FAQ

Find Research Data

We index thousands of data repositories either directly or via DataCite. This translates to tens of millions of indexed datasets. Our complete list of indexed repositories can be provided upon request, or accessed via our API at: https://datasearch.elsevier.com/api/docs

Mendeley Data Search focuses on research data (as opposed to a web search engine or a document search engine) and is constantly optimized based on the recent updates and number of hits. We search for key phrases in the content of every data file in our repository rather than just the metadata index. We have a Push API which allows any repository to push data to DataSearch and therefore appear in Mendeley Data Search results. For the Data Search API refer to the documentation and select Push API in the dropdown. We provide a preview of the data file contents in line (highlighting the search term), without needing to download the file, so that you can quickly evaluate which files are going to be useful for you.

When doing an advanced search in Mendeley Data, use the following information regarding syntax for different ways to search for data.

Searching within fields

Use the following syntax to target one or more specific fields within a dataset by entering the desired field code and then placing the name in parenthesis following it. Note that the syntax field codes must be capitalized as below. For example, when searching by Author, type AUTHOR(Jane Smith)
  • AUTHOR()
  • AUTHOR_ID
  • TITLE()
  • INSTITUTION()
  • INSTITUTION_ID()
  • ID()
  • DOI()
  • KEYWORDS
  • SUBJECT_AREA
  • IS_SUPPLEMENT_TO

Note: Regarding the following field codes:

  • AUTHOR_ID() supports the following IDs: Mendeley User ID, Scopus User ID, ORCID and all user IDs supported by DataCite.
  • INSTITUTION_ID() supports the following IDs: Scopus Institution ID, Scival Institution ID, Mendeley Institution ID.

Boolean Search Terms

Additionally, DataSearch supports Boolean search terms. You may search for data on DataSearch using AND, OR or NOT query terms. Field codes can also be used in any boolean query, which includes OR between normal and field code queries. Currently DataSearch can process queries like these:
  • chip-seq drosophila AUTHOR(Doe)
  • chip-seq drosophila AUTHOR(Doe OR Hari)
  • chip-seq drosophila AUTHOR(Hari) AND INSTITUTION(University of Manchester)
  • chip-seq AND (drosophila OR “fruit fly”) AND ID(GEO4667)
  • (chip-seq drosophila AND AUTHOR(Doe)) OR AUTHOR(Doe AND Hari)
  • IS_SUPPLEMENT_TO(10.1016/j.dib.2015.10.003)
When no operator is included this is assumed to be an implicit OR. If you want to perform an exact match of the text being provided by a Field Code, you can use Field Code modifiers. We offer two ways to do it:
  • FIELD CODE(“abcdfg”)
  • FIELD CODE({abcdfg})
Operators (AND, OR and NOT) are not recognized inside a Field Code modifier. As such, queries using FIELD CODE(“xxxx OR yyyy”) will probably not retrieve the results one would expect. There are three ways to use operators inside a field code:
  • Using a Field Code (without any modifiers): AUTHOR(John OR Martha)
  • Splitting the operands into separate Field Codes with modifiers: AUTHOR({John}) OR AUTHOR({Martha})
  • Using double quotes in a field code modifier. This is accepted in order to provide backwards compatibility to very common queries that might have been used extensibly: INSTITUTION(“University of Manchester” OR “University of Oxford”)

Note: INSTITUTION({University of Manchester} OR {University of Oxford}) is not accepted! Use INSTITUTION({University of Manchester}) OR INSTITUTION({University of Oxford}) instead.

It should be noted that using the “xxxx” construct (i.e. using double quotes to specify an exact text match in a search) is also allowed outside a Field Code. The behavior for such a query is expected. All words inside the double quotes are searched as they appear.

Alternatively, the {xxxx} construct ((i.e. using curly braces to specify an exact text match in a search) is only accepted as a Field Code modifier and is not accepted in a free text search (as curly braces are not accepted characters for free text search).

To conclude, here are some simple examples:
  • AUTHOR(“John Doe”) - will search for authors whose names have exactly the words John followed by Doe in any part of their names
  • DOI({123456:1132/121(56)789}) - will search for the document that has a 123456:1132/121(56)789 DOI identifier.
  • ID(“1234.1224”) - will search for the document that has a 1234.1224 external identifier.
  • AUTHOR({John Doe OR Martha Hari}) - will search for authors whose names are John Doe OR Martha Hari in any part of their names (probably not finding anything).
  • AUTHOR({John Doe}) OR AUTHOR(“Martha Hari”) - will search for authors whose names have either the sequence John Doe or Martha Hari in any part of their names.

To begin, go to data.mendeley.com and click Sign In in the top right section of the page. Upon logging in, the Mendeley Data homepage appears. Click Find Research Data. The Search page displays.

Note: While it is possible to perform a search without doing so, logging into Mendeley Data provides more metadata content.

Enter keywords into the Search field, then click the Search icon (magnifying glass) or click Enter. To filter results, check the desired check boxes in the left column. You can filter by Data Type, Repository Type, and Source.

Note: While you are able to make multiple selections from the Data Type option, you may only make one selection from the Repository Type and Source options. Also, if you select Dataset or Text from the Data Type option, it will automatically default the Repository Type to Data Repository and if you select Slides or Geospatial Data from the Data Type option, it will automatically default the Repository Type to Article Repository. The number displayed behind each filter type indicates the number of results for that type.

Search results appear in the right panel, and each results line item displays basic information such as Contributors, Date, Source, and more.

To view more detailed information, click the title of one of the results line items to expand it, where you will see in the left column under Details, one or more files associated with it.

Enabled by deep indexing, clicking on a file allows you to quickly preview its contents without having to open it. Also, you can verify that keywords in your original search terms are present inside a file, as indicated if a green check mark is next to it.

Note: Although it is possible to upload a zip file, you cannot preview its contents. You will need to download the zip file to view its contents.

After previewing the results, if you decide this is what you are looking for, then you can access full information by clicking either More Details or Go to source, depending on what you are viewing.

For example, depending on the Source, clicking Go to source allows you to download, cite, share, or export the content, and clicking More Information opens the home page of a dataset and allows you to download the files and have full metadata access.

Advanced Search

You can perform an advanced search query by use syntax for different ways to search for data such as searching within fields and using Boolean search terms. For more information about syntax for an advanced search, simply click Advanced search help beneath the search field and then click What are the syntax criteria for an advanced search (Field codes and Boolean)? This will reference the FAQ associated with this topic that will help you complete the advanced search.

Datasets

To improve the usability and performance of the Edit Dataset form, we introduced folders and have disabled the ability to manually re-order your files. Now, files and folders are automatically sorted in alphabetical and numerical order and cannot be re-ordered. This change is aligned with the common behavior of desktop file systems such as Windows and MacOS and ensures we can deliver a much quicker and more usable interface. Previously published datasets will maintain the file order that was chosen. However, files associated with draft datasets and new versions of previously published datasets will be automatically sorted in alphabetical and numerical order.

Yes, as an institutional user you can. Metadata is useful because datasets can be described in more detail and more consistently according to a standard template. This helps the institution with organizing data, and researchers with discovering and interpreting data. Your institution can set a custom metadata template with fields for all datasets. Every author at your institution will be presented with the additional fields on their dataset. The following custom metadata fields are currently supported:

  • Free text
  • List of options (Single-select)
  • List of options (Multi-select)
  • Date

Fields can be either mandatory or optional, and visible or hidden on the published dataset page.Custom metadata is displayed on the dataset page in a metadata section.

Once a dataset is drafted, it is in what is referred to as a “draft” state, meaning it has not yet been published. While in this state datasets can be edited and deleted. However, once a dataset it published, it is not possible to edit or delete it. For example, when you draft a dataset but do not publish it, it appears in My Datasets and has a status of “Draft” in the Published Version column.

If you publish a dataset and need to modify it, you will do so by using a new version of it. Once you begin editing the dataset, for instance to change any of the metadata, or to add or remove files, you are actually editing the next version which is a replication of the original one.

For example, your published dataset appears in My Datasets and has a status of “Version 1” in the Published Version column. If you click Edit draft, the dataset opens, allowing you to make changes. However, you will see in the right panel under the Published information section that the Status filed shows “Draft (Version 2)”, the Published version field shows “Version 1”, and the Visibility field is “Private”.

This new version is visible only to yourself and anyone you invite as a collaborator or with whom you share the link. This new version will also be in the draft state until it is ultimately published. If you start to edit the dataset again after it is again published, you will be editing the next iteration - “Draft (Version 3)”, for example. All published versions of a dataset can be viewed and compared by clicking the links in the Version history.

To begin, go to data.mendeley.com and click Sign In in the top right section of the page. Upon logging in, you the Mendeley Data homepage appears.

What are the options for initially drafting a dataset?

Option A: From the Mendeley Data homepage, click Create a Dataset. The New Dataset window opens, prompting you to enter a name. Then click Create Dataset.

Option B: Alternatively, also from the Mendeley Data homepage, you can click My Datasets, and then click the New Dataset button in the top left section of the page.

Option C: The final option is for institutional users with access to projects. Click My Projects and then, from the list of displayed projects, click on a project. Then, click “+ Add a new shared dataset”. Enter the name and then click Save.

How do I add or remove contributors?

To add contributors, click the “+” symbol in the Contributors section beneath the Title field. Then enter the email address and first and last name (required), and optionally, you may also include the Institution and details about the individual’s contribution. Finally, click Save Contributor. To remove a contributor, click on the individual’s name and then click Remove Contributor.

Note: By default, your name appears as a contributor since you created the dataset. However, you may remove yourself ONLY after at least one other contributor is added to the dataset.

How do I add metadata to my newly drafted dataset?

Once you have taken the preliminary step to create a dataset, you then must enter pertinent metadata. From the top of the new dataset page, you can edit the title.

Enter a description into the Description field.

To add data into the Data field you can click “+ Click or drop any file type to upload”, which prompts you to either upload a file from your computer by navigating to, selecting a file, and clicking Open from the Windows explorer window, or by clicking a file and dragging it onto the “+ Click or drop any file type to upload” area. Once the file upload is complete it will appear in the Data section. You can add a file or an entire folder, including zip files. For more information about file size limits refer to“What are the maximum size and types of files that can be uploaded to Mendeley Data?”.

The Institution field is optional and automatically populates your institution by default. However, you may add other institutions if needed. To do so, enter an institution name.

You are required to add at least one category in the Category field before you publish a dataset. To do so, start typing in the Category field and then you can select from the list that populates as you type. You are not able to add a custom category so choose one that is closest to describing the category you need.

Note: At this point, after having added data, you have effectively drafted your dataset and it is automatically saved into the system. If necessary, you can stop at this point and return to it later to edit or delete it. Refer to the help article, “How do I edit or delete a draft dataset?”. Otherwise, you can continue completing the information.

Optionally, you may add details about how to reproduce the research represented in your dataset by entering these steps in the Steps to reproduce field.

You can also add additional metadata by adding details in the Date the data was collected and Nature of the data fields, respectively.

The License field defaults to “CC BY 4.0” but you can change that by clicking Change and making a selection from the dropdown menu, and then clicking Change License. The All tab is displayed by default, but you can further specify the license type to be changed by selecting the Data, Software, or Hardware tab and then selecting the license type and clicking Change License.

If appropriate you can also add a reference to your dataset by clicking the Add Reference button at the bottom of the page. A window opens, prompting you to indicate a reference type by selecting the Article, Dataset, or Software radio button, entering a URL link, and selecting from the Relationship dropdown menu. You will then click Add reference. The newly added reference appears in the References field near the bottom of the page of your dataset.

How do I preview a newly drafted dataset?

Once you have entered all the information into your newly created dataset, you may want to see how it will appear. To do so, click Preview at the bottom of the page, which will display how it will look once it is published.

What if I want to defer the availability of data to a later date (place a dataset 'under embargo')?

When drafting a dataset, a user may choose to defer the date at which the data becomes available to ensure it is only available at the same time as an associated article, meaning the dataset description and files are not publicly available until that date.

To place a dataset under embargo, it must be in the draft state. Click Set Embargo Date at the bottom right area of the page. The Set Embargo Date window opens, prompting you to indicate the time length of the embargo by either selecting 3 months, 6 months, or 1 year or by setting a date using the calendar feature. Once a selection is made, click Set Embargo.

You can edit or delete a dataset only while it is in the “Draft” state, meaning until the point that you publish it. To do so, you must first locate it. Refer to FAQ, “What is the difference between a “draft” dataset and a “published” dataset?”. After logging into Mendeley Data, from the Datasets tab, click My Datasets. You will see a list of datasets. Only original versions of datasets that are labeled “Draft” in the Published Version column can be edited or deleted.

How do I edit a draft dataset?

To edit a draft dataset, click Edit draft (pencil icon) to open it. You will now be able to make edits. For more detailed information about the differences between editing draft and published datasets, please refer to “What is the difference between a “draft” dataset and a “published” dataset?”.

How do I delete a draft dataset?

To delete a draft dataset, click Delete (trashcan icon). A dialogue box appears, asking you to confirm that you want to delete the dataset. If so, click Delete. If you decide not to delete the dataset, click Cancel or close the dialogue box by clicking the ‘x’ in the top right corner.

How do I delete a published dataset?

To delete a published dataset, click Delete (trash icon). A dialogue box appears, informing you that it is necessary to contact the support team in order to request the deletion, or that you can revert it back to the last published version.

Once you have drafted a dataset and added all the pertinent information, you can publish it by clicking Publish at the bottom right section of the page. Notice that beneath the Publish button, you will see an indication of the version you are about to publish. Once you click Publish you will be prompted to check several boxes indicating that you understand and agree to the conditions under which the dataset will be published.

Note: Any visitor to a Mendeley Data dataset page can preview files in a published dataset without having to download the files. This only applies to publicly available files in that dataset.

Folders are a way to group and logically organize the files in your dataset, in the same way that you would organize the files on your computer. You can add a maximum of 200 folders.

How do I add a folder?

From within the dataset, click New Folder next to the “Data” field. A new folder will appear inside the Data section.

Next, to add a name, hover your mouse over the folder and enter a new name in the field currently populated with the words, “New Folder”. To add a description, again, hover your mouse over the folder and click “+Description”. A text filed expands, allowing you to type a description of the folder - up to 3000 characters. Use this same method to also edit the name and description of a folder.

How do I create sub-folders?

To quickly create a sub-folder simply click on an existing folder and then click New Folder. A new sub-folder will be created within the selected folder.

Note: You can create sub-folders up to 8 folders deep. You can also move a folder into another folder to make it a sub-folder. You cannot re-order folders, as these will always be sorted alphabetically/numerically.

If you need to move one or more folders into another one, then check the box next to each of the folders you wish to move, and then click Move. A dialogue box opens prompting you to select the folder you want to move the folders into. Next, click Move here. These will now be sub-folders inside the folder into which you moved them.

How do I delete a folder?

You can delete a folder if it has no files in it. If the folder has any files you must delete them first. To delete a folder, simply check the box next to the desired folder and then click Delete. You will not be asked to confirm the deletion so be sure this is what you want to do.

Similar to adding files and folders to a dataset, you can add files or entire folders directly into an existing folder in your dataset.

What are the options for adding content?

Option A: To add content directly into a folder, click on the folder to select it and then drag the files onto the drop zone in the Data section, or click “+ Click or drop any file type to upload”, depending on your browser. The file will be placed directly inside the folder without the need to move it.

Option B: After creating a folder, you can move content (files and folders) that are listed in the data section into it. Check the box next to the item(s) you wish to move and then click Move. A dialogue box opens prompting you to select the folder you want to move the content into. Next, click Move here. These will now be placed inside the folder into which you moved them.

Note: You can add a zip file. However, its contents cannot be previewed. So, you will have to download the zip file to view its contents. You can also add nested folders, branching up to 14 levels deep, from the default root folder.

Projects

Available to our institutional customers, Projects are an active research data collaboration tool which enables research groups to gather, organize, annotate, and share data all in one place. Projects allow you to collect your research data as it is generated, easily share it privately with your group, and organize and label it for easy retrieval and packaging. It also helps you get more value from the data you generate by enabling you to share better quality data to increase the likelihood of your data being used and cited.

You can visit https://data.mendeley.com/projects to create a private project data space and invite other researchers to join you or access any project you have previously been invited to. Once you are working on a project, you can connect a range of data sources, upload data to draft datasets, collaborate with your team on datasets, and eventually publish your data.

If your data files are held on a storage service such as Dropbox, Google Drive, Box or Azure, you can connect the folders where they are held, to any of your projects. Then as new files are added, they will become available to your project team to view, share, and add to datasets. If your team uses a storage service we do not currently connect to, let us know so we can investigate supporting it.

Upon signing into data.mendeley.com, click the My Projects tab. The “My Projects” page displays a list of all existing projects for which you are either the owner or have been invited to join by another owner. This is the point from which you will add a new project.

From the Projects page, click “+ New Project”. The “New Project” dialogue box opens, prompting you to enter a Name and Description for the new project. Then, click Create project.

How do I add members and data to a project?

Now that you have created the project, you can connect a range of data sources, upload data to draft datasets, collaborate with your team on datasets, and eventually publish your data.

You may first want to add collaborators to your project. To do so, click the Members icon in the top right corner. The Manage Project Members window opens, prompting you to search for members by name or by their email address if you know it. Click Search. A list of names will appear. Click + Add next to the name you want to add. Now, you must assign a project role to this person as follows:

  • An editor can edit the project and any datasets within it.
  • An owner can edit and delete projects.
  • A project administrator can do different things, like assign owners and members.
  • A viewer can only view the project but cannot change anything about it.

Once you assign the role, click Save. Repeat these steps for each member you wish to add to the project. Now, click Close to close the “Manage Project Members” window.

Note: If an individual you wish to add is not already a registered Mendeley Data user, when you click Search you will get a notification from the system, “Users not found”, upon which you will then be prompted to send an invitation via email for the person to create an account and then subsequently join your project. Click Send email invite to initiate this procedure.

How do I edit a project’s metadata?

From the Projects page, click Settings (gear icon) in the top right corner and select Edit metatada. The Edit Project window opens, allowing you to edit the Project title or Project description. Once you have finished your edits, click Save Changes.

How do I manage data in my project?

From within your project there are three main sections on the page:
  • Data sources – allows you to add a folder from an external cloud storage data source, such as Dropbox, Box, Google Drive, and One Drive, in order to share files with your team.
  • Shared datasets – allows you to create a dataset to share date with your project team.
  • Published data – allows you to view published datasets in your project.

How do I add data sources?

To add data sources, from inside the Data sources section, click Add data sources. The Select a data source window opens, allowing you to select the data source from which you want to add a folder. In the Existing data source section click on the icon (for example Google Drive), and then click Select.

Note: If your data source is not already connected to Mendeley Data, you can add it by clicking an applicable icon from the Create new data source section in the “Select a data source” window. Follow the on-screen instructions and read the notices associated with connecting your external account.

Once you select the data source from which you want to share a folder, the Select folders to add to project window opens, prompting you to select the folder you want to share and then click Share Folders. The folders you selected will appear in the “Data sources” section in your project.

Note: You can only share folders, not individual files. Therefore, you would need to first share a folder and then look in it to locate and download a particular file. You may also drill down into a folder to share sub-folders inside of it.

The folders you add to the Data sources section in your project that are from a cloud storage data source automatically sync with Mendeley Data. So, if you go to your Google Drive and for example, make edits to folders or delete sub-folders, the actions you perform there will replicate identically inside your folder structure in the “Data sources” section of your project.

To easily add content from within the folders in your project into a dataset in your project, clicking on a folder and navigate to the contents inside it or in a sub-folder within it. Once you locate the file you wish to add to a dataset, simply drag it into the dataset in the Shared datasets section and click Copy. A copy of the file will be added to the root of the dataset.

Note: Any updates made to the original source file will NOT sync with a copy that you dragged into the dataset.

How do I share datasets?

To share datasets, from inside the Shared Datasets section, click “+ add a shared dataset”. The Your shared datasets box opens, prompting you to add a Dataset name and click Save.

Once the dataset is added, you can click on it and a window opens, allowing you to edit it. Click Edit to begin. Refer to “How do I draft and edit a dataset?” and go to section “How do I add information to my newly drafted dataset?” for more details about how to add data files or edit fields in a dataset. Repeat the above steps to add additional datasets to your project.

<>How do I publish shared datasets from within my project?

Once you have added datasets to the project, you can publish them from within the project. From inside the Shared Datasets section, click on the dataset you wish to publish. In the right panel, scroll down and click Edit to open the dataset. I may verify that all the information is correct, and/or you make any final edits, and click Publish. Refer to “How do I publish a draft dataset?” for more information. Your dataset will appear in the Published data section of your project.

How do I add contents from folders into a shared dataset in my project?

After adding folders and datasets to your project, you can easily add content from within the folders to datasets, by using one of the following options:

Option A: Click on a folder and navigate to the contents inside it or in a sub-folder within it. Next, you can download the files onto your computer. Now, from the Shared Datasets section, click the dataset to which you want to add files. Click Edit to open the dataset. Refer to Help article, “How do I draft and edit a dataset?” and go to the section labeled “How do I add information to my newly drafted dataset?” for instructions on how to add data into the Data field.

Option B: Click on a folder and navigate to the contents inside it or in a sub-folder within it. Once you locate the file you wish to add to a dataset, simply drag it into the dataset in the Shared datasets section and click Copy. A copy of the file will be added to the root of the dataset.

Note: Any updates made to the original source file will NOT sync with a copy that you dragged into the dataset.

To edit or delete a project you must have the role of the owner or the admin of the project. While logged in, go to My Projects and search for the project you want to edit or delete.

To make edits, click the project. Refer to “How do I create a project in Mendeley Data?”. For instructions on inviting more members, editing metadata, or adding more data sources and data folders to the project.

You will only see the Delete button available if you are the project owner. If the button is available click Delete. A dialogue box opens, asking you to confirm the deletion, as it cannot be undone. Click “Delete” to confirm.

Note: Any of datasets that were linked to the project you deleted will still be available in the respective My Datasets section of the person to whom it belongs.

Collections

After logging in at data.mendeley.com, click the main tab, Datasets. Then, click My Collections. This is the point from which you can add a new collection. To add a new collection, click the “+ New Collection” button. The “New Collection” dialogue box opens, prompting you to enter a Name and Description for the new collection. Then, click Create collection.

How do I add a dataset to a collection?

Now you will add items to your collection. Mainly you will add datasets, but you can also add articles or even other collections. To add a dataset, click “+ Add Dataset”. This opens a search window, prompting you to enter keywords into the Find Research Data field.

Note: By default, the check box to only show results from your institution is checked. You can uncheck this if you choose to include datasets from sources outside your institution.

From the left panel, you can filter your search results by Data Types, Source Types, and Sources (if you opted to deselect the default setting to only show datasets from your institution).

Note: If you need to perform and advanced search, simply click Advanced search help beneath the search field and then click What are the syntax criteria for an advanced search (Field codes and Boolean)? This will reference the FAQ associated with this topic that will help you complete the advanced search.

Search results appear on the right panel of the window. To view more detailed information and to preview its files, click the title of a dataset. To add the dataset to your collection, click “+ Add to collection”. This selection is now added to your new collection.

How do I edit a collection’s metadata?

You have the option to edit the metadata of your collection. Click Edit Metadata. The Edit Collection information dialogue box opens, allowing you to do the following:
  • Edit the name
  • Add or remove contributors
  • Add or remove categories and institutions
  • Edit the description

Note: for instructions on adding and removing contributors, refer to “How do I draft and edit a dataset?” and go to section “How do I add or remove contributors?”. Collections are not required to have any contributors.

Once you have made your edits, click Save. You may also add additional datasets or delete a dataset by clicking Remove next to the dataset you want to delete.

How do I publish a collection?

Once you have created the collection, added datasets to it, and made any necessary edits, click Publish once you are ready to publish it. A dialogue box appears, prompting you to confirm that you want to publish the collection. Click Publish to confirm.

Your collection now appears as a line item in the search results panel on the “My Collections” page with a status of “Published”. Collections are also searchable and will appear in the Find Research Data section of Mendeley Data as well as on your institution's homepage.

Regardless of whether a collection is in the draft state or is published, it is easy to edit, unpublish, or delete it.

How do I edit a collection?

To make edits to a collection, go to My Collections in Mendeley Data, locate the collection you want to edit, and then click Edit (pencil icon). This opens the collection and allows you to edit the metadata and add or remove datasets. You can also unpublish it from here if desired. For instructions on editing a collection, refer to “How do I create a collection?” and go to section “How do I edit a collection's metadata?”

How do I unpublish a collection?

To unpublish a collection, go to My Collections in Mendeley Data, locate the published collection you want to unpublish, and then click Unpublish. This will revert the collection back to “Draft” status.

How do I delete a collection?

To delete a collection, go to My Collections in Mendeley Data, locate the collection you want to delete and complete the following steps accordingly:
  • If the collection is in “Draft” status, simply click Delete. You will be asked to confirm the deletion by clicking “Delete” once more.
  • If the collection is “Published”, follow the steps to unpublish it, which will revert it to “Draft”, and then delete as described above.

Moderation

Yes, we support customer moderation of datasets prior to go-live for customers who wish to do this. When an author associated to your institution submits a dataset for publishing, it appears in the customer moderation queue, allowing delegated customer moderators to view the dataset metadata and download the files. They can then decide to approve the dataset to go live, return it to the author with comments to request changes, or directly edit its title or description with changes. Moderation enables data curators and librarians to curate and maintain the quality of dataset outputs associated to the institution. All datasets submitted for publishing, whose author is associated to your institution, will appear in your queue for moderation.

When an author associated to your institution (or delegated customer moderator) submits a dataset for publishing it appears in your customer moderation queue at https://data.mendeley.com/moderation, allowing you to view the dataset metadata, download the files, and decide what you wish to do next. All datasets submitted for publishing, whose author is associated to your institution, will appear in your queue for moderation. For detailed information refer to “How do I moderate my Institution’s Mendeley Data datasets?”

To begin, go to data.mendeley.com and click Sign In in the top right section of the page. Upon logging in, the Mendeley Data homepage appears. Click the Datasets tab and then click the Moderation tab to display the Datasets in moderation page. You will see two tabs on the page - “Waiting for review” and “Returned to owner”.

How do I review a dataset before approval?

From the “Waiting for review” tab, you will see datasets that were drafted and for which publication was requested and are now awaiting review. Click View Draft next to the dataset you wish to review. A window opens, prompting you to take one of the following actions:

How do I edit the dataset title or description?

The moderator may decide to edit the title or description of the dataset on behalf of the author. To do so, click Edit dataset title and description at the top of the window. Refer to “How do I draft and edit a dataset?” and go to section “How do I add information to my newly drafted dataset?” for instructions. After completing edits, the moderator can return the dataset to the author or approve it.

How do I return a dataset to the author?

The moderator may decide to return the dataset to the author for updates. To do so, click Request Changes. The “Request changes” dialogue box opens, prompting you to enter a reason in the Reason for returning dataset to owner open text field. Click “Request changes”. The dataset will be returned to the author for further action. The dataset that you returned now appears in the Returned to owner tab.

How do I approve the dataset?

The moderator may decide to approve the dataset by clicking Approve.

Note: When reviewing the dataset, the moderator also has the option to download files associated with it.

How can I view a list of all datasets previously returned?

To view a list of datasets that were returned to the author, from the Datasets in moderation page, click the Returned to owner tab. A list of datasets displays. Click View draft to open the dataset and view its information or click Latest request to see the newest comments added when the dataset was returned to the author.

How do I view the moderation history of datasets?

From both the Waiting for review and Returned to owner tabs, click Activity next to the dataset for which you would like to see a chronological history. You will see when the dataset was first submitted, when it was subsequently returned, as well as moderator comments that were added each time it was returned.

Data Monitor

Most content on Mendeley Data Monitor is updated daily, including updates of the repositories that we index directly, and repositories indexed via DataCite. Scopus information is updated bi-weekly and information from Scholix is updated every six months.

Yes. However, you may experience temporary discrepancies due to the daily updates.

Upon signing into data.mendeley.com, the Datasets homepage opens by default. Click My Datasets. The My Datasets page displays a list of all the datasets for the institution with which you are affiliated. For librarians who wish to monitor their institution’s datasets (when they are published as well as the repositories where they are located), click the Data Monitor tab at the top of the page. A list of datasets displays.

Note: Your user role must have the appropriate privileges in order for the Data Monitor tab to appear.

Is it possible to search with a DOI and find relevant data?

Yes, with the DOI() field code users can search for datasets by DOI. With the IS_SUPPLEMENT_TO() field code users can search for datasets that are supplements to a publication identified by a DOI. Boolean operators apply to both fields, so you can do DOI(a OR b OR c) and IS_SUPPLEMENT_TO(a OR b OR c…). See also How do I search for data, including advanced search options? for more information.

How do I filter what datasets appear?

To filter the list of results, use the filters in the left panel. You can filter by date of publication and enter a specific date range. Click the calendar icons to enter From and Until dates. Or, you may choose to select one of the radio buttons to filter by Anytime, Last 3 months, or Last 12 months.

Additional filters include Repositories and Data type. Simply enter search words in each of the filter’s respective text fields or select one or more check boxes from each of the filter’s respective lists, of which the Repository list is in descending order (repository with most datasets at the top). You are also able to make multiple selections for each.

At the top of the left panel is the Institutional datasets dropdown menu. This allows you to make a selection to filter by the following:
  • All datasets (default setting) – this list shows all of your institution’s datasets.
  • Automatically excluded datasets – this list shows…
  • Manually excluded datasets – Once completely processed, this list shows all datasets that have been manually excluded using the Exclude feature in Data Monitor.
  • Pending additions – this list shows all datasets that were added using the Add feature in Data Monitor and are being processed.
  • Pending exclusions - this list shows all datasets that were excluded using the Exclude feature in Data Monitor and are being processed.

What other actions can I take with Data Monitor?

Once you have entered filters to generate the datasets you want to monitor, you can click the dataset title to view metadata including the description, the repository where it is located, contributors, the date it was published, the Dataset DOI/PID, and the types of data contained within it. To view the actual dataset, click the View dataset button or, if applicable, click the View article button to view the article.

How do I add datasets to my institution?

If you find it necessary to add datasets to your institution, from the menu at the top of the right panel, click Add. The Add datasets dialogue box opens, prompting you to add a dataset by copying the DOI or persistent identifier into the field, or by searching the Mendeley DataSearch database. Refer to [Which data repositories are indexed in Mendeley Data Search?] (https://data.mendeley.com/faq#find-data-18-indexing-repositories) to learn more about our dataset corpus.

Once a list of search results appears, click the “+ Add dataset” button for the dataset(s) you wish to add. If you need to do an advanced search, please refer to How do I search for data, including advanced search options? and go to section “Advanced Search”. When you are finished adding the datasets you want, click the “x” at the top right of the box to close it. The newly added dataset will appear on the list of “Pending additions”, found in the Institutional datasets dropdown menu and will show up in your institution’s dataset list as well as the showcase page in approximately twenty-four (24) hours.

Note: Only librarians with access to Data Monitor can add datasets, and only to their own institution.

How do I exclude datasets from my institution?

If necessary to exclude datasets from your institution you must first select what you want to exclude. From the list of results on the right panel, check one or more check boxes next to the desired dataset. From the menu at the top of the right panel, click Exclude. The Exclude datasets dialogue box opens, prompting you to confirm that you want to exclude the dataset(s). Click the “Exclude” button to confirm. The excluded dataset will appear on the list of “Pending exclusions”, found in the Institutional datasets dropdown menu and will be removed from your institution’s dataset list as well as the showcase page in approximately twenty-four (24) hours. Once the exclusion is completely processed, it will appear on the list of “Manually excluded datasets”, found in the “Institutional datasets” dropdown menu.

Note: We do our best to represent the original datasets in the Mendeley Data Monitor corpus. In some cases, when a dataset contains institutional affiliation information in its original metadata, this affiliation cannot be corrected in Mendeley Data Monitor. If you think you found a potential error, we advise that you first check the original dataset and if corrections are needed submit your request to the repository where the dataset is hosted.

How do I export datasets?

You may export all datasets listed in your institution’s Data Monitor page by clicking Export All and then selecting the preferred format (Data as CSV or Data as XLSX) from the dropdown menu that appears. To export a list of selected datasets you must first select what you want to export. From the list of results on the right panel, check one or more checkboxes next to the desired dataset. Then click the Export Selected dropdown menu and then select your preferred file format. Save the file to a location of your choice as you normally would. This is helpful in allowing you to analyze the information about the datasets you exported.

Besides datasets that already have affiliation IDs, we identify related publications via Scholix, and then the authors and their Institutional affiliations via Scopus for all remaining datasets in our corpus. To learn more about which repositories we index refer to Which data repositories are indexed in Mendeley Data Search?. To learn about how we use Institutional IDs for matching read Which Institutional IDs do you use for matching, and how can I check or change them?

We use a combination of the SciVal Institutional ID, Scopus Affiliation IDs and Mendeley Institutional IDs. The list of IDs has been shared and confirmed with your institutional representative, so please reach out to them or your customer consultant for more information. For more details on attribution of a dataset institution refer to How are datasets attributed to my Institution?

The integration without the API key gives you access to the source records (i.e. source metadata), and limits the number of records to 5000. The integration with the API key gives you access to the source records + duplicates clean-up + metadata enrichment and has no limit in the number of records to import. Crucially, the metadata enrichment includes institutional and author affiliation, which makes it possible to match many more datasets to your institution.

The institutional API key has been shared with your institutional representative. For more information please reach out to them.

Will the datasets coming through the Data Monitor integration be ingested directly in my Pure instance?

The Data Monitor settings page in Pure gives you the possibility to set criteria for auto-import. Those datasets matching the criteria for auto-import will be automatically ingested. Otherwise, they will be presented as import candidates.

What is the query logic implemented, when I enter both an institution name and an institution ID in the Data Monitor settings page in Pure?

For Pure 5.17.2 (or above) the query logic is: [institution name] OR [institution ID]; For Pure 5.16.3 - 5.17.1 the query logic is: [institution name] AND [institution ID];

If I have no datasets in my Pure instance, which datasets will be provided through the Data Monitor integration?

The Data Monitor integration will present datasets that match the criteria you entered in the settings page, e.g. matching your institution name or ID.

What happens if I already have some datasets in my Pure instance, will the Data Monitor integration present the same datasets as candidates again?

No, datasets that are already imported (or that were rejected) will not be presented as candidates again.

If I don't have Pure, can I still integrate with Data Monitor?

Yes. You can use either the Mendeley Data Monitor API or export records in a CSV format to integrate with your CRIS or any other institutional system. Please note: to be able to use the enriched Mendeley Data Monitor content you'll have to apply your institutional API key.

You can correct the wrong institutional affiliation of the datasets using the Exclude option in Mendeley Data Monitor. Please select dataset(s) wrongly attributed to your institution and then click “Exclude” link in the top menu. Please note that it will take ~24 hours for exclusions to be processed. You can view all your requests for exclusion. To switch the view, please use the dropdown menu in the top left corner and then select Pending exclusions. For more information on excluding to datasets refer to section “How do I exclude datasets from my institution?” in article How do I Monitor my institution’s Data?

We do our best to represent the original datasets in Mendeley Data Monitor. In cases where you find a potential error in one of the metadata fields, we advise that you first check the original dataset and if corrections are needed submit your request to the repository where the dataset is hosted. If the original dataset is correct, please notify us via your customer consultant and we'll correct the error if we can.

Admin

Mendeley Data dataset-article links can be added or removed (including existing links). This will not affect the attribution of a dataset to your Institution and therefore the dataset remains attributed to your institution.

To begin, go to data.mendeley.com and click Sign In in the top right section of the page. Upon logging in as an administrator, the Mendeley Data homepage appears. Click the Datasets tab and then click the Admin. The Admin page opens on the “Datasets” sub-tab by default. You will see four sub-tabs in the upper right area – Datasets, Users, Audit Log, and Customise Homepage.

How do I manage my institution’s datasets as an administrator?

The Datasets tab lists your institution’s published datasets. There are tabs for both Public and Draft datasets. To filter the list of datasets, enter search words in the Search field and click the Search (magnifying glass) icon or press Enter.

How do I manage public datasets?

From the Public datasets sub-tab, displayed are the Name of the dataset, the Owner, Version, Published date and time, and the DOI. You also have the ability to Manage articles associated with the dataset. Refer to “What is the difference between a “draft” dataset and a “published” dataset?” for more information about dataset versions.

To view the dataset page, click the DOI link to open it, thus enabling you to view more detailed information, download files, cite the dataset and compare versions of it.

To manage articles associated with the dataset, click Manage articles. The “Manage articles” window opens, allowing you to add an article DOI. To do so, enter the DOI in the Add article DOI field and then click “+ Add”.

Once an article is added, it can also be removed. Click Manage articles and the “Manage articles” window opens, allowing you to locate the Associated article you wish to remove and click Delete.

How do I manage draft datasets?

From the Draft datasets sub-tab, displayed are the Name of the dataset, the Owner, Created date and time, and the Last Updated date and time. You also have the ability to Edit datasets. To edit a dataset, click Edit. Refer to “How do I draft and edit a dataset?” and go to section “How do I add information to my newly drafted dataset?” for further instructions.

How do I manage my institution’s users log as an administrator?

From the Admin page, click the Users tab to see a list of users. Also, from the Users tab, you will see three sub-tabs – Admin, Moderator, and Data Monitor. These sub-tabs distinguish the different roles that users have within the system. A user’s name may appear on more than one of these sub-lists, indicating that they have multiple roles and privileges assigned to them.

To add a user, click the appropriate sub-tab (Admin, Moderator, and Data Monitor) and then click “+ Add”. A dialogue box opens, prompting you to add a User email and then click Add.

To remove a user, click Remove next to their name. A dialogue box opens, asking you to confirm the deletion. Click Remove to confirm.

How do I manage my institution’s audit log as an administrator?

From the Admin page, click the Audit Log tab to see a list of all of your institution’s dataset editing activity in chronological order. You can see the Dataset ID, Version, which Action occurred (such as “created” or “updated”), which Entity was updated (such as “license” or “draft dataset”), which User ID made the edit, and the Date (including time) the edit was made. You also have the ability to click Expand details to see more information and download the record of Action details.

You can further filter dataset editing activity by using the filters in the left panel. To view editing activity for a specific date range, click the calendar icons to enter From and Until dates. Or, you may choose to select one of the radio buttons to filter by Anytime, Last 24 hours, Last week, or Last month.

Additional filters include User ID, Dataset ID, and Action type. Each has a text field for you to enter search words.

You may also choose to select one of the radio buttons to filter by All users or Admin only.

Once you have filtered the datasets for which you want to audit editing activity, you can save a list of the results. To do so, click Export results and save the file to a location of your choice as you normally would.

How do I customise my institution’s homepage as an administrator?

From the Admin page, click the Customise homepage tab. Here you will see a section at the top called Featured Sections comprised of various topical sections on your institution’s homepage.

The main body of the page displays the contents of the “Featured sections”. These sections appear as separate areas on your institution’s homepage. Each section has within it a list of collections which include the Collection name, Owner, and Last updated date.

How do I manage a featured section on my institution’s homepage?

You can determine whether or not to display a particular section by clicking the toggle button to make it either Hidden or Public. You can also determine how a section is displayed by making a selection from the Display dropdown menu to have it appear either as a Card or Title. To allocate a position on the homepage, select a number from the Position dropdown menu.

To add a featured section to your institution’s homepage, click New featured section. The Create featured section dialogue box opens, prompting you to add a Section title and click Create. The newly added section will display, and you can now add collections to it.

To add collections to a featured section, go to the featured section to which you want to make an addition, and click Add Collections. The Collections to be added dialogue box opens, prompting you to perform a search for data attributed to your institution. Refer to “How do I create a collection?” and go to the section labeled “How do I add a dataset to a collection?” for instructions.

To edit a featured section, click Edit (pencil icon) next to the section name. The Edit [section name] dialogue box opens, prompting you to edit the Section title and then click Save.

To delete a featured section, click Delete. A dialogue box opens, asking you to confirm the deletion. Click “Delete” to confirm.

To delete a collection from within a featured section, click Remove.

Note: A dialogue box asking you to confirm the deletion DOES NOT appear. So, be sure you want to remove the collection beforehand!

FAQ

Depositing your research data, code or other research objects in a repository enables you to share your results privately with collaborators or peer reviewers. Sharing your data or code publicly supports research reproducibility, supports you with receiving credit for your work through citations and others with reuse.

Your publicly published dataset receive a DOI (“What is a DOI??”) and you may decide to share fully, restrict access to the underlying files and share metadata of your dataset, or you may set an embargo (“What if I want to defer the availability of data to a later date (place a dataset 'under embargo')?”) date to release your dataset publicly in case your results are under peer-review as part of scholarly article. To learn more about creating datasets, refer to “How do I draft and edit a dataset?”

A dataset is the term for a collection of research data files produced in the course of research for a paper or project and includes accompanying metadata. Metadata includes a description of the data, such as title, categories, contributors, licences, etc. as well as identifying who produced the data, and who may access it.

DOI stands for “digital object identifier” and is an alphanumeric code providing a unique and persistent link to specific electronically published content. Mendeley Data assigns a provisional DOI to draft datasets. It is used to make a document and/or reference uniquely identifiable from any others when your article is published and made available electronically. The DOI for a document remains fixed over the lifetime of the document. A permanent DOI for Mendeley Data submitted datasets is issued by the British Library via DataCite.

Datasets are required to have a title, description, and at least one named contributor. Datasets must be scientific in nature and consist of research data. For example, raw or processed experimental or observational data is acceptable, rather than the narrative research article which may have resulted from the research.

Datasets must NOT have already been published, and therefore may not already possess a DOI. They must NOT contain:

  • Executable files or archives that are not accompanied by individually detailed file descriptions
  • Copyrighted content (audio, video, image, etc) to which you do not own the copyright
  • Sensitive information (for example, but not limited to, patient details, dates of birth, etc.)

What are the maximum size and types of files that can be uploaded to Mendeley Data?

You can upload files of any format, up to a maximum of 10GB per dataset. Mendeley Data datasets for personal accounts have a maximum limit of 10 GB per dataset. However, if your Institution subscribes to Mendeley Data you will have the ability to create datasets up to a maximum size of 100GB. The maximum size will depend on the storage agreement that your institution has. To benefit from the additional size limit as an institutional user, you will need to connect your Mendeley account with your institutional email address. The types of files you can upload include individual and zipped files as well as folders.

Note: Zipped files are not able to be previewed.

When publishing data with our service, there is a range of Creative Commons and open software and hardware licences from which to choose. Mendeley (and parent company Elsevier) do not own the data you upload and publish using the Mendeley Data service. You retain complete control and copyright over the data and choose the terms under which others may consume and reuse it. Draft datasets can be deleted in the web interface or API and your published datasets can be deleted by contacting us. By creating a Mendeley user account and posting data, you grant our service permission to 'publish, extract, reformat, adapt, build upon, index, re-distribute, link to, and otherwise use [published data]'. We only seek to carry out these activities to the extent needed to provide services on our website and in our API for end users, such as enabling public datasets to be discovered and accessed.

Yes, datasets posted to Mendeley Data go into a moderation period to verify that requirements are met. Datasets that meet the requirements become publicly visible on the dataset index and are archived with Data Archiving and Network Services(DANS). You may contact our Support team if your dataset remains in moderation for more than a week. Please note that we do not currently validate or curate the contents of valid research datasets. After your dataset has been reviewed, you will receive an email to tell you whether it has been accepted and is publicly visible with an active DOI, or it has been flagged for not complying with the requirements for Mendeley Data datasets. Datasets that do not meet the requirements will not become publicly visible. If you spot a dataset that you think doesn’t meet our requirements, please notify us by clicking the “report” button on the dataset.

To ensure the highest level of integrity and security possible, data is stored on Amazon’s S3 servers in Ireland. Our service was extensively penetration tested and received certification. Additionally, your published datasets are archived with Data Archiving and Network Services(DANS) to preserve your data over the long term. DANS is a long-term archiving provider, which is an institute of the Dutch Academy KNAW, and of the Netherlands' national research council, NWO. We contract with DANS to archive all valid published datasets in perpetuity. The agreement ensures that the DOIs we provide for datasets will always resolve to a web page, where the dataset metadata and files will be available. Data archived at DANS is backed up and stored in three locations for redundancy.

It is not necessary to create a separate Mendeley Data account because your login credentials are valid across Mendeley as a whole.

Yes, Mendeley Data was awarded the Data Seal of Approval, the industry standard certification for data repositories. This means Mendeley Data is recognized as a trustworthy repository to deposit research data. To receive the certification, repositories are evaluated against 18 guidelines covering security, preservation, long-term availability, and other factors. See the Core Trust Seal website for more information.

Mendeley Data supports harvesting our entire repository of public dataset records using the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH) standard. This means metadata for all datasets published in Mendeley Data are available in an open format to facilitate large-scale acquisition and analysis of records, underscoring our commitment to open science. Note that institutional customers also benefit from individual OAI-PMH archives of their public data.

The Framework for Scholarly Link Exchange, known as Scholix, is an initiative to create an open global information ecosystem to collect and exchange links between research data and literature. As a contributor to Scholix, Mendeley Data sends its metadata, including links to associated articles, to DataCite, one of Scholix’s hubs that aggregates information on the links between datasets and articles. This enables broad visibility and findability of these links by anyone.

When publishing a dataset, a user may choose to defer the date at which the data becomes available (for example, so that it is available at the same time as an associated article). This means that the description and files of that dataset are not publicly available until the embargo date is reached. Meanwhile, other information about the dataset, such as the contributors, title, citation and associated articles, becomes available immediately prior to the embargo.

Yes! Once you publish your data it is given a digital object identifier (DOI) number, making it a citable reference.

Yes, your published dataset metadata is aggregated to DataCite’s metadata index (a comprehensive research datasets metadata index) and to the OpenAIRE portal, the EU’s research portal which aims to make as much European-funded research output as possible available to all.

Yes, we can integrate with your institution's local storage servers, thus enabling users to deposit files to, and retrieve them from, your storage via Mendeley Data’s interface. This means that all data file uploads and downloads go to and from your storage and allows your institution to retain full control of your data.

Yes, the following S3-compatible providers are supported:

  • Minio
  • Wasabi
  • DigitalOcean Spaces
  • Dreamhost
  • MERG

Yes, you can choose an alternative AWS region. For more information about AWS available regions, refer to the related Amazon link if you wish your data to remain in a particular jurisdiction.

Yes, you can use Mendeley Data's repository and management interfaces but choose to use your own AWS storage.

Yes, you can opt for local storage to hold data files. Additionally, file transfers are routed through, but not stored on, Mendeley Data servers in AWS eu-west-1 region located in Ireland. However, dataset metadata are stored in that same AWS region, and published data files held on institutional storage are indexed in DataSearch for full discoverability via the search engine.

Mendeley Data is an open, cloud-based research data management (RDM) platform that empowers research institutions to manage the entire lifecycle of research data, and enables researchers to discover, collect and share research data. It enables librarians and administrators to moderate, manage, report on and showcase research data output regardless of which data repository researchers use. For more details please visit our webpage where you can get more information that is relevant to your specific needs.

Mendeley Data datasets are moderated to verify that a number of requirements are met. If you find a dataset you consider does not meet these requirements or is in conflict with publication ethics, please notify us via our email form and include “I wish to report a dataset” in the subject line.

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