data available upon request." This multi-journal, stratified RCT seeks to evaluate the effectiveness of this intervention in encouraging immediate data sharing, potentially setting a standard that could enhance transparency and reproducibility in scientific research​.

Objectives: Clinical Trial Data Sharing has improved a lot recently, but its implementation is still not optimal. This limits the ability to verify the reproducibility of trial results, which is needed to indicate it as trustworthy and robust. This cross-sectional study will analyse the level of data sharing and reproducibility of main studies that supported EMA authorizations of oncological medicines. It will also evaluate the robustness of their conclusion due to different censoring rules and attrition bias. Design: We will first identify all oncologic indications –new drugs or extension of indications– that received a positive opinion from the CHMP between January 01, 2020, and December 31, 2024. Then, we will identify all RCTs referred to as ‘main studies’ in the European public assessment report and randomly sample 60 of them. For each sampled RCT, we will ask for IPD, protocols and information on how the study was conducted, which will be used to reanalyse the two main oncological endpoints (overall survival [OS] and progression-free survival [PFS]) of each trial for estimand, effect size, p-value and conclusion. Two independent researchers will discuss whether the differences between the original analysis and ours affect the conclusion and if the study can be considered reproducible. Afterwards, we will analyse the impact of censoring for OS and PFS, performing a reverse Kaplan-Meier analysis to assess the presence of differential censoring and risk of attrition bias. Finally, we will re-analyse the IPD using different ways to handle censoring and compare the results to evaluate the robustness of the study. Results: The first outcomes we will report are the proportion of trials for which we will be granted access to the data requested and the percentage of studies reproduced for OS and PFS. We will also review the different censoring rules used among the studies to identify patterns. Then we will use them to compare the results for both endpoints under different rules and report the proportion of studies not robust to censoring rule change and the impact that different rules have in terms of OS or PFS, together with the proportion of studies that present informative censoring. Conclusions: Confirming the reproducibility of trial results is needed to increase trust in research. In the case in which the trials will not be reproducible, recommendations on how to improve reproducibility would be provided, which will hopefully help to improve reproducibility in the future.

Objectives: Clinical Trial Data Sharing has improved a lot recently, but its implementation is still not optimal. This limits the ability to verify the reproducibility of trial results, which is needed to indicate it as trustworthy and robust. This cross-sectional study will analyse the level of data sharing and reproducibility of main studies that supported EMA authorizations of oncological medicines. It will also evaluate the robustness of their conclusion due to different censoring rules and attrition bias. Design: We will first identify all oncologic indications –new drugs or extension of indications– that received a positive opinion from the CHMP between January 01, 2020, and December 31, 2024. Then, we will identify all RCTs referred to as ‘main studies’ in the European public assessment report and randomly sample 60 of them. For each sampled RCT, we will ask for IPD, protocols and information on how the study was conducted, which will be used to reanalyse the two main oncological endpoints (overall survival [OS] and progression-free survival [PFS]) of each trial for estimand, effect size, p-value and conclusion. Two independent researchers will discuss whether the differences between the original analysis and ours affect the conclusion and if the study can be considered reproducible. Afterwards, we will analyse the impact of censoring for OS and PFS, performing a reverse Kaplan-Meier analysis to assess the presence of differential censoring and risk of attrition bias. Finally, we will re-analyse the IPD using different ways to handle censoring and compare the results to evaluate the robustness of the study. Results: The first outcomes we will report are the proportion of trials for which we will be granted access to the data requested and the percentage of studies reproduced for OS and PFS. We will also review the different censoring rules used among the studies to identify patterns. Then we will use them to compare the results for both endpoints under different rules and report the proportion of studies not robust to censoring rule change and the impact that different rules have in terms of OS or PFS, together with the proportion of studies that present informative censoring. Conclusions: Confirming the reproducibility of trial results is needed to increase trust in research. In the case in which the trials will not be reproducible, recommendations on how to improve reproducibility would be provided, which will hopefully help to improve reproducibility in the future.

Objectives: Clinical Trial Data Sharing has improved a lot recently, but its implementation is still not optimal. This limits the ability to verify the reproducibility of trial results, which is needed to indicate it as trustworthy and robust. This cross-sectional study will analyse the level of data sharing and reproducibility of main studies that supported EMA authorizations of oncological medicines. It will also evaluate the robustness of their conclusion due to different censoring rules and attrition bias. Design: We will first identify all oncologic indications –new drugs or extension of indications– that received a positive opinion from the CHMP between January 01, 2020, and December 31, 2024. Then, we will identify all RCTs referred to as ‘main studies’ in the European public assessment report and randomly sample 60 of them. For each sampled RCT, we will ask for IPD, protocols and information on how the study was conducted, which will be used to reanalyse the two main oncological endpoints (overall survival [OS] and progression-free survival [PFS]) of each trial for estimand, effect size, p-value and conclusion. Two independent researchers will discuss whether the differences between the original analysis and ours affect the conclusion and if the study can be considered reproducible. Afterwards, we will analyse the impact of censoring for OS and PFS, performing a reverse Kaplan-Meier analysis to assess the presence of differential censoring and risk of attrition bias. Finally, we will re-analyse the IPD using different ways to handle censoring and compare the results to evaluate the robustness of the study. Results: The first outcomes we will report are the proportion of trials for which we will be granted access to the data requested and the percentage of studies reproduced for OS and PFS. We will also review the different censoring rules used among the studies to identify patterns. Then we will use them to compare the results for both endpoints under different rules and report the proportion of studies not robust to censoring rule change and the impact that different rules have in terms of OS or PFS, together with the proportion of studies that present informative censoring. Conclusions: Confirming the reproducibility of trial results is needed to increase trust in research. In the case in which the trials will not be reproducible, recommendations on how to improve reproducibility would be provided, which will hopefully help to improve reproducibility in the future.

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Bibliography: p. 67-68

Bibliography: p. 41

Research protocol of a systematized review of published peer-reviewed articles by agricultural researchers in the fields of crop production to examine their data sharing practices. The systematized review focuses on corn and soybean production research that produced primary data and analyzed if data is shared, where it is shared, and how it is shared. This review was performed by members of the Agriculture Network Information Collaborative (AgNIC) Data Working Group. AgNIC is a partnership of institutions who voluntarily collaborate in the area of agricultural information and data management.

Open data sharing is critical for scientific progress. Discoveries are made through cumulative and collective efforts, ideally based on full and open communication (Merton 1942).Yet, while journal editors increasingly acknowledge the importance of data sharing, authors’ willingness to share their data remains limited (Andreoli-Versbach and Mueller-Langer 2014; Federer et al. 2018; Krawczyk and Reuben 2012; Langille, Ravel, and Fricke 2018; Massen et al. 2017; McIntosh et al. 2020; Savage and Vickers 2009; Science Digital; et al. 2021; Tedersoo et al. 2021; Tenopir et al. 2011). We will conduct an audit experiment to test how status beliefs about the data requestor’s perceived gender, ethnicity, country and institutional affiliation shape scientists’ willingness to share data. Specifically, we will request data from a sample of corresponding authors in the social sciences, health sciences and natural sciences, while varying the identity of the fictitious data requestor (an “about to become” PhD student) on four factors: (i) country of residence (China vs. United States), (ii) institutional affiliation (high status vs. lower status university), (ii) ethnicity (Chinese vs. typical white Anglo-Saxon name), and (iv) gender (masculine-coded vs. feminine-coded name). All other content of the email-based data requests is held constant. Motivated by previous research on status beliefs and inequality (Berger, Cohen, and Zelditch 1972; Correll and Benard 2006; Jost et al. 2009; Ridgeway 2001; Ross et al. 2006; Tomkins, Zhang, and Heavlin 2017), we expect that authors are less likely to share data, when the requestor is (A) from China, (B) affiliated with a lower-status university, (C) has a Chinese name, and (D) has a feminine-coded name.

Book on computer time sharing - American Data Processing, Inc.

Data science is a collaborative field. Collaboration requires sharing the artifacts that data scientists are working on, such as Jupyter Notebooks and SQL tables. Hex is a platform for improving sharing across data science workflows. Caitlin Colgrove and Barry McCardel are founders of Hex and they join the show to discuss what they have built.

The main file of Indian customs and excise department's data sharing policy obtained under RTI

The main file of Indian customs and excise department's data sharing policy obtained under RTI

Stage 1 IPA at PCI RR

Workshop recording from the 2nd Open Economics International Workshop, see more at: http://openeconomics.net/workshop-june-2013 Panel session: Sharing Research Data

Time-sharing, business processor guide 1965, Control Data 3300/3500

Computer time-sharing, Control Data 3300/3500 - Control Data Corp.

Braintrust Partners CEO Tim Hayden discusses all the data that you might, or might NOT, expect your car to be sharing with the world, from biometrics and purchase history to location history and where you're speeding. Be careful out there, kids.

This is an exploratory study that aims to investigate how common data and code sharing are in a random sample of oncology research articles published in 2019.

This is an exploratory study that aims to investigate how common data and code sharing are in a random sample of oncology research articles published in 2019.

This is an exploratory study that aims to investigate how common data and code sharing are in a random sample of oncology research articles published in 2019.

This is an exploratory study that aims to investigate how common data and code sharing are in a random sample of oncology research articles published in 2019.

Informatization Research on Teaching Management Work Mode of College Teachers Based on Cloud Real-Time Recording Terminal Data Sharing

Research in life sciences is increasingly being conducted in a digital and online environment. In particular, life scientists have been pioneers in embracing new computational tools to conduct their investigations. To support the sharing of digital objects produced during such research investigations, we have witnessed in the last few years the emergence of specialized repositories, e.g., DataVerse and FigShare. Such repositories provide users with the means to share and publish datasets that were used or generated in research investigations. While these repositories have proven their usefulness, interpreting and reusing evidence for most research results is a challenging task. Additional contextual descriptions are needed to understand how those results were generated and/or the circumstances under which they were concluded. Because of this, scientists are calling for models that go beyond the publication of datasets to systematically capture the life cycle of scientific investigations and provide a single entry point to access the information about the hypothesis investigated, the datasets used, the experiments carried out, the results of the experiments, the people involved in the research, etc. In this paper we present the Research Object (RO) suite of ontologies, which provide a structured container to encapsulate research data and methods along with essential metadata descriptions. Research Objects are portable units that enable the sharing, preservation, interpretation and reuse of research investigation results. The ontologies we present have been designed in the light of requirements that we gathered from life scientists. They have been built upon existing popular vocabularies to facilitate interoperability. Furthermore, we have developed tools to support the creation and sharing of Research Objects, thereby promoting and facilitating their adoption.

File Notings of Aarogya Setu Data Access and Knowledge Sharing Protocol

This review is based on the access control policy and data sharing mechanisms. We know that Cloud computing is the best & proficient manner on handle our information remotely. Data Confidentiality is one of the chief problem now a day’s. Security is also matters while data storing & sharing with others. Whenever we are using platform like cloud trust factor also considered. A lot of unauthorized community tries to access & steal the confidential data. In the current time cryptographic technologies are used to secure data. Sharing cloud data in between group of users at a best level is still a complicated issue, especially when dealing with dynamic users group .In this paper we present and discuss propose system which deals with dynamic user group problem like revocation and data privacy & make access control policy.

Numerous studies have demonstrated low but increasing rates of data and code sharing within medical and health research disciplines. However it remains unclear how commonly data and code are shared across all fields of medical and health research, as well as whether sharing rates are positively associated with implementation of progressive policies by publishers and funders, or growing expectations from the medical and health research community at large. This registration page contains the protocol for a systematic review that aims to synthesise the findings of medical and health sciences studies that have empirically investigated the prevalence of data or code sharing, or both. Objectives include the investigation of: (i) the prevalence of public sharing of research data and code alongside published articles (including preprints), (ii) the prevalence of private sharing of research data and code in response to reasonable requests, and (iii) factors associated with the sharing of either research output (e.g., the year published, the publisher’s policy on sharing, the presence of a data or code availability statement).

With the ubiquity of wearable gadgets, alongside the advancement of clouds and cloudlet innovation, there has been expanding need to give better medical care. The preparing chain of medical information mostly incorporates information accumulation, information stockpiling and information sharing, and so forth. Customary medicinal services system regularly requires the conveyance of medical information to the cloud, which includes clients' delicate data and causes correspondence vitality utilization. For all intents and purposes, medical information sharing is a basic and testing issue. Subsequently in this paper, we develop a novel social insurance system by using the adaptability of cloudlet. The elements of cloudlet incorporate security insurance, information sharing and intrusion detection. In the phase of information accumulation, we initially use Number Theory Research Unit (NTRU) strategy to scramble client's body information gathered by wearable gadgets. Those information will be transmitted to close-by cloudlet in a vitality productive design. Furthermore, we exhibit another trust model to help clients to choose trustable accomplices who need to share put away information in the cloudlet. The trust display additionally causes comparable patients to speak with each other about their infections. Pooja | Neelambika"Privacy Protection and Intrusion Avoidance for Cloudlet-Based Medical Data Sharing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd15628.pdf http://www.ijtsrd.com/engineering/computer-engineering/15628/privacy-protection-and-intrusion-avoidance-for-cloudlet-based-medical-data-sharing/pooja

With the ubiquity of wearable gadgets, alongside the advancement of clouds and cloudlet innovation, there has been expanding need to give better medical care. The preparing chain of medical information mostly incorporates information accumulation, information stockpiling and information sharing, and so forth. Customary medicinal services system regularly requires the conveyance of medical information to the cloud, which includes clients' delicate data and causes correspondence vitality utilization. For all intents and purposes, medical information sharing is a basic and testing issue. Subsequently in this paper, we develop a novel social insurance system by using the adaptability of cloudlet. The elements of cloudlet incorporate security insurance, information sharing and intrusion detection. In the phase of information accumulation, we initially use Number Theory Research Unit (NTRU) strategy to scramble client's body information gathered by wearable gadgets. Those information will be transmitted to close-by cloudlet in a vitality productive design. Furthermore, we exhibit another trust model to help clients to choose trustable accomplices who need to share put away information in the cloudlet. The trust display additionally causes comparable patients to speak with each other about their infections. Pooja | Neelambika"Privacy Protection and Intrusion Avoidance for Cloudlet-Based Medical Data Sharing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd15628.pdf http://www.ijtsrd.com/engineering/computer-engineering/15628/privacy-protection-and-intrusion-avoidance-for-cloudlet-based-medical-data-sharing/pooja

Innovation of the Development Model of Ideological Education Based on Mobile Device Information Sharing Algorithm in the Era of Big Data

Sharing (raw, anonymous) participant data without restrictions is the new gold-standard of psychological research. However, it is possible that the data quality obtained in psychological studies is affected by this choice. Potential participants might be unwilling to contribute to the research, which can in turn create a biased sample in terms of the type of person who is still willing to participate. Subjects might participate, but behave in a way more in line with the public image they like to display, leading to less accurate self report data. Overall, their care in reading and complying with instructions might be lower than if data is not shared. We conduct two studies, one pen & paper, one online, to investigate the impact of data sharing on participant behaviour. For ethical reasons, we will not share the data of the participants who were in the "no open access" condition, but will make data available upon request.

Sharing (raw, anonymous) participant data without restrictions is the new gold-standard of psychological research. However, it is possible that the data quality obtained in psychological studies is affected by this choice. Potential participants might be unwilling to contribute to the research, which can in turn create a biased sample in terms of the type of person who is still willing to participate. Subjects might participate, but behave in a way more in line with the public image they like to display, leading to less accurate self report data. Overall, their care in reading and complying with instructions might be lower than if data is not shared. We conduct two studies, one pen & paper, one online, to investigate the impact of data sharing on participant behaviour. For ethical reasons, we will not share the data of the participants who were in the "no open access" condition, but will make data available upon request.

In a study evaluating the implementation of the ICMJE data sharing requirement for randomized trials published in three leading medical journals (JAMA, Lancet, and NEJM) between July 1, 2018, and April 4, 2020 it was found that while a high number of articles declared data sharing (334/487), individual participant data (IPD) sets were rarely publicly available at least as of April 2020 (https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2775667). Many trials alluded to some embargo for data sharing, and one might expect that IPD might become available after some period of time when the embargo could be lifted. Many of these trials did actually specify the anticipated duration of the embargo, while others did not state such an explicit date. We aim to investigate the status of data sharing after 3 years of additional follow-up (until April 2023) in these randomized clinical trials that had claimed an embargo for data sharing. Specifically, we will survey whether the embargo on data sharing has actually been lifted. With this additional follow-up we plan to revisit the mechanisms for obtaining the data and whether the promise of data sharing has been fulfilled, in accordance with the original data sharing statement.