Da a Managemen Plan c ea ed using Da a S ewa dship Wiza d.
Co esponds wi h Ho izon Eu ope DMP empla e.
A li e ime wi h language:
he na u e and on ogeny
o linguis ic communica ion
(LangInLi e)
Da a Managemen Plan
VERSION 1.1
02 Oc 2025
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His o y o changes
Ve sion
Publica ion da e
Changes
Ve sion 1.0
02 Jul 2025
-
Ve sion 1.1
02 Oc 2025
De ailed in o ma ion on po en ial da a e-
use, esou ces alloca ion, and da a secu i y
added o he co esponding sec ions.
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Con ibu o s
The ollowing con ibu o s a e ela ed o he p ojec o his DMP:
• doc. Mg . Pa el Caha, Ph.D.
[email p o ec ed], ORCID: 0000-0003-1428-8052
Roles: Da a Collec o , Da a Cu a o , P ojec Leade , Resea che
A ilia ion: Masa yk Uni e si y (MU)
• Mg . e Mg . Ľubomí a No áko á, Ph.D.
[email p o ec ed], ORCID: 0000-0002-8352-439X
Roles: Da a Collec o , Da a Cu a o , Resea che
A ilia ion: Cen al Eu opean Ins i u e o Technology – Masa yk Uni e si y
(CEITEC MU)
• doc. Mg . Radek Šimík, Ph.D.
[email p o ec ed], ORCID: 0000-0002-4736-195X
Roles: Da a Collec o , Da a Cu a o , Resea che
A ilia ion: Cha les Uni e si y
• p o . PhD . Filip Smolík, Ph.D., DSc.
[email p o ec ed], ORCID: 0000-0003-4160-6124
Roles: Da a Collec o , Da a Cu a o , Resea che
A ilia ion: Czech Academy o Sciences (AV ČR)
• Mg . Pa la Ma inko á
[email p o ec ed], ORCID: 0000-0002-1456-4224
Roles: Con ac Pe son, C ea o o DMP, Da a S ewa d
A ilia ion: Masa yk Uni e si y (MU)
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P ojec
We will be wo king on he ollowing p ojec o which he da a and wo k a e desc ibed in
his DMP.
A li e ime wi h language: he na u e and on ogeny o linguis ic communica ion,
CZ.02.01.01/00/23_025/0008726
Ac onym
LangInLi e
S a da e
2025-01-01
End da e
2028-12-31
Funding
• h ps://msm .go .cz: CZ.02.01.01/00/23_025/0008726 (g an ed)
The p ojec ocuses on he language o he indi idual, which is an i eplaceable means o
communica ion wi h he su oundings o e e y pe son. I explo es how language is
acqui ed in childhood, how i unc ions in adul hood and wha challenges indi iduals ace
as hey age. The aim o he p ojec is o conduc in e disciplina y esea ch based on a
combina ion o linguis ics, psychology and neu oscience, which, by p oducing cu ing-edge
esul s, will enable an adequa e and e ec i e esponse o he global language- ela ed
challenges o con empo a y socie y.
The p ojec has h ee main esea ch objec i es, ca ied ou by esea ch eams om
pa icipa ing ins i u es. Resea ch objec i es a e e e ed o as RO1, RO2 and RO3 in his
DMP.
• Resea ch objec i e 1: Language in childhood: acquisi ion and ea ly language
de elopmen (RO1)
• Resea ch objec i e 2: Language in adul hood: na u e, communica ion, language
lea ning and he consequences o mig a ion (RO2)
• Resea ch objec i e 3: Language in old age: loss o language abili ies and how o
slow i down (RO3)
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1. Da a Summa y
Di e en ypes o da a will be c ea ed, acqui ed, and e-used du ing he p ojec , as de ailed
in his sec ion. Da a ela ed o scien i ic publica ions will be made a ailable, in acco dance
wi h Open Science p inciples and ollowing legal equi emen s, as desc ibed in Sec ion 2.
Indi idual da ase s will be speci ied as upda es o his DMP.
The p ojec will gene a e a a ie y o da a su ounding na u e and on ogeny o linguis ic
communica ion. The gene a ed da a will be bo h quan i a i e (e.g., eac ion imes) and
quali a i e (e.g., anno a ed spoken and w i en co po a). All da a will ela e o esea ch
ques ions o each o he esea ch objec i es as ou lined in he p ojec p oposal. The da a
will encompass di e en me hodological app oaches.
The expec ed size o he da a di e s o each o he esea ch objec i es; he expec ed
inc ease in a yea is hund eds o GiB o RO1, unde 10 GiB o RO2, and 550 GiB o RO3.
The da a ob ained will be use ul no only o he p ojec 's esea ch eam, bu also o he
wide communi y o expe s in he ields o linguis ics, psychology, neu oscience, pedagogy,
and clinical p ac ice. The da a can se e as e e ence ma e ial o u he esea ch in o
language de elopmen , bilingualism, he impac o aging on language abili ies, and he
impac o mig a ion on language acquisi ion.
• Equipmen da a
Da a will be collec ed by p ojec membe s, wi h ou own equipmen , which is well
desc ibed and known.
The ollowing ypes o equipmen will be used:
• Eye- acking
• Elec oencephalog am (EEG)
• Magne ic esonance imaging (MRI)
• Func ional nea -in a ed spec oscopy ( NIRS)
Da a will be handled in he ollowing o ma s: BVCDF, CSV, EDF, FNIRS, NIFTI, and o he
o ma s depending on he used equipmen and so wa e.
• Expe imen al s imuli
We will be using audio, ideo and image da a as s imuli in expe imen s.
We will be wo king wi h he ollowing o ma s: DOCX, JPG, MP3, PNG, TXT, WAV, and o he
o ma s depending on he so wa e used.

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Re-used da a
We will be e-using he ollowing da a:
• Openly a ailable linguis ic co po a
(e.g. Czech na ional co pus, Clea pond, English Vocabula y P o ile)
The co po a will be used o s udy linguis ic phenomena ela ed o esea ch objec i es. The
da a can be used in he o ma p o ided wi hou any con e sion needed. We will wo k wi h
hem using online analy ical ools o download hem o local analysis. Po en ial changes in
he da a will no in luence he ep oducibili y o ou esul s. Fo some co po a, only pa s o
hem will be used; any il e ing o selec ion will be documen ed.
• Copy igh ed ex co po a
(e.g. B epolis da abase, p in ed books)
The co po a will be used o s udy linguis ic phenomena ela ed o esea ch objec i es.
Tex s needed o analysis a e a ailable ia subsc ip ion o he da abase o pu chase o he
books (no pa o he p ojec cos s). The e will no be any changes in he da a in luencing
ou esul s. Only pa s o he ex s will be used, and he selec ion will be well documen ed.
• Da ase s p e iously c ea ed by p ojec membe s
Da ase s will be used o s udy linguis ic phenomena ela ed o esea ch objec i es. P ojec
membe s a e he owne s o he da ase s. The da ase s can be used in he o ma p o ided
wi hou any con e sion needed. We al eady ha e a copy o he da a. Those a e ixed
da ase s, changes will no in luence he ep oducibili y o ou esul s. Fo some, only pa s
o he da ase will be used; any il e ing o selec ion will be well documen ed.
1.1 Da a o ma s
In o ma ion abou each o da a o ma s we cu en ly plan o be wo king wi h.
• B ainVision Co e Da a Fo ma (BVCDF)
o An open p op ie a y o ma , complian wi h BIDS (B ain Image Da a S uc u e)
s anda d.
• Comma-sepa a ed Values (CSV)
o An open ile o ma , sui able o long- e m p ese a ion o abula da a.
• O ice Open XML Documen (DOCX)
o A widely used o ma , ollowing he O ice Open XML s anda d.
• Eu opean Da a Fo ma (EDF)
o An open o ma , sui able o exchange and s o age o mul ichannel biological
and physical signals.
• JPEG, JPG
o A o ma sui able o long- e m p ese a ion o as e images.
• Ja aSc ip Objec No a ion (JSON)
o An open ile and da a in e change o ma .
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• Ma kdown (MD)
o An open ile o ma .
• MP3
o An open audio ile o ma .
• Neu oimaging In o ma ics Technology Ini ia i e (NI TI)
o An open ile o ma , used o s o e b ain imaging da a ob ained using MRI
me hods.
• Po able Ne wo k G aphics (PNG)
o A o ma sui able o long- e m p ese a ion o as e images.
• R (R)
o A o ma o p og amming iles used in open-sou ce so wa e o s a is ical
compu ing and g aphics, R.
• R Ma kdown (RMD)
o Fo ma o iles used in open-sou ce so wa e o s a is ical compu ing and
g aphics, R, using Ma kdown ma kup language.
• Sha e Nea In aRed File (SNIRF)
o An open ile o ma , wi h speci ica ions complian wi h BIDS (B ain Image Da a
S uc u e) s anda d.
• Tex File (TXT)
o A o ma sui able o long- e m p ese a ion o ex ual da a.
• Wa e o m Audio Fo ma (WAV)
o A p op ie a y audio ile o ma .
• O ice Open XML Wo kbook (XLSX)
o A widely used o ma , ollowing he O ice Open XML s anda d.
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2. FAIR Da a
2.1. Making da a indable, including p o isions o me ada a
We will sha e da a ha can become open in publicly accessible us ed eposi o ies using
desc ip i e me ada a as equi ed by he eposi o y. Keywo ds will be added o be e
indabili y.
Me ada a will be a ailable in a o m ha can be ha es ed and indexed. All published da a
will be assigned a pe sis en iden i ie which will be included in hei me ada a. This will be
managed by he eposi o ies.
The ollowing me ada a s anda ds a e ele an o a ious da a we will wo k wi h du ing
he p ojec :
• B ain Imaging Da a S uc u e (BIDS)
• Componen Me ada a Speci ica ion (CMDI)
• In es iga ion Desc ip ion Fo ma (IDF)
• Linguis ic Anno a ion Fo ma (LAF)
• Minimum In o ma ion abou an MRI S udy (MI MRI)
• Open Language A chi es Communi y Me ada a (OLAC Me ada a)
2.2. Making da a accessible
We will be wo king wi h philosophy as open as possible o ou da a. Da a will be deposi ed
by he publica ion da e o he ela ed scien i ic publica ion a he la es ; emba go pe iod is
cu en ly no planned o be used o any o he da ase s.
The ollowing eposi o ies a e conside ed o sha ing he da a:
• Zenodo
o A us ed gene al open eposi o y, ope a ed by CERN.
• Figsha e
o A us ed gene al eposi o y, ope a ed by Digi al Science company.
• Da a e se
o A us ed gene al eposi o y, ope a ed by Ha a d.
O he us ed gene al o disciplina y eposi o ies will be speci ied as upda es o his DMP.
Ou da a canno become comple ely open. We will collec da a connec ed o a pe son, i.e.
pe sonal da a, as de ailed in Sec ion 6. We can use pseudonymiza ion, anonymiza ion and
da a agg ega ion o make he da a mo e openly a ailable.
The e a e IP easons why ou da a canno be open, which applies o e-used copy igh ed
ex co po a.
Fo da a wi h limi ed access, he e will be clea ins uc ions on how o ge access in he
me ada a.
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Me ada a will be made openly a ailable unde a public domain dedica ion CC0. This will be
managed by he eposi o ies.
2.3. Making da a in e ope able
We will be sha ing da a in he ollowing o ma s:
• CSV, JSON, TXT
Mo e o ma s will be decided upon close o he publica ion da e and will be speci ied as
upda es o his DMP. Fo sha ing da a, we will use open o ma s as opposed o p op ie a y
ones whene e possible.
2.4. Inc ease da a e-use
Rich me ada a and documen a ion will be p o ided o each da ase ; Readme ile will be
included. Files and olde s will be e sioned and s uc u ed using a ile naming con en ion.
All he me ada a in he ile names will also be a ailable in he p ope me ada a.
Fo expe imen s, we will use pape and elec onic lab no ebooks o make su e ha he e is
good p o enance o he da a analysis. The ollowing open-sou ce esea ch so wa e will be
used o expe imen s:
• PCIbex, an open-sou ce so wa e o in e ne -based expe imen s
• L-Rex, an open-sou ce so wa e o linguis ic expe imen s
• PsychoPY, an open-sou ce beha io al esea ch so wa e
I is clea who owns da a and documen s c ea ed du ing he p ojec and can license hem
o e-use.
We will be employing he ollowing quali y p ocesses o ins umen da a:
• Calib a ing measu emen s
• Repea measu emen s
• S anda dized da a cap u e and eco ding
• Da a En y alida ion
To alida e he in eg i y o he esul s, he ollowing will be done:
• We will un a subse o ou jobs se e al imes ac oss he di e en compu ing
in as uc u es.
• We will be ins umen ing he ools in o pipelines and wo k lows using au oma ed
ools.
• We will use independen ly de eloped duplica e ools o wo k lows o c i ical s eps
o educe o elimina e human e o s.
• We will un pa o he da a se epea edly o ca ch unexpec ed changes in esul s.