User manual  
For usage of the MO|RE data plaꢀorm  
Status: May 2023  
Responsible for the content is the project group MO|RE data of the Insꢁtute of Sports and Sports  
Science at the Karlsruhe Insꢁtute of Technology. For quesꢁons and suggesꢁons, please use the contact  
on our website.  
Content  
1.  
Reading aid: Manual........................................................................................................................ 3  
2
General informaꢁon on MO|RE data ................................................................................................... 4  
2
.1 MO|RE data in the data life cycle.................................................................................................. 5  
.2 What data goes into MO|RE data?................................................................................................ 6  
2
3
Access to MO|RE data.......................................................................................................................... 8  
3.1 Technical requirements ................................................................................................................. 9  
3.2 Registraꢁon with MO|RE data..................................................................................................... 10  
3.3 Logging on to MO|RE data .......................................................................................................... 10  
3.4 Logging out of MO|RE data......................................................................................................... 11  
3.5 Change password/ Forgoꢂen password...................................................................................... 13  
4
. Data preparaꢁon for upload in MO|RE data..................................................................................... 14  
. Submission in MO|RE data................................................................................................................ 16  
5
5.1 Data upload procedure up to submission ................................................................................... 17  
5.2 User profile: submission.............................................................................................................. 19  
5.3 Data mapping .............................................................................................................................. 20  
5.4 Upload of further data................................................................................................................. 21  
5.5. Metadata input...............................................................................Error! Bookmark not defined.  
5.6 Status check and compleꢁon of submission ............................................................................... 25  
5.7 Special features for aggregated data sets.................................................................................... 26  
5.8 Deleꢁng a data set....................................................................................................................... 27  
6. Data quality ....................................................................................................................................... 28  
6
.1 Automaꢁc filters for data quality review..................................................................................... 29  
.2 Review process by the editorial board ........................................................................................ 32  
6
7
. Search on MO|RE data...................................................................................................................... 33  
Addiꢁonal Informaꢁon on the MO|RE data project .......................................................................... 35  
8
8.1 Organizaꢁon ................................................................................................................................ 36  
8.2 MO|RE data cooperaꢁon partners.............................................................................................. 37  
8.3 Digital Object Idenꢁfier (DOI®).................................................................................................... 38  
8.4 Licensing of MO|RE data............................................................................................................. 39  
8.5. Data protecꢁon........................................................................................................................... 40  
9. Glossary ............................................................................................................................................. 41  
1. Reading aid: Manual  
On the one hand, this manual serves as an informaꢁon document for detailed informaꢁon about the  
MO|RE data project, including its background and possible uses. On the other hand, both data users  
and data providers can use the manual directly as a guide for using the plaꢀorm. For this, we  
recommend reading chapter 5-7.  
The manual refers to two roles: data providers, who submit their own data to MO|RE data for public  
use, and data users, who use data sets published on MO|RE data.  
In addiꢁon to the manual, you will find further support materials, including video tutorials on the  
individual usage funcꢁons/areas, on our homepage:  
2
General informaꢀon on MO|RE data  
MO|RE data is a discipline-specific research data repository in which collected research data of sports  
motor tests are published, prepared for citaꢁon, and archived.  
The service is primarily aimed at the enꢁre sports science community to share, use, and cite data in  
research papers using the Digital Object Idenꢁfier (DOI®). Overall, however, it is open to all data  
providers and data users with sport motor test data. The plaꢀorm offers the following funcꢁons: Data  
search, data upload with a data quality check by an editorial board team and DOI award aꢃer successful  
publicaꢁon of the data, and data archiving. All data published on MO|RE data are automaꢁcally stored  
on a permanent repository of the Karlsruhe Insꢁtute of Technology (KIT) at the Steinbuch Centre for  
Compuꢁng (SCC). For published data packages no retenꢁon period has to be selected, it is unlimited.  
KIT guarantees an actual retenꢁon of at least 10 years.  
2
.1 MO|RE data in the data life cycle  
In order to be able to manage collected research data efficiently, a data management plan is oꢃen  
draꢃed while a research project or study is sꢁll in the planning phase. It helps to structure the handling  
of data during and aꢃer the project. Many funding agencies, especially public ones, even require that  
a data management plan be draꢃed and submiꢂed as part of a proposal.  
You can find out more about the components of a data management plan on the Internet at  
www.forschungsdaten.info/themen/informieren-und-planen/datenmanagementplan.  
One of the most important components of a data management plan is data archiving and publicaꢁon.  
The subject-specific repository MO|RE data is perfectly suited for this purpose, as it was established  
for specific requirements of the subject community from sports science. Within the data cycle in Figure  
1, MO|RE data is located under data storage and release.  
Figure 1: Simplified data cycle within a project (own representaꢀon)  
Aꢃer the publicaꢁon of the data, for example via MO|RE data, other data users can use the data set to  
work on and conduct new, further studies and quesꢁons that would not iniꢁally be possible without  
this exisꢁng preliminary work. This closes the circle of the data cycle.  
2
.2 What data goes into MO|RE data?  
MO|RE data is a specific repository for research data of sports motor tests. These data can be uploaded  
as raw data (RAW) or in aggregated form (AGG).  
The following motor test items were selected due to their widespread use, scienꢁfic establishment, as  
well as the large already exisꢁng database and long-term internaꢁonal use and experience in large  
studies:  
Tab. 1 Tesꢁtems in MO|RE data  
Tesꢀtem  
-minute Run  
Test code Test implementaꢀon  
Unit  
6
6min  
Cover as far a distance as possible around a volleyball court (lap Meters  
length 54 m) or on a 400-meter track within 6 minutes.  
Push-Ups  
Sit-Ups  
PU  
Start in prone posiꢁon with hands behind back, push-up Amount in 40  
posiꢁon, one hand touching the other, push-up posiꢁon, prone sec  
posiꢁon with hands behind back  
SU  
SLJ  
Supine posiꢁon, legs fixed and fingers at the temples, torso  
upright  
Amount in 40  
sec  
Standing Long  
Jump  
Jump forward as far as possible from a standing posiꢁon with a Cenꢁmeters  
two-legged takeoff  
2
0m Dash  
20m  
20m (lb)  
From the high start  
From the high start  
Seconds  
Seconds  
20m Dash  
(lightbarrier)  
Jumping  
Sideways  
JumpSw  
In a marked field (100 x 50 cm) jumping sideways back and  
forth over a center line for 15 seconds.  
Amount of  
jumps in 15  
seconds  
Balancing Backw BalBw  
Stand and Reach St&R  
Balancing backwards on 300 cm long and 6 cm, 4.5 cm and 3  
cm wide beams  
In standing posiꢁon, bend the upper body forward downward  
as far as possible with legs extended  
Amount of  
steps  
Cenꢁmeters  
Cooper Test  
Shuꢁle Run  
12min  
SRun  
Cover as far a distance as possible within 12 minutes  
20 m increase pendulum run  
Meters  
Stages, Level  
2
-km-Walking-  
Test  
Danish Step Test DStep  
2kmWalk Complete a flat 2 kilometer course as quickly as possible using Seconds  
the walking technique, pulse measurement.  
Step up and down a stepper in a given rhythm  
VO2max  
(ml/min/kg)  
Handgrip  
Medicine Ball  
Push  
Hgrip  
MED  
Hand strength measurement with hand dynamometer  
Pushing a 1 kg medicine ball as far as possible  
Kilogram  
Meters  
Jump and Reach J&R  
From a standing posiꢁon, jump up as high as possible and touch Cenꢁmeters  
the wall at the highest point  
Walk-Back (ꢀme) WalkB-t  
Walk a distance of 6 meters backwards as fast as possible (if 6m Seconds  
mark is not reached, the furthest distance achieved is  
measured)  
Walk-Back  
distance)  
Slalom Run  
Walk-B-d Walk a distance of 6 meters backwards as fast as possible  
Meters  
(
SlRun  
Run through the obstacle course as fast as possible  
Seconds  
Single Leg Stand 1LegSt  
Target Throw TargT  
Throw and Catch T&C  
60 seconds single-leg stand on a narrow rail (3 cm)  
Throw a tennis ball into a target square  
Throw a ball through the legs to the wall and catch it again  
Error points  
Hits  
Quality raꢁng  
(from 10  
throws)  
Sit and Reach  
Si&R  
In the long seat, bend the upper body down as far as possible  
to touch the toes with the fingers  
Cenꢁmeters  
In the brochure "KOMET - Kompetenzzentrum motorische Tests. Hintergrund & Testbeschreibungen”  
you will find the detailed descripꢁons of the test items and the corresponding test baꢂeries and  
background informaꢁon.  
Only test items that were administered according to exactly these protocols and have the  
corresponding units can be mapped (cf. Chap. 5.3). Data from other test items can also be published in  
MO|RE data without the corresponding mapping of the test item. However, an exact descripꢁon of the  
test execuꢁon should be given within the metadata (Abstract field). In addiꢁon, we recommend  
assigning a unique variable name (e.g. push-up_30Sec, single-leg_5cm).  
In addiꢁon to the data of sports motor tests, anonymized data of test persons and consꢁtuꢁon values  
can be mapped and published within a data set in MO|RE data. Addiꢁonal data or, for example, an  
exact age (with decimal places) can also be published in MO|RE data, without associated mapping. We  
recommend creaꢁng two variables with unique names, e.g. age_years and age_exact, in order to use  
MO|RE data opꢁmally and not to reduce the quality of the datasets.  
Tab. 2 Other variables in MO|RE data that can be mapped  
Variable  
Comment  
Unit  
Age  
Age in whole years  
Male, Female, Diverse  
Bodyweight  
Years  
m, f, d  
Kilogram  
Cenꢁmeters  
BMI-Value  
Gender  
Weight  
Height  
BMI  
Body height  
Body-Mass-Index (Body weight in  
2
kg/Body height in cm )  
Waist size  
Cenꢁmeters  
Other addiꢁonal data (e.g., results from quesꢁonnaires, acꢁvity behavior, etc.) can be uploaded  
simultaneously within the data set, but not mapped.  
If a variable is not mapped, this means that this data cannot be linked within MO|RE data (it cannot be  
searched for specifically in the search funcꢁon). However, the data can be viewed and used by other  
data users. A careful and detailed descripꢁon of these addiꢁonal variables is therefore mandatory.  
3
Access to MO|RE data  
You can access MO|RE data either directly by entering the URL in your web browser (hꢂps://motor-  
research-data.de/) (cf. Figure 2) or by accessing the homepage of the Insꢁtute of Sport and Sport  
Science at the Karlsruhe Insꢁtute of Technology (hꢂps://www.ifss.kit.edu/more/english/index.php). If  
you access via the homepage, you will be redirected to MO|RE data via a link (cf. Figure 3).  
Fig. 2: Front page MO|RE data repository  
Fig. 3: MO|RE data link on the IfSS-Homepage  
3
.1 Technical requirements  
MO|RE data runs as a web-based plaꢀorm via all common browsers. An internet-capable device is  
required for use. We recommend using a tablet, laptop, or PC for best usability (smartphone displays  
may be too small).  
If you experience problems with your browser that do not occur when you change browsers, please  
report them to our team. We are always trying to improve MO|RE data conꢁnuously and would be  
happy if you support us with your feedback.  
We would like to explicitly point out the language seꢄngs: If integrated, turn off the automaꢁc  
translaꢁon of your browser, otherwise someꢁmes tangled translaꢁon errors appear. In the top leꢃ  
window, you can easily switch the language between German and English. However, please avoid  
changing languages during a session on the plaꢀorm, as this can irritate the system.  
3
.2 Registraꢀon with MO|RE data  
For the full use of MO|RE data, you need an access authorizaꢁon, which you can obtain by registering  
see Figure 4). You have the opꢁon of registering in either German or English. On the front page of  
(
MO|RE data you can manually select the language in the upper leꢃ menu bar (cf. Figure 4). Then start  
the registraꢁon process by clicking the Register buꢂon in the upper right menu bar. A registraꢁon form  
will open (see Figure 5). In the registraꢁon form, the mandatory fields are marked with an asterisk (*).  
All personal data you provide will be managed in accordance with DSGVO. For more informaꢁon, please  
refer to the privacy policy of MO|RE data. Aꢃer filling in the fields, click on the Register buꢂon. You will  
shortly receive a noꢁficaꢁon to the e-mail address you provided with the MO|RE data Terms of Use  
and a 19-digit access code. To complete the registraꢁon, click on the link marked in the e-mail. By  
clicking on it, you agree to the MO|RE data terms of use. The registraꢁon is now completed.  
Language seꢄng  
Register  
Fig. 4: Registraꢀon and language seꢁng on MO|RE data  
Fig. 5: Registraꢀon form for MO|RE data  
ENGLISH VERSION NOT POSSIBLE???  
3
.3 Logging on to MO|RE data  
Aꢃer you have registered once, use the Login buꢂon at the top right of the front page for each  
subsequent login to MO|RE data to get to the login screen (cf. Figure 6). Log in there using your e-mail  
address and the access code sent to you. When entering the access code, enter all the components,  
including the hyphens.  
Once you have successfully logged in, you can edit your profile details at any ꢁme. To do this, click on  
the buꢂon with your e-mail address in the upper right menu bar. Your personal data will facilitate your  
idenꢁficaꢁon or can be used for contacꢁng the Editorial Board.  
Fig. 6: Login screen MO|RE data  
3
.4 Logging out of MO|RE data  
You can log out of MO|RE data by clicking the Log out buꢂon in the upper right menu bar (see Figure  
). Use this funcꢁon to protect your data from unauthorized access. Aꢃer logging out, you will be  
7
returned to the MO|RE data front page.  
Logout  
Fig. 7: Logging out of MO|RE data  
3
.5 Change password/ Forgoꢁen password  
If you have forgoꢂen your password, you need to have it reset. To do this, click the Forgot your  
password? buꢂon (see Figure 8). Please enter the e-mail address you used to log in to this website for  
the first ꢁme. Then click on reset password. You will shortly receive a randomly generated password at  
the e-mail address you entered.  
Note: If you do not remember the e-mail address you used to register, or if you do not yet have an e-  
mail account, you must create a new account (see Chapter 3.2).  
Fig. 8: Forgoꢂen password  
4
. Data preparaꢀon for upload in MO|RE data  
Data preparaꢁon is essenꢁal for a fast and uncomplicated upload and submission of a data set to  
MO|RE data. The beꢂer the dataset is prepared, the easier the subsequent steps will work: The  
mapping of variables and the automated quality check as well as the review by the editorial board.  
For the submission of your dataset on MO|RE data, you should consider the following notes on data  
and metadata quality.  
Below you will find a checklist with corresponding comments on the data submission process.  
Tab. 3: Checklist for preparaꢀon  
Preparation point  
Comment  
Check  
Name test items  
clearly  
The more clearly the test item is labeled in its file, the easier it will be for  
you to assign it when mapping. Please use the test abbreviations from  
Table 1.  
□
Check units  
Use the same units for the test items as indicated in Table 1. Please adjust  
them if necessary and convert/allow Excel to convert.  
No names or other person-related assignments (e.g. date of birth) may be  
included in the data set! Please use person IDs!  
□
□
Check anonymity  
Observe file format MO|RE data only accepts Excel documents in .xlsx format. Please convert  
your file to this format.  
MO|RE data will only consider the first spreadsheet of your Excel  
document.  
After uploading the dataset, you cannot delete/insert/format any rows or  
columns. Please upload the dataset already as it can be published. This  
also applies to all columns that are not mapped and are attached as  
additional data (e.g. questionnaire, activity behavior).  
□
□
Completeness of  
the data set  
Arrangement of  
columns  
Arrange the columns in their dataset as follows: Motor test items  
(mapping), constitutional values and header data about the person  
□
□
(mapping), other additional data (no mapping).  
See Figure 9 for an example data set.  
The data set must not contain blank rows, blank columns, different  
formatting within a column, pseudo-syntax (e.g. ""#Null!"), characters ("-"  
Formatting within  
the data set  
"?" "\" "/""@") in the value fields.  
Please follow the recommended guidelines for document design.  
Only for aggregated Exact names of test items from Table 1 must be used.  
data  
□
Fig. 9: Sample data set for raw data  
A standardized arrangement of data sets brings many advantages: The data set can be recorded faster  
and easier and is therefore comparable as well as expandable, so that data sets can be further used.  
Consequently, data preparaꢁon is of parꢁcular importance in the submission process in MO|RE data.  
Please put your enꢁre document in as simple a form as possible. You can achieve this by keeping  
everything in black and white and making your dataset as readable as possible (cf. Figure 9).  
Tab. 4: Recommendaꢀons for document layout  
Overview  
Format  
Headlines: bold, dates: normal  
Possible formats: Text, number or date fields  
Fix top row (View -> Fix window)  
Headlines  
The funcꢁonality and quality of MO|RE data depends to a large extent on the willingness and  
cooperaꢁon of all data providers. The simplicity and comprehensibility of your data set for further use  
is essenꢁal. The quality of the datasets depends largely on your data preparaꢁon and ensures a long-  
term re-use of your collected sport motor research data. Thank you for your contribuꢁon!  
5
. Submission in MO|RE data  
The submission of a data set is the central funcꢁon of MO|RE data. At the end, the dataset including  
metadata can be published, cited with the DOI, re-used, and retrieved. In the following, the process  
from upload to submission to publicaꢁon of a dataset is described in detail. On MO|RE data itself you  
will be guided through these steps step by step.  
5
.1 Data upload procedure up to submission  
Tab. 5: Data upload and submission process  
Upload  
Upload your dataset in raw or aggregated  
dataset to form to MO|RE data. To do this, click the  
MO|RE  
data  
RAW or AGG button under Files accordingly.  
Mapping  
In this step you execute the mapping. By  
clicking on the column header, you can  
select  
a
suitable variable from the  
dropdown menu. Repeat this process for  
each column that should be mapped and  
thus searchable on MO|RE data. After you  
have mapped all columns, press the save  
button.  
Check  
To run the automated quality check, click  
the Check button. An automatic check of  
the mapped columns will run to find  
typing/input errors. If one of the 5 filters  
hits, the associated cells will be highlighted  
in red, and the problem will be listed below  
the table so that you can check the error  
message and revise it if necessary (see  
Chapter 5.1). After completing the quality  
check, click the Save button again.  
Metadata  
The next step is to enter the metadata. To  
do this, click the Metadata button to specify  
information about your dataset. Mandatory  
metadata are marked with a (*). These must  
be filled in so that a DOI can be assigned.  
When you have entered your metadata  
completely, press the Save button.  
Submission To verify your information, click the Submit  
button. If all the information is correct, you  
can complete the submission by clicking  
Send. Your mapped dataset incl. metadata  
will be forwarded to the Editorial Board for  
review.  
Submit  
Submit successful!  
Your file has been successfully submiꢂed to the Editorial  
Board. You will shortly receive a message regarding the  
status of inclusion in the MO|RE data - database.  
Send  
Dataset  
status  
overview  
You can view the current status of your  
record under Files. In the table displayed,  
your current status appears on the left  
under the Status column header:  
Uploaded - Your dataset has been uploaded  
and not yet processed.  
Uploaded* - At least one of the filters 2-5 is  
checked during the mapping process. Please  
check this before proceeding to the  
metadata.  
Submitted  
- Your dataset has been  
submitted. The Editorial Board is now  
reviewing it.  
Accepted - Your dataset has been reviewed  
and accepted by the Editorial Board.  
5
.2 User profile: submission  
In your user profile, you can get an overview of all completed submissions under Files. The following  
informaꢁon can be viewed:  
Status of the dataset (cf. 5.6)  
File name of the data set  
Date of upload to MO|RE data  
Assigned DOI® name for already published datasets  
Data set type: raw data (RAW) or aggregated data (AGG)  
Here you can manage your saved files, e.g. open or delete, find DOI® names for citaꢁon. Please note  
that any modificaꢁon of an already published dataset is no longer possible, as these files are on  
permanent storage (immutability of data).  
Furthermore, the iniꢁal upload of a data set is checked for uniqueness of the delivery. If an already  
exisꢁng, idenꢁcal data record is detected during the upload (can concern content or ꢁtle), the upload  
is stopped, and you receive the following message: "an idenꢁcal file already exists in MO|RE data. The  
delivered file is not accepted (no saving!)".  
5
.3 Data mapping  
Data mapping means that a column is assigned to a specific variable. Data mapping is essenꢁal for  
merging with other data sets and retrieving their data. This mapping is performed for motor test data  
and other personal variables (see Table 1&2).  
For each column that you want to map and is mappable, you can select a variable from the drop-down  
menu. Each variable can only be selected once per data set. At the end of the mapping, we ask you to  
check the columns again so that all variables that should be searchable on MO|RE data have been  
selected.  
Finally, you have the possibility to save the mapping scheme to use it for future datasets (e.g., for  
annual project datasets). The saved mapping scheme can be modified for a new data set when it is  
called up.  
Please note that only test items that have been performed according to the descripꢁons given in Table  
1
can be mapped. The data can also be uploaded without mapping, but care should be taken to include  
an exact descripꢁon in the metadata (Abstract field). In addiꢁon, we recommend assigning a unique  
variable name (e.g. push-up_30sec, single-leg_5cm etc.).  
In addiꢁon, it should be noted that with a few mapped variables, the probability of filter 4 or 5 (cf.  
Chapter 6.1) being hit is very high. Since these can be overridden, nothing stands in the way of the  
upload, but it should be noted.  
Please note for aggregated datasets that the exact names of the test items from Table 1 must be used.  
The following columns can be mapped here: "Test item, unit, gender, age, mean and standard  
deviaꢀon".  
Note: If necessary, change the language seꢄng before mapping (see Error! Reference source not  
found.). If you have already started with the mapping in German, it is important that you keep this  
language seꢄng in the further process. You can map the dataset in English as well as in German.  
Depending on the language you choose, you will be presented the abbreviaꢁons in English or German.  
The language is parꢁcularly important for the mapping of gender, since here only the values m, w, d  
are allowed in German and only the values m, f, d in English (cf. Table 2).  
5
.4 Upload of further data  
In addiꢁon to the mapped data, your dataset may also contain other data from quesꢁonnaires or  
acꢁvity trackers, etc. These remain unchanged during the upload and can then be retrieved and  
published in exactly the same way. However, a dataset must contain at least the variables age and  
gender as well as a test item.  
5
.5. Metadata input  
Metadata must be entered manually in MO|RE data. Aꢃer mapping and the quality check, you can  
store all supplementary informaꢁon on your data set here. There are certain obligatory metadata  
without which no DOI® assignment and thus no upload can take place. These are marked with a (*).  
All other informaꢁon is voluntary, but strongly recommended in order to 1) make the data  
acquisiꢁon/processing traceable and 2) to ensure opꢁmal reuse.  
The complete list of metadata can be found in the following table:  
Tab. 6: Overview metadata (M=Mandatory, R=recommended, O=opꢀonal)  
Name  
Mandatory  
informaꢀon  
Definiꢀon  
Title  
M
A ꢁtle under which the data set is known. Please enter here the ꢁtle  
under which the file should be listed in MO|RE data and displayed in  
search queries.  
Title type  
Author  
M
M
Title type(s) (other than the main ꢁtle).  
The principal researcher/author who parꢁcipated in the creaꢁon of the  
dataset, or the author of the publicaꢁon, in prioriꢁzed order. Repeat this  
property to indicate mulꢁple author  
Name of the author  
a box to ꢁck  
M
M
The full name of the author  
A personal name or insꢁtuꢁon name. The user selects whether to  
include his/her own name OR the name of the research insꢁtuꢁon by  
checking the box.  
First name  
Surname  
Affiliaꢁon  
M
M
M
First name  
Family name  
The author's organizaꢁonal or insꢁtuꢁonal affiliaꢁon.  
Co-author/ Contributor R  
Type of contributor  
Name of the co-author R  
-
R
Type of contributor  
Full name of the co-author.  
a box to ꢁck  
R
Can be a personal name or name of insꢁtuꢁon. The user chooses by  
checking the box whether to include his/her own name OR name of  
research insꢁtuꢁon.  
First name  
Surname  
Affiliaꢁon  
R
R
R
O
O
First name  
Family name  
The organizaꢁonal or insꢁtuꢁonal affiliaꢁon of the co-author.  
Research sponsor  
Name/designaꢁon of research sponsor  
Research sponsor  
Name/designaꢁon of  
research sponsor  
Publisher  
Publicaꢁon year  
M
M
Publisher of the resource  
The year from which the dataset is or will be made publicly available. For  
datasets, publish means to make the data available to the research  
community on a specific date.  
Keywords  
Abstract  
R
R
Keywords describing the data set  
Any addiꢁonal informaꢁon that does not fit into any of the other  
categories.  
Country  
R
R
R
O
R
Country  
Geographic region  
Postal code  
Primary language of the record.  
The research design used  
Region/Place  
ZIP CODE  
Language  
Temporal research  
design  
Survey method  
R
Indicaꢁon of the method used to collect the data  
Period of origin  
License  
Related idenꢁfier  
R
M
R
Period of origin of the resource  
License for the data set (cf. chapter 8.4)  
Idenꢁfiers that refer to a related resource, e.g. publicaꢁon to which the  
published record refers.  
Type of related  
idenꢁfier  
R
What is the type of the related idenꢁfier: a DOI, URL, handle, or another  
from the suggesꢁon list  
Notes Project/data set R  
Any addiꢁonal informaꢁon that does not fit into any of the other  
categories. Essenꢁal comments on the study or data collecꢁon can also  
be entered here, which can contribute to beꢂer data interpretaꢁon and,  
very importantly, also to beꢂer and more efficient data reuse.  
Were data collected by R  
trained and/or  
Quality quesꢁon 1  
experienced tesꢁng  
personnel?  
Is an ethics noꢁce  
available for the  
project/data collecꢁon?  
R
R
Quality quesꢁon 2  
Quality quesꢁon 3  
Is study-related  
documentaꢁon of data  
collecꢁon available (in  
publicaꢁon or  
metadata)?  
Aꢃer compleꢁng the metadata entry, you have the opꢁon to save this metadata in your profile to use  
it for future datasets. When you retrieve the saved metadata again, you can adjust it for a new dataset.  
5
.6 Status check and compleꢀon of submission  
You can end the submission by clicking the Submit buꢂon aꢃer entering the metadata. This forwards  
your mapped dataset including metadata to the Editorial Board for review and gives it the status  
pending for the ꢁme being. You can check the status of your dataset at any ꢁme via your user profile.  
You will also receive an email noꢁficaꢁon when the Editorial Board review is complete.  
If the dataset has no objecꢁons from the Editorial Board (cf. Chap. 6.2), it will be marked as accepted.  
Your dataset with associated metadata is now published on the plaꢀorm, can be found in searches, and  
is prepared for citaꢁon with a DOI®. You can sꢁll view all details in your profile.  
If the dataset was rejected, you can re-upload the dataset in a corrected form using the Editorial Board's  
instrucꢁons.  
5
.7 Special features for aggregated data sets  
Aggregated data sets are usually smaller data sets. It is important that the exact names of the test items  
from Table 1 are used.  
For example, the file may look like this:  
Fig. 10: Example for aggregated data  
Deviaꢁons concern the following areas:  
-
-
-
Mapping (cf. Ch. 5.3)  
Quality filter (cf. Ch. 6.1)  
Search (cf. Ch. 7)  
5
.8 Deleꢀng a data set  
Uploaded data sets can be deleted as long as they have not yet been submiꢂed.  
If you wish to delete a dataset that has already been published, this will only be done in jusꢁfied  
excepꢁonal cases and only aꢃer consultaꢁon with the MO|RE data team.  
The MO|RE data team also reserves the right to delete data records for jusꢁfied reasons. The reasons  
for deleꢁon are, for example, legal violaꢁons, incorrect data in the data record or license changes to  
the data record that are not compaꢁble with the licenses on MO|RE data.  
In case of a deleꢁon, only the data is deleted, but not the metadata. These contain a note that the data  
has been deleted.  
6
. Data quality  
The data quality assessment process consists of two parts (cf. Figure 11: Q1 and Q2). The first part is a  
review of the data quality using defined filters (Ch. 6.1). The second part is a review by the editorial  
board team, a so-called review process (Ch. 6.2).  
Fig. 11: Quality management in MO|RE data  
Aꢃer successful peer review of the dataset, your research data will be automaꢁcally published on  
MO|RE data and assigned a persistent idenꢁfier, a DOI®.  
6
.1 Automaꢀc filters for data quality review  
To ensure the quality of the uploaded data records, all data records undergo an automated check. This  
serves you as a service, e.g. to find typing/entry errors. Only mapped columns are checked (see chapter  
5.3), using five filters described in table 7. The filters were derived based on representaꢁve  
internaꢁonal data.  
Please note: No cells/columns/rows are deleted. If a filter hits, associated cells are highlighted and  
listed so you can review them. With the excepꢁon of filter 1, all filters can be overridden, i.e. you can  
submit the dataset for review by the Editorial Board (EB) despite filters 2 to 5 having been hit. Then a  
red asterisk (*) appears in connecꢁon with the affected dataset. However, we ask for an explanaꢁon  
for the conspicuous data, if it is not already clear from the descripꢁon in the metadata. When hiꢄng  
filter 1, the corresponding values have to be deleted by you and the dataset has to be uploaded again.  
Depending on the size of the data set, the check may take a few minutes. We ask for a liꢂle paꢁence  
here.  
For the gender "Divers" the reference values of the gender "Male" have been used, because unꢁl today  
not enough diverse reference values are available. If you have data of the gender "diverse", we would  
like to encourage you to upload these data in order to be able to form reference values as soon as  
possible. Thank you.  
If you have any quesꢁons about the filter criteria, please feel free to contact the MO|RE data team  
(
more-data@ifss.kit.edu).  
Tab. 7 Overview of the quality filters in MO|RE data for raw data  
Sequence Filtername Descripꢀon  
Required ACTION data  
provider  
Overdrive  
possible?  
Implausible Values that are completely  
Deleꢁon of the  
NO  
1
2
values  
impossible in relaꢁon to the  
variable  
"Impossible value”  
Implausible Values of motor tests that are not Review; approval and  
values-age performed in certain age ranges jusꢁficaꢁon/reconciliaꢁon  
range  
YES  
with EB or deleꢁon of the  
value.  
Implausible Values that are not plausible in  
values-limit relaꢁon to a certain age range  
Review; approval and  
jusꢁficaꢁon/reconciliaꢁon  
with EB or deleꢁon of the  
value.  
YES  
YES  
YES  
3
4
5
Duplicates I Two or more data rows have  
completely idenꢁcal measured  
values (motor tests)  
Review; approval and  
jusꢁficaꢁon/reconciliaꢁon  
with EB or deleꢁon of the  
duplicate.  
Duplicates II Two or more data rows have  
Review; approval and  
idenꢁcal values in consꢁtuꢁonal jusꢁficaꢁon/coordinaꢁon  
values and personal data and  
idenꢁcal values in >2 test items  
with EB or deleꢁon of the  
duplicate.  
Tab. 8: Overview of the quality filters in MO|RE data for aggregated data  
Sequence Filtername  
Descripꢀon  
Required ACTION data provider Overdrive  
possible?  
Implausible  
values  
Values that are completely impossible Deleꢁon of the "Impossible  
NO  
1
in relaꢁon to the variable  
value”  
Implausible  
values-age  
range  
Values of motor tests that are not  
performed in certain age ranges  
Review; approval and  
jusꢁficaꢁon/reconciliaꢁon with  
EB or deleꢁon of the value.  
YES  
2
3
4
Implausible  
values-limit  
Values that are not plausible in relaꢁon Review; approval and  
YES  
YES  
to a certain age range  
jusꢁficaꢁon/reconciliaꢁon with  
EB or deleꢁon of the value.  
Duplicates I  
Two or more data rows have  
Review; approval and  
completely idenꢁcal values in the data jusꢁficaꢁon/reconciliaꢁon with  
rows EB or deleꢁon of the duplicate.  
Tab. 9: Applicaꢀon examples for the five quality filters SCREENSHOTS ÜBERSETZEN!!!  
Sequence Filtername Descripꢀon  
Example  
Filter examples raw data  
th  
The height of 310 cm of the 7 subject is impossible.  
Implausible Values that are  
values  
completely  
impossible in  
relaꢁon to the  
variable  
1
Implausible Values of motor  
values age tests that are not  
The test item 20m dash is not defined in the age group 3-5 years.  
range  
performed in  
certain age ranges  
2
th  
Implausible Values that are not The number of push-ups (PU) of the 4 subject is not plausible.  
values-limit plausible in relaꢁon  
to a certain age  
range  
3
4
Duplicates I Two or more data Subjects 4 and 5 have idenꢁcal values in the motor tests.  
lines have  
completely idenꢁcal  
measured values  
motor tests)  
(
Duplicates II Two or more data Subjects 4 and 5 have idenꢁcal values in the consꢁtuꢁonal values  
rows have idenꢁcal and personal data and two idenꢁcal values in the motor tests.  
values in  
consꢁtuꢁonal  
values and personal  
data and idenꢁcal  
values in >2 test  
items  
5
Filter examples aggregated data  
Impossible Values that are  
The 20m dash mean value shows an impossible value of 401 sec.  
values  
completely  
impossible with  
respect to the  
variable  
1
Implausible Values that are not The Stand&Reach mean value shows an implausible value of 39.5  
values  
boundary  
plausible in relaꢁon cm.  
to a specific age  
range  
2
6
.2 Review process by the editorial board  
The Editorial Board team consists of two editors represenꢁng Sport Science and Research Data  
Management (FDM).  
The editors check the delivered dataset:  
the completeness and correctness of the mandatory metadata (necessary for a DOI® name  
registraꢁon at DataCite)  
the details of the content and addiꢁonal metadata, which enable efficient subsequent use of  
the data by third parꢁes  
self-disclosure of data collecꢁon (three quesꢁons in the metadata sheet)  
results of the data quality check by automaꢁc filters (cf. chapter 6.1)  
comments of the submiꢂer(s) to the Editorial Board team (if available)  
Based on the abovemenꢁoned quality criteria, the Editorial Board team decides on whether the dataset  
can be published or not.  
For the following reasons, the Editorial Board team may reject the dataset if:  
-
-the metadata is incomplete (concerns mandatory metadata) or contradictory or incorrect,  
even if it is just a typo  
-
-
-
quality filters have been overridden in an unjusꢁfied and untraceable way  
the data set has mulꢁple or gross inconsistencies with the quality requirements  
the upload requirements have not been met (e.g. anonymity)  
In case of a rejecꢁon of a dataset, the data provider(s) will receive an E-mail from MO|RE data with the  
reasons for the rejecꢁon. In addiꢁon, the status rejected appears in the user profile for the respecꢁve  
data record.  
The data provider can check the rejected data record according to the reason, adjust it to the quality  
specificaꢁons of MO|RE data and upload the adjusted data record again. In this case, the dataset must  
be re-mapped and go through the review process again.  
In principle, a rejected dataset can be uploaded several ꢁmes aꢃer correcꢁon unꢁl the dataset meets  
the quality specificaꢁons and is accepted by the Editorial Board team.  
In case of a successful data publicaꢁon, the data provider will also be noꢁfied by e-mail and will see  
the status published in the user profile for the respecꢁve data set. The assigned DOI® for the published  
dataset can also be found in the user profile.  
7
. Search on MO|RE data  
The search funcꢁon on MO|RE data is freely accessible to any person. However, data sets can only be  
downloaded with registraꢁon. With the search you can search by the respecꢁve DOI® or with keywords  
and test exercises within the database.  
If you want to search for a specific test exercise, you have the possibility to select several test icons by  
clicking on them (see Figure 12). Furthermore, you can also combine your search query by entering  
corresponding keywords in the free search field next to the test icons. If you enter several search terms,  
connect them with a comma. Search as specifically as possible. Aꢃer you have executed your search  
query, you can check it using the search string highlighted in gray. The search string assigns your search  
terms to a corresponding category so that you can check whether your search was executed as desired.  
Below are some search examples:  
Tab. 10: Examples for searching on MO|RE data ABBILDUNG IN ENGLISCH!!  
Example Searchtext  
Icons  
1.  
12y, 2010  
2.  
2020, 2021, 2022, 2023, raw  
3.  
m, Germany, USA  
Matching search hits are displayed by MO|RE data and provides you with an associated overview that  
includes the following items: Survey period in years (e.g.: 2014-2017), Author, Title, RAW or AGG, N,  
m/f/d, age range.  
The columns displayed can be filtered by clicking on the column header (large-small or A-Z) OR in  
sidebar.  
As a non-registered user, you will get an overview list and can click on the result line to display the first  
line of the respecꢁve record. Addiꢁonally, you can view and download the metadata sheet, but no  
download of the dataset is possible.  
As a registered user, you will receive an overview list and can view and download the complete data  
record by clicking on the result line. In addiꢁon, you can view and download the metadata sheet.  
Fig. 12: Search funcꢀon on MO|RE data ABBILDUNG AUF ENGLISCH  
8
Addiꢀonal Informaꢀon on the MO|RE data project  
In the following chapter, you will find detailed informaꢁon about the project team, which consists of  
experts from the Insꢁtute of Sports and Sports Science and the KIT Library, as well as the cooperaꢁon  
partner mb-mediasports, which takes care of the IT infrastructure. Furthermore, you can find out about  
DOI and licensing and read informaꢁon about data protecꢁon.  
8
.1 Organizaꢀon  
The organizaꢁonal structure of MO|RE data consists of a Scienꢁfic Advisory Board and an Editorial  
Board. The Scienꢁfic Advisory Board contributes to the long-term development of MO|RE data and  
comments on its current status. The Editorial Board Team takes an operaꢁonal role in the organizaꢁonal  
structure and is responsible for reviewing datasets uploaded to MO|RE data.  
The Editorial Board Team is composed of experts in the field of sport and sport science as well as in the  
field of research data.  
The management of the metadata, assignment of DOI® names and publicaꢁon of the research data  
aꢃer the successful review process is done automaꢁcally by the MO|RE data soꢃware system.  
8
.2 MO|RE data cooperaꢀon partners  
For the development and securing of the technical infrastructure of MO|RE data, the Insꢁtute of Sport  
and Sports Science cooperates with the KIT Library, the external IT company mb-mediasports and the  
Steinbuch Centre for Compuꢁng at KIT.  
The cooperaꢁon partner Centre for Compuꢁng secures in the long term all data published on MO|RE  
data on its permanent storage and provides the Insꢁtute of Sport and Sport Science with a server for  
the technical development of the plaꢀorm.  
DOI® naming is possible thanks to a connecꢁon to the RADAR4KIT infrastructure operated internally at  
the KIT Library. The KIT Library as a project partner supports the MO|RE data project mainly in the field  
of research data management and Open Science.  
Mb-mediasports is a very experienced IT service provider with whom the IfSS has already been able to  
realize numerous projects (including the data entry plaꢀorms for the European Fitness Badge, German  
Motor Skills Test, and many more).  
8
.3 Digital Object Idenꢀfier (DOI®)  
In order to ensure permanent access to digital, but also to physical and abstract objects, persistent  
idenꢁfiers are assigned to the data or digital objects published on the network, one of which is the  
Digital Object Idenꢁfier (DOI®).  
The DOI® is used to store metadata about the referenced object. The metadata must comply with the  
schema of the respecꢁve registraꢁon agency (e.g. DataCite). Therefore, the metadata schema on  
MO|RE data is based on the recommendaꢁons of DataCite.  
With DOI® names, published data can be cited more easily and securely, since a DOI® name remains  
unchanged if the locaꢁon is changed.  
For each dataset published on MO|RE data, the plaꢀorm assigns a DOI® name. MO|RE data obtains  
DOI® names through its internal infrastructure, RADAR4KIT, which registers DOI® names with DataCite.  
DataCite is an official DOI® registraꢁon agency for research data.  
8
.4 Licensing of MO|RE data  
MO|RE data offers its users a choice of free Creaꢁve Commons (CC) licenses for the data packages  
dataset & metadata). On MO|RE data, data providers can choose from two licenses: CC-BY 4.0  
(
Internaꢁonal (Aꢂribuꢁon) and CC-BY-SA 4.0 Internaꢁonal (Aꢂribuꢁon-ShareAlike). The license  
selecꢁon is based on the concept of MO|RE data as an Open Science and Open Content plaꢀorm. In  
addiꢁon, these two mutually compaꢁble licenses allow execuꢁon of the data aggregaꢁon funcꢁon. The  
rights of the data owner to the data are not affected by the granꢁng of a CC license. The data owner is  
also the licensor and must explicitly agree to the use of any license by selecꢁng MO|RE data (metadata  
field). This means that only the data owner may determine under which CC license the data will be  
published on MO|RE data. By doing so, the data owner indemnifies the MO|RE data plaꢀorm from  
claims of third parꢁes that could be asserted against MO|RE data due to infringements of rights.  
The free licenses used here grant all data providers the same rights ("everyone licenses"). It is not  
possible to grant exclusive rights of use to selected data providers under the free licenses. Free licenses  
are irrevocable as soon as someone has taken possession of a work licensed in this way. It is not possible  
for the author to withdraw or limit a license once it has been granted.  
Only registered users have access to the data sets and the associated metadata at MO|RE data. Upon  
registraꢁon, each user of MO|RE data must agree to the terms of use. In case of suspicion of misuse of  
the license and/or the terms of use, suspected user accounts may be blocked by the plaꢀorm operator.  
8
.5. Data protecꢀon  
The MO|RE data team takes the privacy of users and the data sets available in MO|RE data very  
seriously. In the two documents "Privacy Policy" and "Terms of Use" at the boꢂom leꢃ of the MO|RE  
data home page, all users will find the necessary informaꢁon to assess data protecꢁon.  
9
. Glossary  
Term  
Definition  
Aggregated data  
Anonymization  
Also "macro data" - summary of "micro data" (raw data).  
According to BDSG (Federal Data Protection Act) § 3, para. 6, anonymization means any  
measures that change personal data in such a way that "the individual details about  
personal or factual circumstances can no longer be assigned to a specific or identifiable  
natural person or can only be assigned to such a person with a disproportionate amount of  
time, expense and effort".  
applicant  
Archive  
Applicant for certification with CoreTrustSeal.  
Related to the research data management context, an archive is a collection of data. These  
should be retained in the archive indefinitely. An archiving period for research data of  
usually ten years has emerged. A special form of archiving research data is the so-called  
repository.  
Authenticity  
The authenticity of an object or data is understood to mean the authenticity and credibility  
of the object or data, which can be verified based on a unique identity and characteristic  
properties.  
Best practice  
A method of running a work process that has already been tried and tested. It is "a  
technique or methodology that has been proven through experience and research to be  
reliable in leading to a desired result."  
Bitstream-Preservation  
Bitstream preservation is the ability to preserve the bitstream beyond technology changes.  
Bitstream preservation only guarantees the exact preservation of the underlying bitstream  
and makes no claims about whether the data that the bitstream represents can still be  
meaningfully represented or analyzed in the future.  
Brief Description of  
Repository  
Brief description of a repository; the description should ideally include a diagram and a  
description of the overarching organizational structure.  
CoreTrustSeal Certificate  
The repository has passed an audit/review by an Expert panel as trustworthy and receives a  
certificate. Further information at: https://www.coretrustseal.org/  
Creative Commons  
Licenses  
A license is permission to use copyrighted material. The use of liberal licensing models, in  
particular the globally recognized Creative Commons (CC) licenses, is one way to specify  
conditions for the subsequent use of published research data in a comprehensible way.  
More information at: https://creativecommons.org/licenses/?lang=de ENGLISCHER LINK??  
DataCite  
DataCite promotes data sharing, access to research data, and enhanced protection of  
research investments. As a global consortium, DataCite brings together individual regional  
(
national) members who can provide direct service to the scientist. The cooperation  
promotes worldwide research scientists and creates global access to scientific research  
data.  
File format  
The file format (sometimes called file type, file type or data format) is generated when a file  
is stored and contains information about the structure of the data present in the file, its  
purpose and affiliation.  
Data provider (here  
identical to data owner)  
Data curation  
A registered user of the platform who wishes to upload his/her research data to the  
"MO|RE data" repository and obtain a DOI name for the research data.  
Data curation describes what management activities are required to maintain research data  
(
over the long term) so that it is available for reuse and preservation. Data curation is a  
necessary element for both searching, locating, and retrieving the data, as well as  
maintaining its quality, adding value, and reusing it over time.  
Data user  
A (registered) user of the platform who searches for, downloads, or cites data.  
Data record  
Group of data in a file that belong together in certain respects.  
Privacy policy  
Describes how data (especially personal data) is processed by an organization, i.e. how this  
data is collected, used and whether it is passed on to third parties.  
Datensicherung (Backup)  
Daten-Upload  
Backing up data is most referred to as a backup or a backup copy and is used to restore the  
original data in the event of data loss.  
The process of uploading data and associated metadata to a data repository.  
Datenzugriff (data access) The ability to access and read specific data and information on storage devices such as  
drives or databases.  
Designated Community  
A target audience of potential users who are able to understand and interpret specific  
information (from the data collection).  
DFG  
The German Research Foundation (DFG) is a registered association that functions as a self-  
governing body for the promotion of science and research in the Federal Republic of  
Germany.  
Digital Object  
DMT 6-18  
An object consisting of a bit sequence.  
German Motor Skills Test 6-18; More information at:  
https://www.ifss.kit.edu/dmt/english/index.php  
DOI  
Digital Object Identifier, a persistent identifier (PI). A DOI remains the same throughout the  
lifetime of a designated object.  
Editorial Board  
A group of experts who check the quality and correctness of the data supplied.  
(
empirical) study  
A scientific method which, through the systematic collection, evaluation and interpretation  
of data, gains knowledge and allows statements to be made about reality.  
Use of information technology to support existing and new forms of research.  
eResearch  
Evidence  
FAIR data  
Evidence (documentation)  
FAIR means Findable, Accessible, Interoperable, and Reusable. The main goal of the FAIR  
Data principles is an optimal preparation of research data, which should be findable,  
accessible, interoperable, and reusable.  
Research Data  
Research data are (digital) data generated during scientific activity (e.g. through  
measurements, surveys, tests, source work). They form a basis for scientific work and  
document its results.  
Research Data  
Management  
Research data management is the process of transforming, selecting, and storing research  
data with the aim of keeping it accessible, reusable and verifiable in the long term and  
independently of the data creator.  
Ingest  
The process of entering data and associated metadata into a data repository.  
Insource/Outsource  
Partners  
Cooperation partners internal/external  
Integrity  
A system guarantees data integrity if it is not possible for subjects (e.g. users) to  
manipulate, e.g. change, the data to be protected without authorization and without being  
noticed.  
KOMET  
Competence center of motor tests. Coordination and bundling of all activities related to  
motor tests - see also "Bös et al. (2021). KOMET - Kompetenzzentrum motorische Tests.  
Hintergrund & Testbeschreibungen".  
Continuity of access to  
data (continuity of  
access)  
Sustainability of data storage; long-term archiving/long-term availability of digital  
resources.  
Long-term archiving  
More than just the permanent storage of digital information on a data carrier. Rather, it  
includes the preservation of the permanent availability and thus a subsequent use and  
interpretability of the digital resources.  
Mapping (data mapping)  
Metadata  
Data mapping is the process of transferring data (elements) from one data model to  
another. This is the first step in integrating foreign information into one's own information  
system.  
Intrinsically independent data that contain structured information about other data or  
resources and their characteristics. They are stored independently of or together with the  
data they describe in more detail.  
Metadata standard  
Mission/Scope  
MO|RE data  
A requirement to provide a common understanding of the meaning of the data to ensure  
the correct and proper use and interpretation of the data by its owners and users.  
Each repository has an explicit mission to provide access to stored data for its intended  
audience (designated community) and to archive the data for the long term.  
Motor research data - eResearch infrastructure for sports science motor research data.  
Reuse of data/Data Reuse The reuse of collected data for a specific purpose, to investigate a new problem or to verify  
the conclusions of the data producer.  
NFDI  
The National Research Data Infrastructure is a digital, distributed infrastructure currently  
under construction that will provide the scientific community in Germany with services and  
advice on all aspects of research data management. More information at:  
https://www.nfdi.de/?lang=en  
Standardization/Norm  
data  
Normalization is the development of a conversion scale from raw scores to norm scores for  
the purpose of establishing comparability of an individual test result with a representative  
comparison group.  
Manual  
Terms of use  
Ordered compilation of knowledge, explanations, instructions for use.  
Legal agreements between a service provider and a person who wants to use this service.  
The person must agree to comply with the terms of use in order to use the offered service.  
A free access to scientific literature and other materials on the internet.  
An archive consisting of an organization of people and systems that has taken on the  
responsibility of preserving information and making it available to a target group  
(designated community).  
Open access  
Open Archival  
Information System  
(
OAIS)  
Open data  
Open data is data that anyone can use, redistribute, and reuse for any purpose.  
Open science  
Open science is transparent and accessible knowledge that is shared and (further)  
developed through collaborative networks.  
Persistent identifier  
In research data management, a persistent identifier is a permanent (persistent) digital  
identifier consisting of digits and/or alphanumeric characters that is assigned to a data set  
(
or other digital object) and refers directly to it. A persistent identifier refers to the object  
itself and not to its location on the Internet.  
Personal data  
The German Federal Data Protection Act (BDSG) defines personal data as "individual  
information about personal or factual circumstances of a specific or identifiable natural  
person (data subject)". Data can be considered personal if it can be clearly assigned to a  
specific natural person. Typical examples are the name, occupation, height or nationality of  
the person.  
Repository type  
Repository  
Determines the function of a repository. Repositories are divided into three variants based  
on their subject focus and operator: subject-specific, generic, institutional.  
Storage place to archive digital research data for a longer period of time and in many cases  
also to publish it.  
Raw Data  
Trustworthy Data  
Repositories  
Also "primary data" or "original data" - data that has not yet been processed or evaluated.  
A repository is certified or assessed as trustworthy based on the 16 specified requirements  
(Requirements).  
type of data accepted by  
the repository  
Scope and nature of data collection.  
Originator  
URN (Uniform Resource  
Name)  
According to the Copyright Act (§ 7 UrhG), an author is the creator of the work.  
URN is the name of an identification and addressing system and is used similarly to a DOI  
for persistent identification of digital objects (net publications, datasets, etc.).  
A group of external experts who support the project team in the scientific evaluation of the  
results and act in an advisory capacity.  
Scientific Advisory Board  
Citation  
Currently, there is no uniform standard for the citation of research data. However, research  
data should be given a persistent identifier, such as a DOI, when it is published, and this  
identifier should be used for citation.