#  Introduction

RESOLUTE is a public-private partnership with 13 partners from academia and
industry with an overarching goal: To trigger an escalation in the
appreciation and intensity of research on solute carriers (SLCs) worldwide and
to establish SLCs as a tractable target class for medical research and
development. Through the coupling of an inclusive, “open-access ethos” to the
data, results, techniques and reagents with the highest-possible quality of
research output, RESOLUTE expects to accelerate the pace and increase the
impact of research in the field of SLCs to the global benefit of basic
academic research through to applied research in SMEs and pharmaceutical
companies.

Data management plays a major role in the project by providing a central data
repository for collecting, curating, sharing, integrating, analysing and
publishing data generated across the individual work packages and consortium
members. This data management plan (DMP) describes the infrastructure,
policies and life cycle for research data generated and/or processed
throughout the RESOLUTE project. It is intended to be a living document and
will be updated throughout the project whenever significant changes occur. The
official and public data management plan is updated at mid-term (Deliverable
8.2) and a final data management plan is presented at the end of the project
(Deliverable 8.3).

#  Research Data

Virtually every individual task of the RESOLUTE project depends on or produces
data in order to develop reagents, deorphanize SLCs or set up assays. On the
one hand, existing data in the public domain or with permissive licenses is
collected and compiled into the RESOLUTE knowledgebase (Deliverable 8.4). A
majority of data, on the other hand, is newly generated, processed and
analysed throughout the project. This process is supported by the RESOLUTE
database (Deliverable 8.5). Both resources are accessible from the RESOLUTE
web portal at https://re-solute.eu (Deliverable 8.6; see Figure 1). For more
details, please refer to _Data Curation: the RESOLUTE Data- and Knowledgebase_
on page 12.

**Figure 1.** RESOLUTE web portal.

Ultimately, data generated in course of RESOLUTE will be disseminated through
the RESOLUTE knowledgebase, among other means (see also _Data Lifecycle_ on
page 13).

##  Data Types

A multitude of types of data is expected to be produced in the course of the
RESOLUTE project, ranging from primary data over scientific publications to
reagents, analysis tools and workflows. The following table describes the
different data types and levels, and their corresponding format, standards and
relations in detail. Please note that this list will be significantly extended
during the course of the project. ▪ **RNA-Seq measurements of cell lines**  
  
<table>  
<tr>  
<th>

_Data Description_

</th>  
<th>

RNA-Seq measurements of various cell lines using NGS

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

_Data Type_

</td>  
<td>

Transcription profiling

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

_Data Level_

</td>  
<td>

primary (raw)

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

_Data Format_

</td>  
<td>

FASTQ (community accepted, open, human readable format)

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

_Data Source_

</td>  
<td>

Illumina high-throughput sequencing of isolated RNA

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

_Meta Data_

</td>  
<td>

  * RNA isolation protocol 

  

  * Library preparation protocol 

  

  * Instrument type 

  

  * Instrument method 

  

  * Sample size 

  

  * Sample description (source, treatment, timepoint, etc.) 

  

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

_Quality Control_

</td>  
<td>

Standardized quality control using FastQC software to assess:

  * Per base sequence quality (Phred scores) 

  

  * Per base sequence content 

  

  * Per sequence GC content 

  

  * Per base N content 

  

  * Sequence duplication level 

  

  * Adapter content 

  

  * K-mer content 

  

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

_Data Origin_

</td>  
<td>

Task 1.5 (parental cell lines, knockout and overexpression cell lines,
required controls)

Task 7.1 (knockout cell lines of priority SLCs under perturbation conditions)

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

_Data Author_

</td>  
<td>

Boehringer (T1.5), Sanofi (T7.1)

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

_Data Relation & Utility _

</td>  
<td>

Serves as input to mapping to a reference genome (see _Alignments of RNASeq to
reference genome_ below).

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

_Data Volume_

</td>  
<td>

10 TB (expected)

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

_Data Sustainability_

</td>  
<td>

Data can be duplicated to following public repositories:

  * EBI’s sequence read archive (SRA) 

  

  * Gene Expression Omnibus (GEO) 

  

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

##  ▪ Alignments of RNA-Seq to reference genome  
  
<table>  
<tr>  
<th>

_Data Description_

</th>  
<th>

Mapping of RNA-Seq to a reference genome

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

_Data Type_

</td>  
<td>

Transcription profiling

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

_Data Level_

</td>  
<td>

derived (processed)

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

_Data Format_

</td>  
<td>

BAM: Binary Alignment Map (community accepted, open, binary format)

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

_Data Source_

</td>  
<td>

Processing of raw reads from _RNA-Seq measurements of cell lines_ on page 5

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

_Meta Data_

</td>  
<td>

  * Read trimming and clipping method 

  

  * Used reference gene and transcript model (e.g. ENSEMBL GRCh38, release 94) 

  

  * Software version and parameters used for read mapping 

  

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

_Quality Control_

</td>  
<td>

Custom analysis scripts to assess:

  * Gene body coverage (no 3’ bias) 

  

  * Mapping rate 

  

  * Unique mapping rate 

  

  * Gene model mapping rate 

  

  * Replicate correlation 

  

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

_Data Origin_

</td>  
<td>

Task 1.5 (parental cell lines, knockout and overexpression cell lines,
required controls)

Task 7.1 (knockout cell lines of priority SLCs under perturbation conditions)

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

_Data Author_

</td>  
<td>

CeMM

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

_Data Relation & Utility _

</td>  
<td>

Serves as input to quantitative gene expression analysis (see _Quantitative
gene expression of cell lines_ below).

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

_Data Volume_

</td>  
<td>

10 TB (expected)

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

_Data Sustainability_

</td>  
<td>

Because of straightforward reproducibility, data from this intermediate step
in transcription profiling will not be submitted to external repositories.

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

##  ▪ Quantitative gene expression of cell lines  
  
<table>  
<tr>  
<th>

_Data Description_

</th>  
<th>

Quantitative gene expression analysis of various cell lines

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

_Data Type_

</td>  
<td>

Transcription profiling

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

_Data Level_

</td>  
<td>

interpreted (analysed)

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

_Data Format_

</td>  
<td>

CSV comma-separated values (community accepted, open, human readable format)

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

_Data Source_

</td>  
<td>

Analysis of gene-model mapped reads from _Alignments of RNA-Seq to reference
genome_ on page 5.

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

_Meta Data_

</td>  
<td>

  * Data normalization procedure 

  

  * Software version and parameters used for quantification 

  

  * Statistical model used 

  

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

_Quality Control_

</td>  
<td>

Custom analysis scripts to assess:

  * Clustering on PCA analysis of replicates 

  

  * Reproducibility of previously published transcription profiles (if available) 

  

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

_Data Origin_

</td>  
<td>

Task 1.5 (parental cell lines, knockout and overexpression cell lines,
required controls)

Task 7.1 (knockout cell lines of priority SLCs under perturbation conditions)

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

_Data Author_

</td>  
<td>

CeMM

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

_Data Relation & Utility _

</td>  
<td>

  * Supports decision process on cell line selection for assay development in Work Package 3 

  

  * Input to priority list generation in Task 4.2 

  

  * Input to integrative analysis in Task 7.3 

  

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

</td>  
<td>

\- Data from Task 1.5 will be published in the RESOLUTE knowledgebase

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

_Data Volume_

</td>  
<td>

0.5 TB (expected)

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

_Data Sustainability_

</td>  
<td>

Data can be duplicated to following public repositories:

\- EBI’s sequence read archive (SRA)

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

##  ▪ SLC-wide viability screen  
  
<table>  
<tr>  
<th>

_Data Description_

</th>  
<th>

Pooled viability screen using a SLC-wide sgRNA library

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

_Data Type_

</td>  
<td>

pooled sgRNA profiling

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

_Data Level_

</td>  
<td>

primary (raw)

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

_Data Format_

</td>  
<td>

BAM (community accepted, open, binary format)

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

_Data Source_

</td>  
<td>

Illumina high-throughput sequencing

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

_Meta Data_

</td>  
<td>

  * Library preparation protocol 

  

  * Instrument type 

  

  * Instrument method 

  

  * Sample size 

  

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

_Quality Control_

</td>  
<td>

Standardized quality control using in-house scripts to assess:

\- Per base sequence quality (Phred scores)

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

_Data Origin_

</td>  
<td>

Task 2.5 (Genetic interactions)

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

_Data Author_

</td>  
<td>

CeMM

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

_Data Relation & Utility _

</td>  
<td>

Serves as input to mapping reads to the sgRNA library (see _Quantitative
viability in SLC-wide screen_ below).

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

_Data Volume_

</td>  
<td>

2 TB (expected)

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

_Data Sustainability_

</td>  
<td>

Data can be duplicated to following public repositories:

  * EBI’s sequence read archive (SRA) 

  

  * Gene Expression Omnibus (GEO) 

  

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

##  ▪ Quantitative viability in SLC-wide screen  
  
<table>  
<tr>  
<th>

_Data Description_

</th>  
<th>

Viability of SLC genes as count tables of mapped sgRNAs

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

_Data Type_

</td>  
<td>

pooled sgRNA profiling

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

_Data Level_

</td>  
<td>

derived (processed)

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

_Data Format_

</td>  
<td>

CSV comma-separated values (community accepted, open, human readable format)

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

_Data Source_

</td>  
<td>

Demultiplexing and mapping of reads from _SLC-wide viability screen_ above to
a reference library.

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

_Meta Data_

</td>  
<td>

  * mapping parameters 

  

  * sgRNA library used 

  

  * barcodes used 

  

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

_Quality Control_

</td>  
<td>

Standardized quality control using in-house scripts to assess:

  * Mapping rate of barcodes 

  

  * Mapping rate of guides 

  

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

_Data Origin_

</td>  
<td>

Task 2.5 (Genetic interactions)

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

_Data Author_

</td>  
<td>

CeMM

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

_Data Relation & Utility _

</td>  
<td>

Serves as input to genetic interaction analysis (see _Genetic interaction
analysis_ below).

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

_Data Volume_

</td>  
<td>

0.1 TB (expected)

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

_Data Sustainability_

</td>  
<td>

Because of straightforward reproducibility, data from this intermediate step
in sgRNA profiling will not be submitted to external repositories.

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

##  ▪ Genetic interaction analysis  
  
<table>  
<tr>  
<th>

_Data Description_

</th>  
<th>

Statistical model and network for genetic SLC-SLC interactions based on pooled
viability screens in knock out cell lines.

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

_Data Type_

</td>  
<td>

pooled sgRNA profiling

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

_Data Level_

</td>  
<td>

interpreted (analysed)

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

_Data Format_

</td>  
<td>

CSV comma-separated values (community accepted, open, human readable format)

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

_Data Source_

</td>  
<td>

Analysis of viability tables from _Quantitative viability in SLC-wide screen_
on page 7.

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

_Meta Data_

</td>  
<td>

  * Data normalization procedure 

  

  * Statistical model used 

  

  * Network representation thresholds 

  

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

_Quality Control_

</td>  
<td>

Custom analysis scripts to assess:

  * Count distributions and outlier detection 

  

  * Clustering on PCA analysis of replicates and time points 

  

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

_Data Origin_

</td>  
<td>

Task 2.5 (Genetic interactions)

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

_Data Author_

</td>  
<td>

CeMM

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

_Data Relation & Utility _

</td>  
<td>

  * Supports decision process on cell line selection for assay development in Work Package 3 

  

  * Input to priority list generation in Task 4.2 

  

  * Input to integrative analysis in Task 7.3 

  

  * Data from Task 2.5 will be published in the RESOLUTE knowledgebase 

  

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

_Data Volume_

</td>  
<td>

0.1 TB (expected)

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

_Data Sustainability_

</td>  
<td>

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

##  ▪ Interaction proteomics based on mass spectrometry  
  
<table>  
<tr>  
<th>

_Data Description_

</th>  
<th>

Mass spectra of interacting proteins

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

_Data Type_

</td>  
<td>

SLC-focused protein-protein interactions

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

_Data Level_

</td>  
<td>

primary (raw)

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

_Data Format_

</td>  
<td>

mzML (community accepted, open, XML-based format)

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

_Data Source_

</td>  
<td>

LC-MS/MS (Thermo Orbitrap Mass Spectrometer)

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

_Meta Data_

</td>  
<td>

  * Sample preparation protocol 

  

  * Instrument type 

  

  * Instrument method 

  

  * Sample size 

  

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

_Quality Control_

</td>  
<td>

Standardized quality control using ProteomeDiscoverer / Skyline to assess:

  * Total ion chromatogram 

  

  * Visual inspection of the “ion map” 

  

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

_Data Origin_

</td>  
<td>

Task 2.6 (AP-MS)

Task 7.2 (BioID, AP-MS)

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

_Data Author_

</td>  
<td>

CeMM

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

_Data Relation & Utility _

</td>  
<td>

Serves as input to peptide identification and quantification (see _Protein-
protein interaction data_ below).

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

_Data Volume_

</td>  
<td>

5 TB (expected)

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

_Data Sustainability_

</td>  
<td>

Data can be duplicated to following public repositories:

\- ProteomeXchange

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

##  ▪ Protein-protein interaction data  
  
<table>  
<tr>  
<th>

_Data Description_

</th>  
<th>

Peptides and proteins identified and quantified

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

_Data Type_

</td>  
<td>

SLC-focused protein-protein interactions

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

_Data Level_

</td>  
<td>

derived (processed)

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

_Data Format_

</td>  
<td>

mzID (community accepted, open, XML-based format)

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

_Data Source_

</td>  
<td>

Searching MS/MS spectra (from _Interaction proteomics based on mass
spectrometry_ on page 8) in a reference database and quantifying signal
intensity.

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

_Meta Data_

</td>  
<td>

  * reference protein database 

  

  * search parameters 

  

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

_Quality Control_

</td>  
<td>

Standardized quality control using in-house scripts to assess:

  * amount of contamination 

  

  * success rate of peptide-spectrum-matching 

  

  * score distributions in a target-decoy approach 

  

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

_Data Origin_

</td>  
<td>

Task 2.6 (AP-MS)

Task 7.2 (BioID, AP-MS)

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

_Data Author_

</td>  
<td>

CeMM

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

_Data Relation & Utility _

</td>  
<td>

Serves as input to interaction network analysis (see _Protein interaction
network_ below).

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

_Data Volume_

</td>  
<td>

1 TB (expected)

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

_Data Sustainability_

</td>  
<td>

Data can be duplicated to following public repositories:

  * ProteomeXchange 

  

  * PRIDE 

  

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

##  ▪ Protein interaction network  
  
<table>  
<tr>  
<th>

_Data Description_

</th>  
<th>

Statistical model and network for SLC focused protein-protein interactions.

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

_Data Type_

</td>  
<td>

SLC-focused protein-protein interactions

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

_Data Level_

</td>  
<td>

interpreted (analysed)

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

_Data Format_

</td>  
<td>

CSV comma-separated values (community accepted, open, human readable format)

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

_Data Source_

</td>  
<td>

Integrative analysis of identification and quantification data from
_Proteinprotein interaction data_ on page 9.

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

_Meta Data_

</td>  
<td>

  * Data normalization procedure 

  

  * Negative controls used 

  

  * Statistical model used 

  

  * Network representation thresholds 

  

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

_Quality Control_

</td>  
<td>

Custom analysis scripts to assess:

  * Intensity distributions and sample outlier detection 

  

  * Clustering on PCA analysis of replicates 

  

  * Integration of subcellular localization data 

  

  * Integration of CRAPome reference data sets 

  

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

_Data Origin_

</td>  
<td>

Task 2.6 (AP-MS)

Task 7.2 (BioID, AP-MS)

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

_Data Author_

</td>  
<td>

CeMM

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

_Data Relation & Utility _

</td>  
<td>

  * Input to priority list generation in Task 4.2 

  

  * Input to integrative analysis in Task 7.3 

  

  * Data will be published in the RESOLUTE knowledgebase 

  

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

_Data Volume_

</td>  
<td>

0.1 TB (expected)

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

_Data Sustainability_

</td>  
<td>

Deposition of high-confidence interaction data at:

  * BioGRID 

  

  * STRING 

  

  * IntAct 

  

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

##  ▪ Quantitative ionomics  
  
<table>  
<tr>  
<th>

_Data Description_

</th>  
<th>

Quantitative determination of different isotopes

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

_Data Type_

</td>  
<td>

Ionomic profiling

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

_Data Level_

</td>  
<td>

primary (raw)

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

_Data Format_

</td>  
<td>

CSV comma-separated values (community accepted, open, human readable format)

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

_Data Source_

</td>  
<td>

ICP-MS analysis of cell lysates

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

_Meta Data_

</td>  
<td>

sample name, date of analysis, processing method, tune parameters

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

_Quality Control_

</td>  
<td>

calibration curves, CV of replicates

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

_Data Origin_

</td>  
<td>

Task 2.8 (Determination of SLCs using ions as (co-)substrates)

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

_Data Author_

</td>  
<td>

Vifor (International) Ltd.

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

_Data Relation & Utility _

</td>  
<td>

characterization of SLCs on ion activity input to priority list generation in
Task 4.3

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

_Data Volume_

</td>  
<td>

5 GB (expected)

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

_Data Sustainability_

</td>  
<td>

potential data duplication to public repositories _(to be discussed)_

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

##  ▪ Cell-based or cell-free assays using fluorescence readout  
  
<table>  
<tr>  
<th>

_Data Description_

</th>  
<th>

Quantitative SLC transporter assays, such as calcein quenching assay,
fluorescent SLC or ligand binding or internalization assays, fluorescence
polarization assay

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

_Data Type_

</td>  
<td>

Spectrophotometric data, high content imaging (HCI)

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

_Data Level_

</td>  
<td>

primary (raw) / derived (processed) / interpreted (analysed)

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

_Data Format_

</td>  
<td>

Excel, Columbus, TIFF, GraphPad Prism, Microsoft PowerPoint and Word

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

_Data Source_

</td>  
<td>

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

_Meta Data_

</td>  
<td>

sample name, date of acquisition, filter set, settings, pixel data

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

_Quality Control_

</td>  
<td>

Performance of assay confirmed by appropriate controls and reference substrate
/ inhibitors; checked by a second person before release

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

_Data Origin_

</td>  
<td>

Task 3.5 (Assays involving fluorescence); Task 6.1 (Assay development)

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

_Data Author_

</td>  
<td>

Vifor (International) Ltd.

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

_Data Relation & Utility _

</td>  
<td>

Establishing assays to support WP3 and WP6

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

_Data Volume_

</td>  
<td>

0.5 TB (if HCI assays performed)

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

_Data Sustainability_

</td>  
<td>

These data are not compatible to public repositories. Could be part of
scientific manuscripts or reports.

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

##  ▪ Functional assays  
  
<table>  
<tr>  
<th>

_Data Description_

</th>  
<th>

Generation of robust cell-based and cell-free assays for SLCs

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

_Data Type_

</td>  
<td>

Functional readout systems

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

_Data Level_

</td>  
<td>

interpreted (analysed)

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

_Data Format_

</td>  
<td>

Depending on the technology used: Kinetics or end-point values;
substrates/activators dose/response curves

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

_Data Source_

</td>  
<td>

Primary data by the new developed assays on parental cell lines, knockout and
overexpression cell lines, purified expressed proteins, with their negative
controls.

Data from public sources (i.e. publications, databases, patents etc.).

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

_Meta Data_

</td>  
<td>

  * Data normalization procedure 

  

  * Software version and parameters used for quantification 

  

  * Statistical model used 

  

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

_Quality Control_

</td>  
<td>

  * Data robustness (i.e. RZ’ factor) and reproducibility inter-experiments and over time 

  

  * Reproducibility of eventual previously published data 

  

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

_Data Origin_

</td>  
<td>

Work package 6 (Generation of robust cell-based (high-throughput) and/or cell-
free assay systems for all proteins on the SLC priority list)

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

_Data Author_

</td>  
<td>

all participants of work package 6

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

_Data Relation & Utility _

</td>  
<td>

  * Input from WP1: stable cell lines and/or constructs’ generation 

  

  * Input from WP2: SLCs potential substrates for orphan SLCs 

  

  * Input from WP3: most suitable technology to be used to set a functional assay 

  

  * Input from WP4: priority target list to work on 

  

  * Input from WP5: available expressed SLC proteins, for cell-free assay set up 

  

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

_Data Volume_

</td>  
<td>

0.5 TB (expected)

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

_Data Sustainability_

</td>  
<td>

Data can be duplicated to following public repositories, upon consortium
agreement:

\- IUPHAR or NIH databases

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

##  ▪ SLC homology models  
  
<table>  
<tr>  
<th>

_Data Description_

</th>  
<th>

Homology modeling

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

_Data Type_

</td>  
<td>

3D models

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

_Data Level_

</td>  
<td>

derived (processed)

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

_Data Format_

</td>  
<td>

atomic coordinates in PDB format (community accepted, open, human readable
format)

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

_Data Source_

</td>  
<td>

public repositories (PDB, HGNC)

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

_Meta Data_

</td>  
<td>

  * Alignment files 

  

  * Unrefined homology models 

  

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

_Quality Control_

</td>  
<td>

  * Accuracy of the template/target alignment 

  

  * Structure evaluation programs 

  

  * Enrichment calculations to assess the models’ utility for virtual screening 

  

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

_Data Origin_

</td>  
<td>

Task 8.5 (Data mining)

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

_Data Author_

</td>  
<td>

UniVie

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

_Data Relation & Utility _

</td>  
<td>

  * Helps understanding the mechanism of transport 

  

  * Useful to conduct SAR studies 

  

  * Helps to predicts new compounds 

  

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

_Data Volume_

</td>  
<td>

2 TB (expected)

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

_Data Sustainability_

</td>  
<td>

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

This data management plan is a living document and this section on Data Types
will be updated and extended regularly. Further data types to be defined
comprise e.g. targeted and untargeted metabolomics data, small molecule
binding, functional bioactivity data, high-content screening data, scripts,
algorithms and software tools for data analysis and visualization, QSAR
equations, computational models, and data mining workflows.

**Data Ownership**

Data is inherently owned by the institution of its producer (see also
_Consortium Agreement_ paragraph 7.1.1).

#  Data Curation: the RESOLUTE Data- and Knowledgebase

While data physically resides on the CeMM storage server (see _Secure Storage_
on page 17), the RESOLUTE data- and knowledgebase represent the central points
of data and knowledge management, data curation and data access throughout the
RESOLUTE project.

The RESOLUTE database (Deliverable 8.5) provides an interface to _primary_ ,
_derived_ and _interpreted_ data as well as corresponding _metadata_ .
Moreover, it is not limited to data, but is also used to identify and manage
material (e.g. reagents, biological samples, etc.) and events (e.g.
experiments, measurements, shipping, etc.). Thus, it enables linking data to
its origin and allows efficient implementation of data lineage and data
provenance tracking. The RESOLUTE database has a restricted area, accessible
to the participants only, and a public area, which is openly accessible.

The RESOLUTE knowledgebase (Deliverable 8.4) comprises mainly _reference_
data, i.e. data compiled from external databases and resources. It is a
comprehensive and accurate resource of integrated state-of-the-art knowledge
on the various SLC families. Over the course of the project, it will be
expanded by selected _interpreted_ data sets, i.e. analysis results from data
produced in RESOLUTE. The RESOLUTE knowledgebase is largely openly accessible
and contains some extra views accessible to the participants only.

In summary, the RESOLUTE database is experiment and data set centric, while
the RESOLUTE knowledgebase focuses on biological entities. Both tools will
link to each other wherever appropriate.

##  Data Lifecycle

The generation of data takes place at the sites of participants and initially
is stored locally. After passing a local quality control and an appraisal of
relevance, data will be uploaded to the RESOLUTE database. This submission
process requires annotation of metadata. Data in the RESOLUTE database is
automatically available to all participants (see _Data Sharing_ on page 14).
To be publicly accessible, data has to go through a data dissemination
procedure (see _Data Dissemination_ on page 14). Selected interpreted data
will be also published in the RESOLUTE knowledgebase.

**Figure 2.** Schema of data lifecycle including the data dissemination
procedure in RESOLUTE.

##  Data Identifiers

We developed a RESOLUTE identifier schema following recently published
recommendations for the field of life sciences  1  . The identifiers have to
be short and robust, as they will not only be used for data but also for
reagents and therefore have to be written/read to/from test tubes regularly.

A RESOLUTE identifier starts with two letters, specifying the type of entity
(e.g. CE for cell line). The main part consists of four alphanumeric
characters specifying the actual entity. In the end, an optional checksum
character adds redundancy and allows spotting typos. The main part encodes an
incremental number, using the digits 09 and the letters A-Z with the exception
of D, I, J, L, O, Q, U and Y because of their similarity to other digits or
letters. This results in an alphabet of 28 symbols and thus a total of up to
614,656 words using four characters. The checksum character is taken from an
extended alphabet (including Q, U and Y) and is calculated as modulo 31 (i.e.
the next prime number). The identifier is not case sensitive.

This RESOLUTE identifier schema applies to all entries of the RESOLUTE
database, and is therefore not limited to data, but also used to identify and
manage material (e.g. reagents, biological samples, etc.) and events (e.g.
experiments, shipping, etc.).

##  Data Sharing

All data generated or compiled is openly shared among all participants (see
_Consortium Agreement_ paragraph 8.1.2). Data is shared either via the
RESOLUTE SharePoint (for documents, reports, etc.) or via the RESOLUTE
database (for primary, derived and interpreted data).

For sharing data with the scientific community, please refer to _Data
Dissemination_ below.

##  Data Quality and Annotation

As well-structured and high-quality data is essential to higher-level analyses
and data reuse, we implement multiple layers of data documentation, annotation
and quality control.

When uploading data to the RESOLUTE database, a submission form requires the
author to provide annotation and metadata, including consistent linkage to
existing data. Once in the database, data can be reviewed and curated by all
participants. While standardized data annotation is required (see also _Making
data findable_ on page 15), an additionally implemented Wiki system encourages
also informal data annotation by free text comments.

As we expect RESOLUTE reagents and data to lay the foundation of future SLC
research, highest possible quality standards have to be met. We implement
quality control on three levels: A first quality control step happens at the
site of data generation, and specification of corresponding quality control
metrics (see _Data Types_ on page 5) is required when submitting to the
RESOLUTE database. In a second step of quality control, a _data release
proposal_ with a focus on data annotation and quality control has to be
prepared before the public release of a data set. In case of a scientific
publication in a journal, the peer-review process forms the third layer of
quality control and assessment of data annotation. To ensure a peer-review
step also for data releases without scientific publication, we implement _data
annotation and release workshops_ (see _Data Dissemination_ below).

##  Data Dissemination

All relevant data, i.e. data generated or collected by the RESOLUTE project
that passed certain quality control and data appraisal procedures, should be
rapidly published open access. Publication procedure follows one of three
different routes (please also refer to the three columns in the central panel
_RESOLUTE database_ in Figure 2):

  * **data release:** Any participant can propose data for public release by submitting a written _data release proposal_ to the Work Package 8 team, specifying the following details: 

    * an abstract on the data, o the motivation for publication, o the data identifiers, o the annotation status, 

  

    * the standards compliance, o assessment of quality control, o license to use for publication. 

  

  

Work Package 8 team will collect these proposals for further discussion in
_data annotation and release workshops_ , taking place regularly at consortium
meetings or possibly also as tele conferences in between. Participation at the
workshop is voluntary, and all participants will receive the collected data
release proposals two weeks before the workshop. At the workshop, the
proposals will be discussed in detail with the aim of curating data, extending
and refining annotation and strengthening quality control, but there will be
no immediate decision taken on publication. Within a week after the workshop,
Work Package 8 team together with the proposal’s authors will send out a
refined version of the data release proposal to the _publication and
dissemination officers_ of all consortium partners. In case there are no
objections within a 30 days review period, the data will be published and
openly accessible in the RESOLUTE database. Minor objections can be directly
resolved with the objecting participant, while major objections require an
adaption of the proposal, effectively restarting the review period.

  * **supporting a scientific publication:** Data might also be disseminated as integral part or supplemental material to a scientific, peer-reviewed publication. For this route, the same _data release proposal_ as described above has to be submitted to Work Package 8 team, but a data annotation and release workshop is not required. All scientific publications will follow the _gold_ or _hybrid_ open access policy by submitting the manuscripts to open access journals or by paying for this option in the subscription-only journals. For more details on dissemination in the form of scientific publications, please refer to the _Dissemination and Exploitation Plan_ (Deliverable 9.2). 

  

  * **end of project:** At the end of the project (i.e. July 2023) we intend to release and make openly accessible a vast majority of all data within the RESOLUTE database that was not published before. 

  

#  FAIR Data

Participants of the RESOLUTE consortium acknowledge that living a culture of
openness for this project will benefit the whole community of solute carrier
research. By ensuring that all generated research data will be findable,
accessible, interoperable and reusable (FAIR), the RESOLUTE data- and
knowledgebase will efficiently lead to discovery, integration and innovation.
To support these efforts, we established contact to the IMI project FAIRplus,
which is expected to start early 2019\.

##  Making data findable

To make something findable, a synthesis of identifiability, locatability,
actionability and discoverability is required. All data in the RESOLUTE
database will be _identifiable_ by their automatically assigned, unique and
persistent identifier (see _Data Identifiers_ on page 13). The data is
_locatable and actionable_ by a direct mapping of the identifier to an URI
(e.g. https://re-solute.eu/CL0001) as well as a corresponding CURIE (compact
URI, e.g. RES:CL0001), which will be registered at the resolving system of
identifiers.org  2  3  . The RESOLUTE data- and knowledgebase allows published
data to be _discoverable_ in a manual way via the user-friendly web interface,
and in an automated way by implementing the Bioschemas  4  specification.

In addition, data is annotated with an abstract and keywords (enabling free
text search), a set of generic metadata (i.e. the data author, a link to the
experiment and sample, timestamps, etc.) and a set of specific metadata (see
_Data Types_ on page 5). By building on established metadata standards (e.g.
Dublin Core  5  , ISA  6  , MIBBI  7  , etc.) and controlled vocabularies, we
increase the findability and interoperability of the data.

Furthermore, we also encourage participants to use Open Researcher and
Contributor Identifiers (ORCIDs) for referencing their contribution to
published datasets.

To further increase exposure and findability, we will register the RESOLUTE
data- and knowledgebase at FAIRsharing  8  , a registry for research standards
and databases, and re3data  9  , a registry for research data repositories.

##  Making data openly accessible

All data generated or collected by the RESOLUTE project is posed certain
quality control and data appraisal procedures. Data of sufficient quality and
relevance will be stored and shared in the RESOLUTE database and, ultimately,
will be openly accessible. Refer to _Data Dissemination_ on page 14 for the
different routes and timelines of data publication.

Data is available at the RESOLUTE web portal (https://re-solute.eu;
Deliverable 8.6), which forms the entry point to the RESOLUTE data- and
knowledgebase. They provide open access to published scientific papers,
reports and research data without the need for authentication or special
software (apart from a standardscompliant modern web browser). In addition,
corresponding metadata and licensing information is provided.

We will also provide advanced tools for optimal data exploitation, i.e. a web
based query system featuring interactive graphs and reports, and APIs and
workflow support (e.g. KNIME  10  nodes) for automated database access and
data mining (Deliverable 8.7).

##  Making data interoperable

Coherent use of non-proprietary, standardized data and metadata formats,
aiming at maximum compliance with open source software applications, ensures
interoperability of RESOLUTE data. To enable inter-disciplinary
interoperability, relevant existing resources are referenced and metadata
standards established across research domains are used (see also _Making data
findable_ on page 15).

##  Increasing data re-use

We support data re-usability by a rapid data generation to publication
timeline, based on our streamlined data release process (see _Data
Dissemination_ on page 14). Upon publication, data is openly accessible (see
_Making data openly accessible_ above) and re-usable for third parties by a
permissive open access standard license, provided either by the Creative
Commons  11  or by the Open Data Commons  12  project. Data re-usability is
also increased by employment of strict data quality assurance processes (see
_Data Quality and Annotation_ on page 14).

Algorithms or analysis software tools developed in the course of the project
that might be needed to re-use data and re-produce results will be published
alongside the data.

#  Data Security

While there is no highly sensitive data (like patient data) in the RESOLUTE
project and all data will be ultimately open access, data security is still a
necessary and important issue. The following paragraphs list actual
implementation and infrastructure details ensuring secure data storage, access
and transfer.

**Figure 3.** Overview on RESOLUTE data- and knowledgebase infrastructure and
technology stack.

##  Secure Storage

Documents like SOPs, protocols, reports, or meeting minutes, are
collaboratively written and stored in a SharePoint environment via a cloud-
hosted service (Microsoft Office 365).

Data (primary, derived and interpreted data) are stored at local IT
infrastructure of CeMM, managed by the CeMM IT department. The IT
infrastructure at CeMM is currently in the process of receiving a significant
upgrade to its storage hardware, and is expected to be fully operational mid
of 2019. The full scale of estimated required storage volume for RESOLUTE (see
_Data Stewardship_ on page 18) will then be available.

To minimize the risk of data loss, a local backup strategy as well as a cloud
backup strategy is in place. The local backup runs incrementally and writes to
magnetic tapes (IBM Backup system). The cloud backup is provided by a
Microsoft Azure cloud service and fulfils the rules of the EU General Data
Protection Regulation (GDPR). To rule out data manipulation or corruption we
employ MD5 checksums.

Access to the CeMM storage server is strictly restricted and available only
from within the CeMM intranet. Therefore, special means of secure data access
and secure data transfer have to be established to allow participants to
submit or review internal data, and to allow public users to view and download
published data.

##  Secure Access

Participants of the RESOLUTE consortium have access to all the documents and
information in SharePoint via a personalized RESOLUTE account, managed by a
Microsoft Azure Active Directory service. Implementing an OAuth2 workflow, the
same RESOLUTE account is used to securely access data and information in the
internal sections of the RESOLUTE database and the RESOLUTE knowledgebase (see
_Data Curation: the RESOLUTE Data- and Knowledgebase_ on page 12).

Authenticated as well as unauthenticated (i.e. public) access to data and
information in the RESOLUTE web portal, the RESOLUTE knowledgebase, and the
RESOLUTE database will be protected by implementation of transport layer
security using the secure communication protocol (HTTPS).

##  Secure Transfer

Data is produced by individual participants locally, but managed and stored
centrally at the CeMM storage server. Offering a smooth user experience in
this process requires fast and secure data transfer. As the CeMM storage
server is only accessible locally (see _Secure Storage_ on page 17), remote
access and transfer via the internet to the CeMM storage server has to be
mediated by some means. We employ three strategies for secure transfer:

  1. As initial solution, we used the SharePoint environment, which provides an easy-to-use interface for data down- and upload. However, transfer of large files or transfer of a bulk of files turned out to be cumbersome, and data at the SharePoint has to be moved manually from or to the CeMM storage server. 

  

  2. As temporary solution, we set up a SFTP server with one dedicated user account per participant, allowing for easy programmatic transfer of many and/or large files. Again, data at the SFTP server has to be moved manually from or to the CeMM storage server on demand. 

  

  3. Starting from mid of 2019, we will employ a dedicated data transfer server. Data will be transferred from the CeMM storage server to this data transfer server automatically upon demand, where it is then available for download via the RESOLUTE web portal, the RESOLUTE database or the RESOLUTE knowledgebase. The same setup will also be used for data upload / data submission. 

  

  

#  Data Sustainability

Published data is not only available via the RESOLUTE data- and knowledgebase,
but is also submitted to technology-specific or domain-specific, community-
accepted repositories wherever possible (e.g. EBI’s Sequence Read Archive for
next-generation sequencing data, ProteomeXchange for interaction proteomics
data, or ChEMBL for compound/transporter interaction data). For data-type
specific sustainability implementations please refer to _Data Types_ on page
5.

Data structure, data formats, metadata formats and also code base are built on
standards defined by major public initiatives, such as the World Wide Web
Consortium (W3C) or HUPO Proteomics Standards Initiative, ensuring
compatibility and sustainability.

#  Data Preservation

All data entering the RESOLUTE database will be stored for the course of the
project. The RESOLUTE data- and knowledgebase will be supported by CeMM for at
least 5 more years after the end of the project. After that, for long-term
preservation, the diligent use of domain specific standards will ensure that
all data will be transferable to corresponding certified public data
repositories. A detailed data preservation plan will follow in the mid-term
update on the data management plan (Deliverable 8.2).

#  Data Stewardship

CeMM is hosting the server infrastructure. We estimate production of a total
data volume of 300 TB throughout the project, corresponding to a data
production rate of 5 TB per month. Required resources for storage and backup
are provided by CeMM (see _Secure Storage_ on page 17) and are budgeted with a
total of 200,000 EUR.

CeMM (in particular Ulrich Goldmann) is also responsible for data management,
with strong support from UniVie (in particular Gerhard Ecker). At least one
representative per participant takes part in the monthly tele conferences on
RESOLUTE data management (Work Package 8).

Two individuals per participant are responsible for data dissemination as
_publication officers_ . In addition, all participants are encouraged to
engage in the regular _data annotation and release workshops_ (see _Data
Dissemination_ on page 14 ).

#  Ethical and Legal Considerations

There is no personal data generated in the RESOLUTE project. However, data is
generated on human cell lines and for details please refer to the ethics
report (Deliverables 10.1 and 10.2).

External data which might be collected and re-used (e.g. for the RESOLUTE
knowledgebase) is scrutinized for ethical or legal compliance beforehand.

#  Appendix

##  Abbreviations  
  
<table>  
<tr>  
<th>

API

</th>  
<th>

Application Programming Interface

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

AP-MS

</td>  
<td>

Affinity purification mass spectrometry

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

CURIE

</td>  
<td>

Compact Uniform Resource Identifier

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

CV

</td>  
<td>

Coefficient of Variation

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

DMP

</td>  
<td>

Data Management Plan

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

FAIR

</td>  
<td>

Findability, Accessibility, Interoperability, and Reusability

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

HTTPS

</td>  
<td>

Hyper Text Transfer Protocol Secure

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

ICP-MS

</td>  
<td>

Inductively Coupled Plasma Mass Spectrometry

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

ID

</td>  
<td>

Identifier

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

LC-MS/MS

</td>  
<td>

Liquid chromatography–mass spectrometry

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

MD5

</td>  
<td>

Message Digest algorithm; used for calculating file checksums

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

NGS

</td>  
<td>

Next-Generation Sequencing

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

OAuth2

</td>  
<td>

Open Authorization 2.0

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

ORCID

</td>  
<td>

Open Researcher and Contributor Identifier

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

PCA

</td>  
<td>

Principal component analysis

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

QSAR

</td>  
<td>

Quantitative Structure–Activity Relationship

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

RESOLUTE

</td>  
<td>

Research Empowerment on Solute Carriers

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

RNA-Seq

</td>  
<td>

RNA (ribonucleic acid) sequencing

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

SAR

</td>  
<td>

Structure–Activity Relationship

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

SFTP

</td>  
<td>

Secure File Transfer Protocol

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

SLC

</td>  
<td>

Solute Carrier

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

SOP

</td>  
<td>

Standard Operating Procedure

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

URI

</td>  
<td>

Uniform Resource Identifier

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

XML

</td>  
<td>

eXtensible Markup Language

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

</td>  
<td>

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

##  Consortium Members  
  
<table>  
<tr>  
<th>

**short name**

</th>  
<th>

**full name**

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

CeMM

</td>  
<td>

CeMM Research Center for Molecular Medicine of the Austrian Academy of
Sciences

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

UoX

</td>  
<td>

University of Oxford

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

UoL

</td>  
<td>

University of Liverpool

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

AXXAM

</td>  
<td>

Axxam SpA

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

ULei

</td>  
<td>

Universiteit Leiden

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

MPIMR

</td>  
<td>

Max-Planck Institut für medizinische Forschung

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

UniVie

</td>  
<td>

Universität Wien

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

Pfizer

</td>  
<td>

Pfizer Ltd.

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

Novartis

</td>  
<td>

Novartis Pharma AG

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

Boehringer

</td>  
<td>

Boehringer Ingelheim

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

Vifor

</td>  
<td>

Vifor International Ltd.

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

Sanofi

</td>  
<td>

Sanofi Aventis Recherche et Développement

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

Bayer

</td>  
<td>

Bayer AG

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