**3 MIND Data-sets**

The MIND Data Management Plan (DMP) has three main purposes:

  1. To support the project partners when publishing the datasets generated by the project. 

  

  2. Support the Coordinator in assuring the availability of the data generated by the project. 

  

  3. Assist external parties in analysing the work performed and accessing the data generated by the project. 

  

  

To ensure that all three purposes are fullfilled we have standardized the
information that needs to be given regarding each of the datasets generated by
the project.

The project partner responsible for producing a dataset will fill out the two
tables show below which will be added to this dokument as individula sub-
chapters to chapter 3 in this document (3.1, 3.2, 3.3 etc.). Assitance in
doing this will be available through both the work package managers and the
coordinators.  
  
<table>  
<tr>  
<th>

**Metadata**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

**Dataset reference and/or name**

</td>  
<td>

Unique name identifying the dataset. Identifier should start with EU-
MIND2020-WPx where “x” is the relevant work package number followed by a three
digit number.

Example: “ _EU-MIND2020-WP4-001_ ”

</td> </tr>  
<tr>  
<td>

**MIND Datatype**

</td>  
<td>

Choose one or more of the relevant data types: Experimental Data,
Observational Data, Raw Data, Derived Data, Physical Data (samples), Models,
Images and Protocols. Alternatives are further described in Appendix 1.

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

Source of the data. Reference should include work package number, task number
and the main project partner or laboratory which produced the data.

</td> </tr> </table>

**Dataset description** Provides a brief description of the data set along
with the purpose of the data and whether it underpins a scientific
publication. This should allow potential users to

determine if the data set is useful for their needs.  
  
<table>  
<tr>  
<th>

**Standards and metadata**

</th>  
<th>

</th>  
<th>

Provides a brief description of the relevant standards used and list relevant
metadata in accordance with the description in Appendix 1.

The usage of the Directory Interchange Format is optional.

</th> </tr> </table>

**Science Keywords** List relevant scientific key words to ensure that the
data can be efficiently indexed so others may locate the data.  
  
<table>  
<tr>  
<th>

**Data sharing**

</th>  
<th>

</th>  
<th>

Description of how data will be shared both during and after the MIND2020
project. Include access procedures, embargo periods (if any), outlines of
technical mechanisms for dissemination and necessary software and other tools
for enabling re-use, and definition of whether access will be widely open or
restricted to specific groups.

Information should include a reference to the repository where data will be
stored.

In case the dataset cannot be shared, the reasons for this should be mentioned
(e.g. ethical, rules of personal data, intellectual property, commercial,
privacy-related, security-related).

</th> </tr> </table>

**Archiving and** Description of the procedures that will be put in place for
long-term preservation of **preservation** the data.

#  3.1 “Dataset one”  
  
<table>  
<tr>  
<th>

**Metadata**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

**Dataset reference and/or name**

</td>  
<td>

…

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

…

</td> </tr>  
<tr>  
<td>

**Dataset description**

</td>  
<td>

…

</td> </tr>  
<tr>  
<td>

**Standards and metadata**

</td>  
<td>

…

</td> </tr>  
<tr>  
<td>

**Data sharing**

</td>  
<td>

…

</td> </tr>  
<tr>  
<td>

**Archiving and preservation**

</td>  
<td>

…

</td> </tr> </table>

#  3.2 “Dataset two”  
  
<table>  
<tr>  
<th>

**Metadata**

</th>  
<th>

**Description**

</th> </tr>  
<tr>  
<td>

**Dataset reference and/or name**

</td>  
<td>

…

</td> </tr>  
<tr>  
<td>

**Source**

</td>  
<td>

…

</td> </tr>  
<tr>  
<td>

**Dataset description**

</td>  
<td>

…

</td> </tr>  
<tr>  
<td>

**Standards and metadata**

</td>  
<td>

…

</td> </tr>  
<tr>  
<td>

**Data sharing**

</td>  
<td>

…

</td> </tr>  
<tr>  
<td>

**Archiving and preservation**

</td>  
<td>

…

</td> </tr> </table>

#  APPENDIX A: MIND2020 DATA TYPES

##  1 Experimental Data

###  1.1 Dataset description

The experimental data originate from measurements performed in a laboratory
environment, be it _in situ_ or _ex situ_ . The data comprise point or
continuous numerical measurements (e.g. pH, temperature), microbial counts,
dose measurements)

The data will be collected either on a sample basis (sampling an experiment at
a certain point in time) or on an experiment scale (without sampling the
experimental set-up). Data can be derived from either destructive of
preservative analyses. Experimental data collection can occur automatically or
manually, and will be available in a digital or a hard copy format. In the
case of the latter, experimental data will first be copied to e.g. a lab book
and then digitized.

Experimental data are supposed to be unique, in the way that new experiments
will be set-up, producing fresh data. In some cases, similar data will be
available from previous/other experiments within the project, within the
partners’ institution or from overlapping projects, allowing comparison and
integration of the newly obtained data.

Experimental data will be used in downstream statistical analyses (hypothesis
testing, correlations, etc.), interpretations, quantifications and modelling
approaches.

###  1.2 Standards and metadata

Experimental data are obtained using standardized laboratory techniques which
are calibrated when applicable. Positive and negative controls are used and
standards, internal or external, are introduced.

Metadata (summary information) can optionally be provided according to a
Directory Interchange Format (DIF). A DIF allows users of data to understand
the contents of a dataset and contains those fields which are necessary for
users to decide whether a particular dataset would be useful for their needs.

##  2 Observational Data

###  2.1 Dataset description

Observational research (or field research) is a type of correlational (i.e.,
non-experimental) research in which a researcher observes ongoing behaviour.

###  2.2 Standards and metadata

The metadata for observational data should include any standards used and the
necessary information so that an external researcher has the possibility to
analyse how the data was gathered.

##  3 Raw Data

###  3.1 Dataset description

_Raw data_ are primary data collected from a source, not subjected to
processing or any other manipulation.

Raw data are derived from a source, including analysis devices like a
sequencer, spectrometer, chromatograph etc. In most cases, raw data are
digitally available. In some cases (e.g. sequencing), the raw data will be
very extensive datasets.

Raw data has the potential to become information after extraction,
organization, analysis and/or formatting. It is therefore used as input for
further processing.

###  3.2 Standards and metadata

Raw data are obtained using standardized laboratory techniques which are
calibrated when applicable. Positive and negative controls are used and
standards, internal or external, are introduced.

Metadata should at least include standards, techniques and devices used.
Metadata can optionally be provided according to a DIF. A DIF allows users of
data to understand the contents of a dataset and contains those fields which
are necessary for users to decide whether a particular dataset would be useful
for their needs.

##  4 Derived Data

###  4.1 Dataset description

Derived data are the output of the processing or manipulation of raw data.

Derived data originate from the extraction, organization, analysis and/or
formatting or raw data, in order to derive information from the latter. In
most cases, derived data are digitally available, as are the raw data. Derived
data will allow for the interpretation of laboratory experiments, e.g. through
statistical analysis or bioinformatics processing.

###  4.2 Standards and metadata

Manipulation of data will be performed using a ‘scientific code of conduct’,
i.e. maintaining scientific integrity and therefore not falsifying the output
or its representation.

Metadata should include any standard or method or best practice used in the
analysis. Metadata can optionally be provided according to a Directory
Interchange Format. A DIF allows users of data to understand the contents of a
dataset and contains those fields which are necessary for users to decide
whether a particular dataset would be useful for their needs.

##  5 Physical Data (samples)

###  5.1 Dataset description

Physical data are samples that have been produced by an experiment or taken
from a given environment. Sampling of an environment or experiment is
performed in order to obtain information through analyses. As such,
experimental, raw and or derived data will be obtained from physical data.
When the analyses are destructive, the samples cannot be stored for later use.
When the analyses are preservative, samples can be stored for later use, but
only for a limited time. Environmental samples will primarily be samples from
the Underground Research Facilities and analogue sites.

###  5.2 Standards and metadata

When sampling an environment or experiment, blank samples are taken as well,
as a reference. In case of microbiological samples, a blank can be a non-
inoculated experiment.

Metadata should include description of the origin of the sample, age,
processing, storage conditions and expected viability of the sample (as some
sets of samples can only be stored for a limited time, due to their nature).

##  6 Models

###  6.1 Dataset description

Representation or simplified version of a concept, phenomenon, relationship,
structure or system used for facilitating understanding by eliminating
unnecessary components.

###  6.2 Standards and metadata

References and metadata should include existing standards of the discipline
used, tools used in the modelling and focus of the modelling.

##  7 Images

###  7.1 Dataset description

Imaging data are optical semblances of physical objects.

Objects of macro- and microscopic scale can be imaged in a variety of ways
(e.g. photography, electron microscopy), enabling the optical appearance to be
captured for later use or for sharing. When required, the optical appearance
can be magnified (e.g. microscopy) and manipulated to enable the
interpretation of the objects (mostly samples from an environment or
experiment). Imaging data support the interpretation of other data, like
experimental data. Some imaging data will be raw data (3.3), which need to be
derived through image processing to enable interpretation.

###  7.2 Standards and metadata

Advanced imaging devices are calibrated to ensure prospering visualization.

Metadata which are provided are time of imaging, device settings and
magnification/scale when appropriate. In addition, metadata will be provided
about the object that is being imaged.

##  8 Protocols

###  8.1 Dataset description

A protocol is a predefined written procedural method in the design and
implementation of experiments or sampling. In addition to detailed procedures
and lists of required equipment and instruments, protocols often include
information on safety precautions, the calculation of results and reporting
standards, including statistical analysis and rules for predefining and
documenting excluded data to avoid bias.

###  8.2 Standards and metadata

Protocols enable standardization of a laboratory method to ensure successful
replication  of results by others in the same laboratory or by partners’
laboratories.

Metadata for Protocols should include the purpose of the protocols, references
to standards and literature.