Raw fMRI data of 14 healthy controls (HC) and 15 chronic low back pain patients (CLBP). Subject had to perform a motor imagery (MI) task during scanning session.
Dataset description: A description of the dataset, with more information than the title.
Raw fMRI data of controls and patients
Title: The title of the dataset
2016-02-02T09:35:03Z
Publication Date: Date that the dataset was made available through the repository. Dates should be in a standard format, e.g., ISO 8601
Note that this date may be different than when the data are deposited in the repository. If the data have been deposited and are under embargo, the repository should provide information on when the data are available.
2016-02-02T09:35:03Z
Deposit Date: Date that the data set was uploaded to the repository. Dates should be formatted according to ISO 8061 standard.
2016-02-02T09:35:03Z
Data availability and disposition (conditional) : The landing page should provide information on the availability of the data, e.g., if it is currently under embargo or otherwise restricted, or has been de-accessioned (i.e., removed from the archive). As stated in the JDDCP, metadata should persist beyond de-accessioning.
1.0
Versions: What versions of the data are available, if there is more than one version that may be accessed.
12 Files
Data access: Users should be able to download or otherwise access data via the landing page
Data availability and disposition (conditional) : The landing page should provide information on the availability of the data, e.g., if it is currently under embargo or otherwise restricted, or has been de-accessioned (i.e., removed from the archive).
CC0 - "Public Domain Dedication"
License/Waiver: The license or waiver under which access to the content is provided (preferably a link to standard license/waiver text (e.g. https://creativecommons.org/publicdomain/zero/1.0/)
Cranmer, Skyler (The Ohio State University)
Publisher/Contact: The organization and/or contact who published the dataset, e.g., a data repository, and is responsible for its persistence.
Explanation: The publisher/contact field should probably be split. The contact person, in this case, is the author(s) who are responsible for generating the data.
Publisher = Harvard Dataverse. No need to add this information to this page, as it is implicit in the location. The machine readable metadata also identifies the Dataverse as the publisher. However, if a landing page is maintained for this data set in another context, e.g., Data Cite, Biocaddie, the publisher field should state that Harvard Dataverse is the responsible party.
Cranmer, Skyler (The Ohio State University) Leifeld, Philip (University of Glasgow) McClurg, Scott (Southern Illinois University) Rolfe, Meredith (UMass Amherst)
Creator/author: The person(s) and/or organizations who generated the dataset and are responsible for its integrity.
Starr et al. strongly recommends that Creator Identifier(s, e.g., ORCiD, be included as well.
2016-04-21
Publication Date: Date that the dataset was made available through the repository. Dates should be in a standard format, e.g., ISO 8601
doi:10.7910/DVN/2XP8YF
Data set identifier: A machine-actionable identifier resolvable on the Web to the dataset.
The last decade has seen substantial advances in statistical techniques for the analysis of network data, and a major increase in the frequency with which these tools are used. These techniques are designed to accomplish the same broad goal, statistically valid inference in the presence of highly interdependent relationships, but important differences remain between them. We review three approaches commonly used for inferential network analysis---the Quadratic Assignment Procedure, Exponential Random Graph Model, and Latent Space Network Model---highlighting the strengths and weaknesses of the techniques relative to one another. An illustrative example using climate change policy network data shows that all three network models outperform standard logit estimates on multiple criteria. This paper introduces political scientists to a class of network techniques beyond simple descriptive measures of network structure, and helps researchers choose which model to use in their own research. (2016-01-31)
Description: More detailed description of the data beyond that available in the title
Cranmer, Skyler (The Ohio State University)
Publisher/Contact: The organization and/or contact who published the dataset, e.g., a data repository, and is responsible for its persistence.
Explanation: The publisher/contact field should probably be split. The contact person, in this case, is the author(s) who are responsible for generating the data.
Publisher = Harvard Dataverse. No need to add this information to this page, as it is implicit in the location. The machine readable metadata also identifies the Dataverse as the publisher. However, if a landing page is maintained for this data set in another context, e.g., Data Cite, Biocaddie, the publisher field should state that Harvard Dataverse is the responsible party.
Cranmer, Skyler (The Ohio State University) Leifeld, Philip (University of Glasgow) McClurg, Scott (Southern Illinois University) Rolfe, Meredith (UMass Amherst)
Creator/Author: The person(s) and/or organizations who generated the dataset and are responsible for its integrity.
doi:10.7910/DVN/2XP8YF
Data set identifier: A machine-actionable identifier resolvable on the Web to the dataset.
The last decade has seen substantial advances in statistical techniques for the analysis of network data, and a major increase in the frequency with which these tools are used. These techniques are designed to accomplish the same broad goal, statistically valid inference in the presence of highly interdependent relationships, but important differences remain between them. We review three approaches commonly used for inferential network analysis---the Quadratic Assignment Procedure, Exponential Random Graph Model, and Latent Space Network Model---highlighting the strengths and weaknesses of the techniques relative to one another. An illustrative example using climate change policy network data shows that all three network models outperform standard logit estimates on multiple criteria. This paper introduces political scientists to a class of network techniques beyond simple descriptive measures of network structure, and helps researchers choose which model to use in their own research.
Description: More detailed description of the data beyond that available in the title
Replication Data for: Navigating the Range of Statistical Tools for Inferential Network Analysis
Title: Title of the dataset
dc.contributor.author Vrana, Andrea dc.contributor.author Hotz-Boendermaker, Sabina dc.contributor.author Stämpfli, Philipp dc.contributor.author Hänggi, Jürgen dc.contributor.author Seifritz, Erich dc.contributor.author Humphreys, B. Kim dc.contributor.author Meier, Michael L.
Data creators/authors: The person(s) and/or organizations who generated the dataset and are responsible for its integrity.
Note that Dryad uses the Dublin Core (dc) metadata attributes to make this page machine-parseable.
DOI http://dx.doi.org/10.5061/dryad.2h0q3
Data set identifier: A machine-actionable identifier resolvable on the Web to the dataset.
Raw_Data.zip (2.090 Gb)
Means to access data; in this case, the link provides a way to download the data.