Dataset + File Management¶

Supported Metadata Export Formats¶

Once a dataset has been published its metadata is exported in a variety of formats. A button on the dataset page’s metadata tab will allow a user to export the metadata of the most recently published version of the dataset. Currently supported export formats are:

• Dublin Core
• DDI (Data Documentation Initiative)
• DataCite 4
• JSON (native Dataverse format)
• OAI_ORE
• OpenAIRE
• Schema.org JSON-LD

Supported HTML Fields¶

We currently only support the following HTML tags for any of our textbox metadata fields (i.e., Description) : <a>, <b>, <blockquote>, <br>, <code>, <del>, <dd>, <dl>, <dt>, <em>, <hr>, <h1>-<h3>, <i>, <img>, <kbd>, <li>, <ol>, <p>, <pre>, <s>, <sup>, <sub>, <strong>, <strike>, <ul>.

HTTP Upload¶

HTTP Upload is a common browser-based file upload tool you may be familiar with from other web applications. You can upload files via HTTP by selecting them from your browser or dragging and dropping them into the upload widget.

Once you have uploaded files, you will be able to edit file metadata, restrict access to files [1] , and/or add tags. Click “Save Changes” to complete the upload. If you uploaded a file by mistake, you can delete it before saving by clicking the checkbox to select the file, and then clicking the “Delete” button above the Files Table.

File upload limit size varies based on Dataverse installation. The file upload size limit can be found in the text above the HTTP upload widget. If you need to upload a very large file or a very large number of files, consider using rsync + SSH upload if your installation of Dataverse offers it.

Dropbox Upload¶

Some Dataverse installations support the ability to upload files directly from Dropbox. To do so, click the “Upload from Dropbox” button, log in to Dropbox in the pop-up window, and select the files you’d like to transfer over.

rsync + SSH Upload¶

rsync is typically used for synchronizing files and directories between two different systems, using SSH to connect rather than HTTP. Some Dataverse installations allow uploads using rsync, to facilitate large file transfers in a reliable and secure manner.

Command-line DVUploader¶

The open-source DVUploader tool is a stand-alone command-line Java application that uses the Dataverse API to upload files to a specified Dataset. Since it can be installed by users, and requires no server-side configuration, it can be used with any Dataverse installation. It is intended as an alternative to uploading files through the Dataverse web interface in situations where the web interface is inconvenient due to the number of files or file locations (spread across multiple directories, mixed with files that have already been uploaded or file types that should be excluded) or the need to automate uploads. Since it uses the Dataverse API, transfers are limited in the same ways as HTTP uploads through the Dataverse web interface in terms of size and performance. The DVUploader logs its activity and can be killed and restarted as desired. If stopped and resumed, it will continue processing from where it left off.

java -jar DVUploader-v1.0.0.jar -server=<Dataverse server URL> -did=<Dataset DOI> -key=<User's API Key> <file or directory list>

Tabular Data Files¶

Files in certain formats - Stata, SPSS, R, Excel(xlsx) and CSV - may be ingested as tabular data (see “Tabular Data Ingest” section of the User Guide for details). Tabular data files can be further explored and manipulated with TwoRavens - a statistical data exploration application integrated with Dataverse, as well as other External Tools if they have been enabled in the installation of Dataverse you are using. TwoRavens allows the user to run statistical models, view summary statistics, download subsets of variable vectors and more. To start, click on the “Explore” button, found next to each relevant tabular file (the application will be opened in a new window). To download subsets of variables click on the “Download” button found next to a relevant tabular file and select “Data Subset” in the dropdown menu. You will then be able to create your subset using the interface opened in a new window (this functionality is also provided by the TwoRavens project). See the TwoRavens documentation section for more information.

Additional download options available for tabular data (found in the same drop-down menu under the “Download” button):

• As tab-delimited data (with the variable names in the first row);
• The original file uploaded by the user;
• Saved as R data (if the original file was not in R format);
• Variable Metadata (as a DDI Codebook XML file);
• Data File Citation (currently in either RIS, EndNote XML, or BibTeX format);
• All of the above, as a zipped bundle.

Geospatial¶

Geospatial shapefiles can be further explored and manipulated through our integration with WorldMap, a geospatial data visualization and analysis tool developed by the Center for Geographic Analysis at Harvard University. A shapefile is a set of files, often uploaded/transferred in .zip format. This set may contain up to 15 files. A minimum of 3 specific files (.shp, .shx, .dbf) are needed to be a valid shapefile and a 4th file (.prj) is required for WorldMap–or any type of meaningful visualization.

For ingest into Dataverse and connecting to WorldMap, these 4 files are the minimum required:

• .shp - shape format; the feature geometry itself
• .shx - shape index format; a positional index of the feature geometry to allow seeking forwards and backwards quickly
• .dbf - attribute format; columnar attributes for each shape, in dBase IV format
• .prj - projection format; the coordinate system and projection information, a plain text file describing the projection using well-known text format

For a zipped shapefile, we require 4 files with these extensions. Other files may be included within the zipped shapefile, but they are not required:

• .shp
• .shx
• .prj
• .dbf

For example, if these files were included within a .zip, the “Map Data” button would appear:

• subway_line.shp
• subway_line.shx
• subway_line.prj
• subway_line.dbf

Once you publish your dataset with your shape files, you will be able to use the “Map Data” button using GeoConnect to visualize and manipulate these files for users to Explore this geospatial data using the WorldMap interface. Please note: In order to map your data file, a copy will be sent to Harvard’s WorldMap platform. You have the ability to delete any maps, and associated data, from the Harvard WorldMap platform, at any time.

Astronomy (FITS)¶

Metadata found in the header section of Flexible Image Transport System (FITS) files are automatically extracted by Dataverse, aggregated and displayed in the Astronomy Domain-Specific Metadata of the Dataset that the file belongs to. This FITS file metadata, is therefore searchable and browsable (facets) at the Dataset-level.

Compressed Files¶

Compressed files in .zip format are unpacked automatically. If a .zip file fails to unpack for whatever reason, it will upload as is. If the number of files inside are more than a set limit (1,000 by default, configurable by the Administrator), you will get an error message and the .zip file will upload as is.

If the uploaded .zip file contains a folder structure, Dataverse will keep track of this structure. A file’s location within this folder structure is displayed in the file metadata as the File Path. When you download the contents of the dataset, this folder structure will be preserved and files will appear in their original locations.

These folder names are subject to strict validation rules. Only the following characters are allowed: the alphanumerics, ‘_’, ‘-‘, ‘.’ and ‘ ‘ (white space). When a zip archive is uploaded, the folder names are automatically sanitized, with any invalid characters replaced by the ‘.’ character. Any sequences of dots are further replaced with a single dot. For example, the folder name data&info/code=@137 will be converted to data.info/code.137. When uploading through the Web UI, the user can change the values further on the edit form presented, before clicking the ‘Save’ button.

Other File Types¶

There are several advanced options available for certain file types.

• Image files: .jpg, .png, and .tif files are able to be selected as the default thumbnail for a dataset. The selected thumbnail will appear on the search result card for that dataset.
• SPSS files: SPSS files can be tagged with the language they were originally coded in. This is found by clicking on Advanced Options and selecting the language from the list provided.

Edit File Metadata¶

Go to the dataset you would like to edit, where you will see the listing of files. Select the files you would like to edit by using either the Select All checkbox or individually selecting files. Next, click the “Edit Files” button above the file table and from the dropdown menu select if you would like to:

• Delete the selected files
• Edit the file metadata (file name, description) for the selected files
• Restrict the selected files
• Unrestrict the selected files (only if the selected files are restricted)
• Add tags to the selected files

You will not have to leave the dataset page to complete these action, except for editing file metadata, which will bring you to the Edit Files page. There you will have to click the “Save Changes” button to apply your edits and return to the dataset page.

If you restrict files, you will also prompted with a popup asking you to fill out the Terms of Access for the files. If Terms of Access already exist, you will be asked to confirm them. Note that some Dataverse installations do not allow for file restrictions.

File Path¶

The File Path metadata field is Dataverse’s way of representing a file’s location in a folder structure. When a user uploads a .zip file containing a folder structure, Dataverse automatically fills in the File Path information for each file contained in the .zip. If a user downloads the full dataset or a selection of files from it, they will receive a folder structure with each file positioned according to its File Path.

A file’s File Path can be manually added or edited on the Edit Files page. Changing a file’s File Path will change its location in the folder structure that is created when a user downloads the full dataset or a selection of files from it.

If there is more than one file in the dataset, and once at least one of them has a non-empty directory path, the Dataset Page will present an option for switching between the traditional table view, and the tree-like view of the files showing the folder structure, as in the example below:

File Tags¶

File tags are comprised of custom, category (i.e. Documentation, Data, Code) and tabular data tags (i.e. Event, Genomics, Geospatial, Network, Panel, Survey, Time Series). Use the dropdown select menus as well as the custom file tag input to apply these tags to the selected files. There is also a Delete Tags feature that, if checked, will allow you to delete unused file tags within that dataset.

CC0 Public Domain Dedication¶

By default, all new datasets created through Dataverse’s web UI are given a Creative Commons CC0 Public Domain Dedication.

The Creative Commons organization defines a number of licenses that allow copyright holders to release their intellectual property more openly, with fewer legal restrictions than standard copyright enforces. Each Creative Commons license typically specifies simple terms for how the IP must be used, reused, shared, and attributed. In addition to these licenses, Creative Commons also provides public domain tools that make it easy to dedicate IP to the public domain.

In the context of Dataverse, their CC0 Public Domain Dedication allows you to unambiguously waive all copyright control over your data in all jurisdictions worldwide. Data released with CC0 can be freely copied, modified, and distributed (even for commercial purposes) without violating copyright. In most parts of the world, factual data is exempt from copyright anyway, but applying CC0 removes all ambiguity and makes the legal copyright status of the data as clear as possible. Dataverse applies CC0 to datasets by default because it facilitates reuse, extensibility, and long-term preservation of research data by assuring that the data can be safely handled by anyone without fear of potential copyright pitfalls.

Though CC0 waives a dataset owner’s legal copyright controls over the data, it does not exempt Dataverse users from following ethical and professional norms in scholarly communications. The Dataverse Community Norms * as well as scientific best practices assert that proper credit should be given via citation. Regardless of whether CC0 has been applied or not, Dataverse users are expected to cite the data they use, giving credit to the data’s authors. This expectation applies to both the Dataverse Community and the entire wider scholarly community.

Additionally, users are still expected to respect access restrictions and other terms applied to CC0 files in Dataverse. Additional restrictions, conditions, and terms can still be compatible with CC0, as CC0 only operates in the realm of copyright, which is rather limited when it comes to data.

If a data owner feels that CC0 is not suitable for their data, they are able to enter custom Terms of Use, as detailed in the following section.

* Legal Disclaimer: these Community Norms are not a substitute for the CC0 waiver or custom terms and licenses applicable to each dataset. The Community Norms are not a binding contractual agreement, and that downloading datasets from Dataverse does not create a legal obligation to follow these policies.

Custom Terms of Use for Datasets¶

If you are unable to use the CC0 Public Domain Dedication for your datasets, you may specify your own custom Terms of Use. To do so, select “No, do not apply CC0 - “Public Domain Dedication”, and a Terms of Use text box will show up allowing you to enter your own custom terms of use for your dataset. To add more information about the Terms of Use, we have provided fields like Special Permissions, Restrictions, Citation Requirements, etc.

Here is an example of a Data Usage Agreement for datasets that have de-identified human subject data.

Restricted Files + Terms of Access¶

If you restrict any files in your dataset, you will be prompted by a pop-up to enter Terms of Access for the data. This can also be edited in the Terms tab or selecting Terms in the “Edit” dropdown button in the dataset. You may also allow users to request access for your restricted files by enabling “Request Access”. To add more information about the Terms of Access, we have provided fields like Data Access Place, Availability Status, Contact for Access, etc.

Note: Some Dataverse installations do not allow for file restriction.

Guestbook¶

This is where you will enable a particular Guestbook for your dataset, which is setup at the Dataverse-level. For specific instructions please visit the Dataset Guestbooks section of the Dataverse Management page.

Dataset-Level¶

Admins or curators of a dataset can assign roles and permissions to the users of that dataset. If you are an admin or curator of a dataset, then you can get to the dataset permissions page by clicking the “Edit” button, highlighting “Permissions” from the dropdown list, and clicking “Dataset”.

When you access a dataset’s permissions page, you will see two sections:

Users/Groups: Here you can assign roles to specific users or groups, determining which actions they are permitted to take on your dataset. You can also reference a list of all users who have roles assigned to them for your dataset and remove their roles if you please. Some of the users listed may have roles assigned at the dataverse level, in which case those roles can only be removed from the dataverse permissions page.

Roles: Here you can reference a full list of roles that can be assigned to users of your dataset. Each role lists the permissions that it offers.

File-Level¶

If specific files in your dataset are restricted access, then you can grant specific users or groups access to those files while still keeping them restricted to the general public. If you are an admin or curator of a dataset, then you can get to the file-level permissions page by clicking the “Edit” button, highlighting “Permissions” from the dropdown list, and clicking “File”.

When you access a dataset’s file-level permissions page, you will see two sections:

Users/Groups: Here you can see which users or groups have been granted access to which files. You can click the “Grant Access to Users/Groups” button to see a box where you can grant access to specific files within your dataset to specific users or groups. If any users have requested access to a file in your dataset, you can grant or reject their access request here.

Restricted Files: In this section, you can see the same information, but broken down by each individual file in your dataset. For each file, you can click the “Assign Access” button to see a box where you can grant access to that file to specific users or groups.

Thumbnails¶

Thumbnail images can be assigned to a dataset manually or automatically. The thumbnail for a dataset appears on the search result card for that dataset and on the dataset page itself. If a dataset contains one or more data files that Dataverse recognizes as an image, then one of those images is automatically selected as the dataset thumbnail.

If you would like to manually select your dataset’s thumbnail, you can do so by clicking the “Edit” button on your dataset, and selecting “Thumbnails + Widgets” from the dropdown menu.

On this page, under the Thumbnail tab you will see three possible actions.

Select Available File: Click the “Select Thumbnail” button to choose an image from your dataset to use as the dataset thumbnail.

Upload New File: Upload an image file from your computer to use as the dataset thumbnail. While by default your thumbnail image is drawn from a file in your dataset, this will allow you to upload a separate image file to use as your dataset thumbnail. This uploaded image file will only be used as the dataset thumbnail; it will not be stored as a data file in your dataset.

Remove Thumbnail: If you click the “Remove” button under the thumbnail image, you will remove the dataset’s current thumbnail. The Dataset will then revert to displaying a basic default icon as the dataset thumbnail.

When you’re finished on this page, be sure to click “Save Changes” to save what you’ve done.

Note: If you prefer, it is also possible to set an image file in your dataset as your thumbnail by selecting the file, going to Edit Files -> Metadata, and using the “Set Thumbnail” button.

Widgets¶

The Widgets feature provides you with code for your personal website so your dataset can be displayed. There are two types of Widgets for a dataset: the Dataset Widget and the Dataset Citation Widget. Widgets are found by going to your dataset page, clicking the “Edit” button (the one with the pencil icon) and selecting “Thumbnails + Widgets” from the dropdown menu.

In the Widgets tab, you can copy and paste the code snippets for the widget you would like to add to your website. If you need to adjust the height of the widget on your website, you may do so by editing the heightPx=500 parameter in the code snippet.

Version Details¶

To view exactly what has changed, starting from the originally published version to any subsequent published versions: click the Versions tab on the dataset page to see all versions and changes made for that particular dataset.

Once you have more than one version (this can simply be version 1 and a draft), you can click the “View Details” link next to each summary to learn more about the metadata fields and files that were either added or edited. You can also click the checkboxes to select any two dataset versions, then click the “View Differences” button to open the Version Differences Details popup and compare the differences between them.

Cloud Computing¶

The “Compute” button on dataset and file pages will allow you to compute on a single dataset, multiple datasets, or a single file. You can use it to build a compute batch and go directly to the cloud computing environment that is integrated with Dataverse.

Cloud Storage Access¶

If you need to access a dataset in a more flexible way than the Compute button provides, then you can use the Cloud Storage Access box on the dataset page to copy the dataset’s container name. This unique identifer can then be used to allow direct access to the dataset.