LimeSoDa: A da ase collec ion o benchma king o machine lea ning
eg esso s in digi al soil mapping
Jonas Schmidinge
a,b,*
, Sebas ian Vogel
b
, Viachesla Ba ko
a,b
, Anh-Duy Pham
a,b
,
Robin Gebbe s
b
, Hamed Ta akoli
b
, Jose Co ea
b
, Tiago R. Ta a es
c
, Pa ick Filippi
d
,
Edwa d J. Jones
d
, Voj ech Lukas
e
, E ic Boenecke
, Joe g Ruehlmann
, Ingma Sch oe e
g
,
Ecka K ame
g
, S e an Pae zold
h
, Masakazu Kodai a
i
, Alexand e M.J.-C. Wadoux
j
,
Luca B agazza
k
, Kon ad Me zge
k
, Jingyi Huang
l
, Domingos S.M. Valen e
m
,
Jose L. Sa anelli
n
, Edua do L. Bo ega
o
, Rica do S.D. Dalmolin
p
, Csilla Fa kas
q
,
Alexande S eige
, Tacia a Z. Ho s
s
, Leona do Rami ez-Lopez
,u
, Thomas Schol en
,w
,
Felix S ump
x
, Pablo Rosso
y
, Ma celo M. Cos a
z
, Rod igo S. Zandonadi
aa
,
Johanna We e lind
ab
, Ma in A zmuelle
a,ac
a
Osnab ück Uni e si y, Join Lab A i icial In elligence and Da a Science, Osnab ück, Ge many
b
Leibniz Ins i u e o Ag icul u al Enginee ing and Bioeconomy (ATB), Depa men o Ag omecha onics, Po sdam, Ge many
c
Uni e si y o S˜
ao Paulo (USP), Cen e o Nuclea Ene gy in Ag icul u e (CENA), Pi acicaba, B azil
d
The Uni e si y o Sydney, Sydney Ins i u e o Ag icul u e, Sydney, Aus alia
e
Mendel Uni e si y in B no, Depa men o Ag osys ems and Bioclima ology, B no, Czech Republic
Leibniz Ins i u e o Vege able and O namen al C ops, Nex Gene a ion Ho icul u al Sys ems, G ossbee en, Ge many
g
Ebe swalde Uni e si y o Sus ainable De elopmen , Landscape Managemen and Na u e Conse a ion, Ebe swalde, Ge many
h
Uni e si y o Bonn, Ins i u e o C op Science and Resou ce Conse a ion (INRES)—Soil Science and Soil Ecology, Bonn, Ge many
i
Tokyo Uni e si y o Ag icul u e and Technology, Ins i u e o Ag icul u e, Tokyo, Japan
j
LISAH, Uni . Mon pellie , Ag oPa isTech, INRAE, IRD, L’Ins i u Ag o, Mon pellie , F ance
k
Ag oscope, Field-C op Sys ems and Plan Nu i ion, Nyon, Swi ze land
l
Uni e si y o Wisconsin-Madison, Depa men o Soil Science, Madison, USA
m
Fede al Uni e si y o Viçosa, Depa men o Ag icul u al Enginee ing, Viçosa, B azil
n
Woodwell Clima e Resea ch Cen e , Falmou h, USA
o
Fede al Uni e si y o San a Ma ia (UFSM), Academic Coo dina ion, San a Ma ia, B azil
p
Fede al Uni e si y o San a Ma ia (UFSM), Soil Depa men , San a Ma ia, B azil
q
No wegian Ins i u e o Bioeconomy Resea ch (NIBIO), Di ision o En i onmen and Na u al Resou ces, Aas, No way
Uni e si y o Ros ock, Chai o Geodesy and Geoin o ma ics, Ros ock, Ge many
s
Fede al Technological Uni e si y o Pa an´
a, Dois Vizinhos, B azil
BÜCHI Labo echnik AG, Da a Science Depa men , Flawil, Swi ze land
u
Impe ial College London, Impe ial College Business School, London, UK
Uni e si y o Tübingen, Depa men o Geosciences, Tübingen, Ge many
w
Uni e si y o Tübingen, DFG Clus e o Excellence ‘Machine Lea ning o Science’, Ge many
x
Be n Uni e si y o Applied Sciences, Compe ence Cen e o Soils, Zolliko en, Swi ze land
y
Leibniz Cen e o Ag icul u al Landscape Resea ch (ZALF), Simula ion and Da a Science, Münchebe g, Ge many
z
Fede al Uni e si y o Ja aí, Ins i u e o Ag icul u al Sciences, Ja ai, B azil
aa
Fede al Uni e si y o Ma o G osso, Ins u e o Ag icul u al and En i onmen al Scinces, Sinop, B azil
ab
Swedish Uni e si y o Ag icul u al Sciences (SLU), Depa men o Soil and En i onmen , Ska a, Sweden
ac
Ge man Resea ch Cen e o A i icial In elligence (DFKI), Resea ch Depa men Plan-Based Robo Con ol, Osnab ück, Ge many
ARTICLE INFO
Handling Edi o : B andon Heung
ABSTRACT
Digi al soil mapping (DSM) elies on a b oad pool o s a is ical me hods, ye de e mining he op imal me hod o
a gi en con ex emains challenging and con en ious. Benchma king s udies on mul iple da ase s a e needed o
e eal s eng hs and limi a ions o commonly used me hods. Exis ing DSM s udies usually ely on a single da ase
* Co esponding au ho .
E-mail add ess: [email p o ec ed] (J. Schmidinge ).
Con en s lis s a ailable a ScienceDi ec
Geode ma
jou nal homepage: www.else ie .com/loca e/geode ma
h ps://doi.o g/10.1016/j.geode ma.2025.117337
Recei ed 4 Ma ch 2025; Recei ed in e ised o m 30 Ap il 2025; Accep ed 6 May 2025
Geode ma 459 (2025) 117337
A ailable online 20 May 2025
0016-7061/© 2025 The Au ho (s). Published by Else ie B.V. This is an open access a icle unde he CC BY license ( h p://c ea i ecommons.o g/licenses/by/4.0/ ).
Keywo ds:
Machine lea ning
Benchma king
Open da a
Da ase collec ion
Digi al soil mapping
Pedome ics
wi h es ic ed access, leading o incomple e and po en ially misleading conclusions. To add ess hese issues, we
in oduce an open-access da ase collec ion called P ecision Liming Soil Da ase s (LimeSoDa). LimeSoDa consis s
o 31 ield- and a m-scale da ase s om a ious coun ies. Each da ase has h ee a ge soil p ope ies: (1) soil
o ganic ma e o soil o ganic ca bon, (2) clay con en and (3) pH, alongside a se o ea u es. Fea u es a e
da ase -speci ic and we e ob ained by op ical spec oscopy, p oximal- and emo e soil sensing. All da ase s we e
aligned o a abula o ma and a e eady- o-use o modeling. We demons a ed he use o LimeSoDa o
benchma king by compa ing he p edic i e pe o mance o ou lea ning algo i hms ac oss all da ase s. This
compa ison included mul iple linea eg ession (MLR), suppo ec o eg ession (SVR), ca ego ical boos ing
(Ca Boos ) and andom o es (RF). The esul s showed ha al hough no single algo i hm was uni e sally su-
pe io , ce ain algo i hms pe o med be e in speci ic con ex s. MLR and SVR pe o med be e on high-
dimensional spec al da ase s, likely due o be e compa ibili y wi h p incipal componen s. In con as , Ca -
Boos and RF exhibi ed conside ably be e pe o mances when applied o da ase s wi h a mode a e numbe
(<20) o ea u es. These benchma king esul s illus a e ha he pe o mance o s a is ical me hods can be highly
con ex -dependen . LimeSoDa he e o e p o ides an impo an esou ce o imp o ing he de elopmen and
e alua ion o s a is ical me hods in DSM.
1. In oduc ion
In digi al soil mapping (DSM), s a is ical modeling o he ela ion-
ships be ween labo a o y measu ed soil p ope ies and seconda y ea-
u es is used o c ea e soil maps o en i onmen al and ag icul u al
applica ions. A key goal o DSM esea ch is o con inuously imp o e he
accu acy o soil maps (Minasny and McB a ney, 2016). Gi en he s a-
is ical and compu a ional na u e o he ask, mos o he ecen esea ch
has ocused on imp o ing accu acy by implemen ing mo e sophis ica ed
modeling app oaches—especially, since he adop ion o machine
lea ning (ML) and deep lea ning algo i hms has inc eased p edic i e
capabili ies (Heu elink and Webs e , 2022). Howe e , modeling pipe-
lines in DSM a e usually no s aigh o wa d because se e al s a is ical
challenges ha e o be add essed such as high-dimensional, noisy and
in e co ela ed ea u es (Shi e al., 2023); he inco po a ion o spa ial
and some imes empo al componen s (Heu elink and Webs e , 2022); o
e y sca ce aining da a (Schmidinge e al., 2024b). Va ious s a is ical
me hods ha e been sugges ed and de eloped in esponse. These ange
om di e en ML algo i hms (Wadoux e al., 2020), p e-p ocessing
echniques (Shi e al., 2023) and da a usion app oaches (Wang e al.,
2022) o he de elopmen o new sampling designs (B us, 2019). While
each o he sugges ed me hods may se e a speci ic pu pose, i is di icul
o keep ack o he b oad and e e -e ol ing pool o a ailable ools. In
addi ion, di e en s udies o en show con adic o y conclusions abou
he e ec i eness o commonly used me hods, as discussed by ˇ
Zíˇ
zala
e al. (2024) o sampling designs, Shi e al. (2023) o p e-p ocessing
echniques, o Heung e al. (2016) o lea ning algo i hms. As a esul ,
i o en emains unclea how obus each o hese me hods is and o
which con ex hey a e mos sui able. T us wo hy and in o ma i e
benchma king s udies a e needed o ully unde s and hei capabili ies
and s eng hs.
Benchma king e e s o he me hodological compa ison o
compe ing s a is ical me hods o assess and ank hei capabili ies (Nießl
e al., 2022). Essen ially, in a benchma king s udy, he pe o mance o
wo o mo e me hods (e.g., lea ning algo i hms) a e compa ed based on
e alua ion me ics (e.g., R
2
). Such s udies a e al eady common in DSM,
as shown by ou suppo ing li e a u e e iew on benchma king s udies
in 2023, p o ided in Appendix A. Howe e , he in o ma i e alue o
hem is o en limi ed because he unde lying s udy designs do no sup-
po comp ehensi e and gene alizable conclusions. In he ollowing, we
will e e o he main indings om he li e a u e e iew on DSM
benchma king (Appendix A) o highligh cu en ly p esen sho comings
acco ding o se e al guidelines on benchma king (e.g., Boules eix e al.,
2017; Nießl e al., 2022; Webe e al., 2019).
We obse ed ha o e 95 % o DSM s udies elied on a single da ase
o hei benchma king (see Appendix A, Fig. A2). These single da ase s
we e ei he s udy-speci ic p op ie a y da ase s o one o he ew a ail-
able open-access op ions, such as he Land Use and Co e age A ea F ame
Su ey (LUCAS) (O giazzi e al., 2018). Howe e , gi en ha he
pe o mance o me hods s ongly depend on he con ex and inhe en
pa e ns wi hin da ase s, benchma king should include a subs an ially
la ge numbe o da ase s (Boules eix e al., 2015; S obl and Leisch,
2024). The la ges numbe o da ase s used o DSM benchma king in
2023 was h ee (Appendix A, Fig. A2). In con as , classical ML s udies
may ea u e ens o o e a hund ed da ase s o hei benchma king
(Shmuel e al., 2024). Including a la ge numbe o da ase s ensu es a
mo e obus analysis, as esul s will be less in luenced by indi idual
da ase -speci ic pa e ns. Mo eo e , i allows he e alua ion o me hods
unde a ying condi ions, which helps o unco e hei con ex -speci ic
s eng hs and limi a ions (G insz ajn e al., 2022; Shmuel e al., 2024).
Ano he c i ical aspec o obus benchma king is he open accessi-
bili y o bo h code and da a. Fewe han 10 % o DSM benchma king
s udies p o ided open da ase s and e en less han 5 % sha ed hei code
(see Appendix A, Fig. A1), which is ma ginal compa ed o o he
compu a ional scien i ic ields (Lau ina ichyu e e al., 2022; Pineau
e al., 2021). While many s udies (48 %) included a s a emen ha
indica ed a willingness ‘ o sha e da a upon eques ’, p io s udies on
ep oducibili y ha e demons a ed ha only a mino i y o esea che s
e en ually comply wi h his commi men (K a z and S asse , 2014;
Lau ina ichyu e e al., 2022). The e o e, mos DSM benchma k s udies
mus be deemed i ep oducible. Rep oducibili y in he con ex o
modeling is c ucial no only o iden i ying po en ial e o s h ough code
e iew (G eene e al., 2017) bu also because i allows esea che s o
build upon exis ing wo k o cumula i e scien i ic p og ess (Kapoo and
Na ayanan, 2022). Ye , ep oducibili y becomes e en mo e c i ical in
he con ex o benchma king. Benchma king is o en conduc ed along-
side he in oduc ion o new me hods o p o e hei me i s. I has been
a gued ha his can make he benchma king inhe en ly biased owa ds
he newly p oposed me hod (Nießl e al., 2022; Webe e al., 2019). This
is because au ho s can ha e a compe i i e d i en in e es in publishing
esul s ha show hei no el me hod as supe io o common compe i o s
(B own e al., 2017). Such supe io i y may be achie ed h ough un ai
s udy design choices, al hough o en unin en ionally (Nießl e al., 2022).
In con as , ep oducible esea ch wi h sha ed code and sha ed da a
encou ages mo e igo ous and less biased s udy designs.
We a ibu e he wo a o emen ioned sho comings (i.e., he eliance
on oo ew da ase s in benchma king and he lack o ep oducibili y), in
pa icula o he lack o open da ase s o DSM pu poses. This issue has
been add essed in o he academic ields wi h he es ablishmen o
benchma k da ase collec ions (e.g., Mo is e al., 2020; Romano e al.,
2022). Howe e , a compa able collec ion does no ye exis o DSM.
While p og ess has been made o imp o e he da a-a ailabili y as indi-
ca ed by he open soil spec oscopy lib a y (OSSL) by Sa anelli e al.
(2025) and o he soil-da abases (Gobezie e al., 2024), he ew exis ing
open soil da ase s a e p ima ily ocused on la ge-scale labo a o y-based
soil spec oscopy o include only he a ge soil p ope ies wi hou
accompanying p edic o ea u es. Fu he mo e, some open da ase s (e.
g., LUCAS) we e published unde es ic i e licenses, which p ohibi s
J. Schmidinge e al.
Geode ma 459 (2025) 117337
2
sha ing o he da ase wi hin a code eposi o y. On he o he hand, open
da ase s om a ield- and a m-scale o high- esolu ion p ecision ag i-
cul u e a e almos comple ely missing, despi e he ac ha p ecision
ag icul u e wi h p oximal soil senso s is an impo an applica ion o
DSM (Gebbe s and Adamchuk, 2010). Field-scale DSM wi h p oximal
soil senso s has i s own dis inc i e challenges such as aining da a
sca ci y (Schmidinge e al., 2024b) nex o high-dimensional da a
usion (Schmidinge e al., 2024a; Wang e al., 2022). To add ess hese
issues, we in oduce P ecision Liming Soil Da ase s (LimeSoDa). LimeSoDa
is an open da ase collec ion wi h small-scale da ase s, ha a e eady- o-
use o modeling. This expands he possibili y o DSM p ac i ione s o
benchma k s a is ical me hods ac oss a a ie y o soil mapping con ex s.
In Sec ion 2, we p o ide an o e iew o he da ase s included in
LimeSoDa. Addi ionally, we p esen an example benchma k s udy in
which we compa ed ou lea ning algo i hms (Sec ion 3), o demon-
s a e how LimeSoDa can lead o mo e comp ehensi e and nuanced
conclusions. Las ly, Sec ion 4 o e s an ou look on he po en ial appli-
ca ions o LimeSoDa beyond he p esen s udy.
2. LimeSoDa
2.1. O e iew
LimeSoDa con ains 31 da ase s a ield- o a m-scale. Da ase s a e
eady- o-use o modeling, which means ha hey con ain con inuous
a ge soil p ope ies and ea u es in a abula o ma , usable o
eg ession asks. Th ee a ge soil p ope ies a e included in all da ase s:
soil o ganic ma e (SOM) o soil o ganic ca bon (SOC), pH and clay.
Since SOC and SOM a e di ec ly ela ed, we ea ed hem in e change-
ably as a single soil p ope y. These h ee soil p ope ies a e among he
mos commonly e alua ed soil p ope ies in DSM (Chen e al., 2022) and
a e c ucial pa ame e s o assessing soil quali y. We e e o his
benchma king collec ion as “LimeSoDa” because hese h ee a ge soil
p ope ies a e no exclusi ely bu especially ele an o lime equi e-
men calcula ions, acco ding o bes managemen p ac ices in he UK
and Ge many (Ag icul u e Ho icul u e De elopmen Boa d, 2023;
B¨
onecke e al., 2021). Fea u es o modeling a e da ase -speci ic and
o igina e om labo a o y-based spec oscopy, in-si u p oximal soil
sensing, and emo e sensing. Da ase s a e eleased unde a pe missi e
open-access license, allowing implemen a ion in a code- eposi o y o
inc ease he possibili y o code sha ing. Addi ionally, p e-de e mined
olds o c oss alida ion (CV) a e p o ided o each da ase , o enable
compa abili y wi h ou benchma king and u u e benchma king esul s.
In o al, he combined da ase s include 3,174 soil samples bu sample
sizes o indi idual da ase s ange om 30 o 460 samples.
P e-p ocessing is no manda o y when wo king wi h LimeSoDa
da ase s. Howe e , o da ase s wi h high-dimensional spec al da a,
ea u e dimensionali y educ ion is s ongly encou aged. Ne e heless,
u he p e-p ocessing may imp o e modeling pe o mances, an aspec
ha should i sel be benchma ked using his da ase collec ion.
Table 1
Lis o da ase s included in LimeSoDa.
Da ase ID Loca ion S udy A ea
(ha)
Numbe o
Samples
Numbe o
Fea u es
Senso Da a* P e ious Usage**
SSP.460 S a e o Sao Paulo, B azil 473 460 830 is-NIR Rami ez-Lopez e al. (2019)
BB.250 B andenbu g, Ge many 52 250 17 DEM, ERa, Gamma, pH-ISE,
RSS, VI
Schmidinge e al. (2024b)
SP.231 Sai ama P e ec u e, Japan 3.1 231 272 is-NIR Kodai a and Shibusawa (2020)
B.204 Bahia, B azil 204 204 16 DEM, RSS, VI Pe ei a e al. (2022)
G.150 Goias, B azil 79 150 17 DEM, ERa, RSS, VI Valen e e al. (2024)
H.138 Hubei, China 420 138 2,489 MIR Wadoux e al. (2025)
SL.125 Skåne L¨
an, Sweden 78 125 2,082 ERa, is-NIR We e lind e al. (2010)
UL.120 Uppsala L¨
an, Sweden 97 120 2,082 ERa, is-NIR We e lind e al. (2010)
NRW.115 No h Rhine-Wes phalia, Ge many 17 115 1,686 MIR Leenen e al. (2022)
MG.112 Ma o G osso, B azil 111 112 17 DEM, ERa, RSS, VI Valen e e al. (2024)
SA.112 Saxony-Anhal , Ge many 27 112 1,412 DEM, ERa, Gamma, NIR, pH-
ISE, VI
−
G.104 Goias, B azil 95 104 16 DEM, RSS, VI −
MGS.101 Ma o G osso do Sul, B azil 95 101 16 DEM, RSS, VI −
CV.98 Can on o Vaud, Swi ze land 28 98 2,151 is-NIR Me zge e al. (2024)
SC.93 San a Ca a iMG-na, B azil 108 93 2,146 is-NIR Ho s e al. (2018)
BB.72 B andenbu g, Ge many 3.4 72 17 DEM, ERa, Gamma, pH-ISE,
RSS, VI
−
NRW.62 No h Rhine-Wes phalia, Ge many 0.6 62 1,686 MIR Leenen e al. (2019)
RP.62 Rhineland-Pala ina e, Ge many 3.3 62 1,410 ERa, Gamma, NIR, pH-ISE, VI Ta akoli e al. (2022)
SSP.58 S a e o Sao Paulo, B azil 0.7 58 351 is-NIR Ta a es e al. (2020)
NSW.52 New Sou h Wales, Aus alia 1,158 52 5 DEM, RSS Filippi e al. (2019)Jones e al.
(2021)
BB.51 B andenbu g, Ge many 40 51 4 DEM, ERa, pH-ISE −
SC.50 San a Ca a ina, B azil 13 50 3 DEM, ERa Bo ega e al. (2022)
W.50 Wisconsin, USA 80 50 15 DEM, ERa, VI, XRF Cha e jee e al. (2021)
PC.45 Pes Coun y, Hunga y 4.5 45 4 CSMois u e, ERa Ris olainen e al. (2006)
MG.44 Ma o G osso, B azil 13 44 351 is-NIR Ta a es e al. (2020)
NRW.42 No h Rhine-Wes phalia, Ge many 1.5 42 1,686 MIR Leenen e al. (2019)
SM.40 Sou h Mo a ia, Czechia 53 40 3 DEM, ERa Lukas e al. (2009)
MWP.36 Mecklenbu g-Wes e n Pome ania,
Ge many
18 36 5 DEM, RSS S eige e al. (2025)
O.32 Occi ania, F ance 1.5 32 1,637 MIR Weh le e al. (2022)
BB.30_1 B andenbu g, Ge many 19 30 8 DEM, ERa, pH-ISE, VI −
BB.30_2 B andenbu g, Ge many 1.4 30 13 DEM, ERa, Gamma, RSS, VI −
*Abb e ia ions: Capaci i e soil mois u e (CSMois u e), Digi al ele a ion model and e ain pa ame e s (DEM); Appa en elec ical esis i i y (ERa); Gamma- ay ac-
i i y (Gamma); Mid in a ed spec oscopy (MIR); Nea in a ed spec oscopy (NIR); Ion selec i e elec odes o pH de e mina ion (pH-ISE); Remo e sensing de i ed
spec al da a (RSS), X- ay luo escence de i ed elemen al concen a ions (XRF), Vege a ion indices (VI), Visible- and nea in a ed spec oscopy ( is-NIR).
** The da ase s used in he e e enced s udies does no always comple ely align wi h he da ase s o LimeSoDa, because only a subse o addi ional da a may ha e been
used.
J. Schmidinge e al.
Geode ma 459 (2025) 117337
3
All da ase s we e collec ed in he con ex o p e ious s udies o
esea ch p ojec s. The as majo i y o hese da ase s had no been
publicly a ailable p io o his e o . Hence, LimeSoDa is buil upon
olun a ily submissions om esea che s and esea ch ins i u es. An
o e iew o each da ase is p o ided in Table 1. Da ase s we e included
based on he ollowing ou c i e ia:
P ecision liming con ex : A da ase had o con ain SOM/SOC, pH,
and clay as opsoil (<30 cm) a ge p ope ies a ield- and a m-scale
(<2,000 ha).
We chemis y: Ta ge soil p ope ies had o be de e mined h ough
we chemis y echniques ins ead o being in e ed om spec al models.
P edic i e pe o mance: Ta ge soil p ope ies had o be p edic -
able wi h he gi en da ase -speci ic se o ea u es. We excluded da ase s
whe e no lea ning algo i hm ou pe o med he null model (i.e., wi h an
R
2
<0) o any o he h ee a ge soil p ope ies gi en he modeling
pipeline in Sec ion 3.1.
Sample size: A da ase had o con ain a minimum size o a leas 30
soil samples o all h ee soil p ope ies a he same dis inc sampling
loca ions.
Da ase s we e no excluded because o hei sampling design. Fo
example, some da ase s we e based on a a ge ed sampling design (see
Appendix B, Table B1), which e e s o sampling ocused on speci ic
a eas o ea u es o in e es a he han andom o sys ema ic sampling.
The alida ion o soil maps gene a ed om a ge ed- o spa ially clus-
e ed sampling wi hou an addi ional independen es ing sample se is
con o e sial because he absolu e pe o mance may no be eliably
es ima ed h ough CV o da a spli ing (Piikki e al., 2021). Howe e , we
a gue ha o he pu pose o benchma king, he ela i e pe o mance
di e ences emain in o ma i e. None heless, use s o LimeSoDa can
decide i hey p e e o he alida ion s a egies ha ake in o accoun
he spa ial dependency o pa e n o a da ase .
Spa ial coo dina es a e a ailable o mo e han hal o he da ase s
(see Appendix B, Fig. B1). In o he cases, coo dina es had o be excluded
o anonymized mos ly because o p i acy conce ns.
2.2. P ocessing o da ase s
Only a minimal amoun o p ocessing was conduc ed o da ase s o
LimeSoDa o ensu e ha he da a was as close o he o iginal o m as
possible while s ill enabling e ec i e benchma king. This gi es use s o
LimeSoDa he lexibili y o apply hei own p e-p ocessing on he
da ase s. O dina y k iging was employed o da ase s collec ed wi h on-
he-go p oximal soil senso s o ma ch sensing loca ions wi h soil sam-
pling loca ions. Fo ea u es a ailable in as e o ma (DEM, RSS and
VI), ea u e alues we e ex ac ed a he soil sampling loca ions. Some
senso s had an unde lying da a-p ocessing s ep wi hin hei in e nal
so wa e. This led o some op ical spec oscopy da a being esampled o
di e en wa ebands and spec al esolu ions compa ed o he measu ed
aw da a. Fo da ase s wi h limi ed aining samples (<60 samples), we
included ewe emo e sensing-based ea u es o main ain a a o able
sample- o- ea u e a io. P ocessing s eps a e documen ed in mo e de ail
in he me ada a o he da ase s. Samples wi h missing alues in ei he
he ea u e o a ge soil p ope y ma ix we e always disca ded. Two
spec oscopy da ase s (H.138 and NRW.115) had a ew samples wi h
e lec ance alues abo e 100 % o ce ain bands (see Appendix B,
Fig. B2). Whe he hese anomalies a e due o noise o ins umen al
calib a ion is unclea . Ne e heless, we did no disca d hese samples o
bands om he da ase .
I is impo an o no e ha da ase s ac oss LimeSoDa we e delibe -
a ely no ha monized. While his educes he possibili y o c oss-da ase
lea ning, i inc eased he lexibili y o including da ase s om a ious
con ex s and domains wi hou elying on many assump ions necessa y
o da a ha moniza ion. The aim o LimeSoDa is no o build a uni ied
da abase bu o p o ide mul iple smalle da ase s ha a e independen
en ies o a benchma king s udy. As a consequence, a ge soil p op-
e ies a e some imes exp essed in di e en uni s depending on he
measu emen me hod (see Appendix B, Fig. B2). This dis inguishes
LimeSoDa om spec al lib a ies, ha ely on ha moniza ion o spec al
ea u es and soil p ope ies (Sa anelli e al., 2025). None heless, o
cohe ence wi hin LimeSoDa, we ans o med he appa en elec ical
conduc i i y o ERa and spec al measu emen s a e exp essed as
e lec ance.
2.3. Accessibili y
LimeSoDa is closely aligned o he FAIR-p inciples (Wilkinson e al.,
2016). I is eely accessible h ough Zenodo (h ps://doi.o g/10.5
281/zenodo.14932573), is licensed unde CC BY-SA 4.0 and con ains
ex ensi e documen a ion in he o m o da ase -speci ic me ada a.
Addi ionally, an R- and Py hon da ase package, called likewise Lime-
SoDa downloadable om Gi Hub, was c ea ed. I can be accessed
h ough gi hub.com/JonasSchmidinge /LimeSoDa o R and h ps://
gi hub.com/a11 o1n3/LimeSoDa o Py hon.
3. Demons a i e benchma k s udy
3.1. Me hodology
A benchma king s udy was conduc ed o demons a e he use o
LimeSoDa. Fou lea ning algo i hms we e compa ed based on hei
p edic i e pe o mance. These included andom o es (RF) (B eiman,
2001) and suppo ec o eg ession (SVR) due o hei widesp ead use
in DSM (Khaledian and Mille , 2020), ca ego ical boos ing (Ca Boos )
(P okho enko a e al., 2018) because o i s s ong pe o mances in
ecen ML benchma king s udies on abula da ase s (McEl esh e al.,
2023; Shmuel e al., 2024) and mul iple linea eg ession (MLR) o
include a simple baseline model. We ained and e alua ed each algo-
i hm independen ly on SOC/SOM, pH and clay o each o he 31
da ase s o LimeSoDa. Consequen ly, a o al o 93 p edic ion asks (31
da ase s ×3 a ge soil p ope ies) we e u ilized in he benchma king.
Ac oss hese 93 p edic ion asks, algo i hms we e compa ed on he R
2
and he o dinally anked oo mean squa e e o (RMSE) (see Appendix
C.1). Addi ionally, he Wilcoxon Signed-Rank Tes was used o de e -
mine whe he he mean o dinal anks based on he RMSE signi ican ly
di e ed (
α
=0.1) among he lea ning algo i hms.
The lea ning algo i hms we e e alua ed and hype pa ame e s uned,
using a nes ed K- old c oss alida ion (CV), wi h he ou e loop (K =10)
used o model es ing and he inne loop (K’ =5) o hype pa ame e
selec ion. The op imal hype pa ame e combina ions o SVR, Ca Boos
and RF we e selec ed h ough a andom sea ch wi h 400 i e a ions based
on he lowes agg ega ed RMSE o he inne loop. The hype pa ame e
sea ch-space is gi en in Table 2.
Fo da ase s wi h high-dimensional op ical spec oscopy da a ( is-
NIR, NIR o MIR), modeling wi h he aw da a is s ongly limi ed due o
he un a o able sample o ea u e a io. Hence, we duplica ed he
hype pa ame e sea ch space o 800 i e a ions and added wo unsu-
pe ised bu compu a ionally e icien me hods o dimensionali y
educ ion as addi ional hype pa ame e b anch: p incipal componen
analysis (PCA) and a co ela ion ma ix il e (CMF) (Pe ez-Ri e ol e al.,
2017). CMF and PCA we e applied only o he is-NIR, NIR, o MIR
ea u es a e he sepa a ion o aining and es ing o alida ion olds.
Wi h CMF, spec al ea u es we e disca ded when he absolu e pai wise
Pea son co ela ion coe icien exceeded a de ined cu o , ollowing he
indCo ela ion algo i hm o he R-package ca e (Kuhn, 2008). The
cu o o CMF anged om 0.7 o 1, whe e CMF =1 e e s o no
dimensionali y educ ion, and he numbe o sea ched p incipal com-
ponen s o PCA om 5 o 20. Algo i hm 1 p esen s he modeling
pipeline as pseudocode. The ac ual R-code is a ailable on gi hub.com
/JonasSchmidinge /LimeSoDa_benchma king.
J. Schmidinge e al.
Geode ma 459 (2025) 117337
4
3.2. Resul s & Discussion
The R
2
dis ibu ion ob ained om all p edic ion asks wi h he ou
di e en lea ning algo i hms is shown in Fig. 1a. O e all, he e appea
o be small di e ences in he pe o mance dis ibu ion. Al hough SVR
and Ca Boos achie ed he bes a e age R
2
o 0.44 ac oss all p edic ion
asks, hey we e only mode a ely be e han MLR, which had he lowes
a e age R
2
o 0.40. Simila ly, his is also e lec ed by he o dinally
anked RMSE in Fig. 2a. I illus a es how equen ly a p edic ion al-
go i hm achie ed each ank compa ed o o he s, based on he ascending
RMSE. Fo example, MLR had he lowes (i.e., he bes ) RMSE o 27 % o
he p edic ion asks, ecei ing he i s ank. Simul aneously, i had he
highes (i.e., he wo s ) RMSE o 30 % o p edic ion asks, placing i in
he ou h ank. O e all, Fig. 2a esembles a uni o m-like dis ibu ion.
This means ha each lea ning algo i hm had oughly he same numbe
o p edic ion asks in which hey anked bes o wo s . The Wilcoxon
Signed-Rank Tes u he con i med ha di e ences among he lea ning
algo i hms we e no s a is ically signi ican (Fig. 2a). This migh be
su p ising o some DSM p ac i ione s, because mo e sophis ica ed
me hods a e o en expec ed o signi ican ly ou pe o m a simple model
like MLR (Pada ian e al., 2020). Howe e , di e en lea ning algo-
i hms, including simplis ic models like MLR, ha e ad an ages unde
ce ain condi ions, as illus a ed in he nex pa ag aphs.
G ouping he p edic ion asks based on he p esence o absence o
is-NIR, NIR o MIR ea u es e eals a signi ican shi in he pe o -
mance dis ibu ion among he lea ning algo i hms (Fig. 1b-c & Fig. 2b-
c). The g ouping is made because da ase s wi h is-NIR, NIR and MIR a e
cha ac e ized by a highly in la ed ea u e o aining sample a io, so
ha dimensionali y educ ion is usually necessa y p io o he modeling.
On da ase s wi hou is-NIR, NIR and MIR, ee-based lea ning
Table 2
Hype pa ame e sea ch-space o SVR, Ca Boos and RF, whe e a andom
ins ance is d awn om he gi en dis ibu ion. Names o he hype pa ame e s
e e o he ange R-package (W igh and Ziegle , 2017) o RF, ca boos R-
package o Ca Boos (Dmi ie e al., 2024) and e1071 R-package (Meye e al.,
2024) o SVR.
Lea ning Algo i hm Hype pa ame e Sea ch Space Dis ibu ion
Ca Boos dep h [1, 10] Disc e e Uni o m
lea ning_ a e [0.005, 0.5] Log-Uni o m
i e a ions [50, 2000] Disc e e Uni o m
l2_lea _ eg [0, 10] Uni o m
sm [0.6, 1] Uni o m
subsample [0.6, 1] Uni o m
andom_s eng h [0.001, 10] Log-Uni o m
RF num. ees =2 000 −
m y [0.1, 1]* Uni o m
min.node.size [1, 12] Disc e e Uni o m
max.dep h [1, 10] Disc e e Uni o m
sample. ac ion [0.6, 1] Uni o m
SVR cos [0.01, 1000] Log-Uni o m
gamma [0.001, 10] Log-Uni o m
ke nel [linea , adial] Disc e e Uni o m
* Gi en as ac ion ins ead o absolu e alue inpu o R- unc ion.
J. Schmidinge e al.
Geode ma 459 (2025) 117337
5

algo i hms (i.e., RF and Ca Boos ) conside ably ou pe o med SVR and
MLR (Fig. 1b). This is u he highligh ed by he ac ha in 63 % o he
p edic ion asks in hese da ase s, RF o Ca Boos had he bes pe o -
mance (Fig. 2b). Meanwhile, MLR o SVR had he wo s pe o mance in
72.5 % o he cases. The ad an ages o ee-based lea ning algo i hms
o egula abula da ase s ha e been highligh ed in nume ous bench-
ma king s udies (G insz ajn e al., 2022; Shmuel e al., 2024; Shwa z-
Zi and A mon, 2022). Thei pa icula s eng hs include e ec i e eg-
ula iza ion when dealing wi h mul icollinea i y and i ele an ea u es,
along wi h he abili y o lea n i egula unc ions o in e ac ions. Fo his
eason, ee-based algo i hms, mos no ably RF, ha e become he mos
widely used lea ning algo i hms in DSM (Khaledian and Mille , 2020).
Opposi e esul s a e e iden o da ase s wi h is-NIR, NIR and MIR.
He e, SVR and MLR had on a e age an R
2
conside ably la ge han ha
o Ca Boos and RF (Fig. 1c). Addi ionally, hey anked i s in 69 % o
he p edic ion asks (Fig. 2c), whe eas RF o Ca Boos had he wo s
pe o mances in 73.8 % o he cases. The e y high dimensionali y and
inhe en mul icollinea i y in spec al da a, whe e adjacen bands a e
o en highly co ela ed, made ea u e dimensionali y educ ion ech-
niques, such as PCA and CMF, essen ial. Fo example, he da ase O.32
had up o 1,637 ea u es bu only 32 soil samples a ailable o aining
(Table 1). Lea ning algo i hms would se e ely o e i on his in la ed
ea u e o aining sample a io. Howe e , CMF and especially PCA
acili a ed p ope modeling, as shown by he bes selec ed
Fig. 1. Violin plo showing he dis ibu ion o R
2
alues g ouped by lea ning algo i hms o p edic ion asks om (a) all da ase s, (b) da ase s wi hou is-NIR, NIR,
o MIR, and (c) da ase s wi h is-NIR, NIR, o MIR. Ho izon al lines wi h labels ep esen he mean R
2
alue.
Fig. 2. Ba plo showing he ela i e equency dis ibu ion o o dinal anks based on he lowes RMSE (i.e., lowe ank numbe indica es be e pe o mance) o he
ou lea ning algo i hms o p edic ions asks om (a) all da ase s, (b) da ase s wi hou is-NIR, NIR, o MIR, and (c) da ase s wi h is-NIR, NIR, o MIR. Mean anks
o he o dinally anked RMSE o he lea ning algo i hms a e shown, including signi icance le e s om he Wilcoxon Signed-Rank Tes (
α
=0.1) o indica e whe he
he pe o mance di e ences be ween he lea ning algo i hms we e s a is ically signi ican .
J. Schmidinge e al.
Geode ma 459 (2025) 117337
6
hype pa ame e s (see Appendix C.2, Fig. 1C). This posed a disad an age
o he ee-based algo i hms because PCA has been epo ed o pe o m
weake when combined wi h ee-based algo i hms (Howley e al.,
2005). PCA c ea es new unco ela ed ea u es ha a e linea combina-
ions o he o iginal ea u es bu ee-based models a e in lexible o
adap o o a ed o ans o med da a ha do no ep esen he o iginal
ea u e composi ion (G insz ajn e al., 2022). Consequen ly, MLR and
SVR ou pe o med Ca Boos and RF due o hei be e compa ibili y
wi h PCA. P e ious benchma king s udies in soil spec oscopy, as
e iewed by Pada ian e al. (2020), ha e epo ed di e en ou comes
a o ing mo e sophis ica ed me hods. One possible explana ion o his
disc epancy is ha hese ea lie s udies elied on a single da ase , which
may inad e en ly lead o o e in e p e a ion o inciden al esul s.
Al e na i ely, an unin ended publica ion bias a o ing no el me hods
o e simple ones, as obse ed in o he compu a ional ields (Buchka
e al., 2021), could ha e con ibu ed o he di e ing ou comes. None-
heless, we acknowledge ha ee-based models possibly pe o m be e
on ull spec al da a when e y la ge aining se s a e a ailable
(Clingensmi h and G unwald, 2022) o wi h e ec i e ea u e selec ion
(Cane o e al., 2024). Such an ex ended analysis was no wi hin he
scope o his s udy.
Las ly, we ound a ela ionship be ween he mean o dinal ank o a
lea ning algo i hm and he sample size a ailable o a p edic ion ask
(Fig. 3). Mos no ably, MLR showed sensi i i y o he numbe o a ail-
able samples, as i s mean ank wo sened o da ase s wi h mo e han 100
samples. This aligns wi h p e ious esul s, which simila ly indica ed ha
MLR can ha e ad an ages o small sized da ase s bu may no be ideal i
enough aining da a is a ailable (Schmidinge e al., 2024b). The
opposi e beha io was obse ed o SVR, which conside ably imp o ed
i s ank o da ase s wi h mo e han 100 samples. Fo RF and Ca Boos ,
he e ec o he sample size was no as p onounced. Ca Boos was
sligh ly be e wi h mo e aining da a, whe eas RF was mo e e ec i e
o smalle da ase s.
Despi e he ad an ages ha ce ain lea ning algo i hms ha e, hei
supe io i y is no de e minis ic. Thei pe o mances can a y based on
nume ous ac o s and may no be op imal in e e y case. Fo example, RF
was gene ally he wo s pe o ming lea ning algo i hm o da ase s wi h
is-NIR, NIR and MIR (Fig. 1b), ye i s ill u ned ou o be bes in 10 % o
he p edic ion asks (Fig. 2b). The e o e, elying on a single da ase is
insu icien o es ablish he supe io i y o one me hod and may os e
misleading conclusions based on non-gene alizable esul s.
4. Fu he applica ions
Fo demons a i e pu poses, we es ic ed he benchma king o ou
lea ning algo i hms, al hough many o he ele an me hods emain o
be explo ed. The benchma king s udy o Sec ion 3.2 is ully ep oduc-
ible, as he code and da ase s ha e been published. The e o e, addi ional
lea ning algo i hms o di e en ea u e selec ion s a egies can eadily
be in eg a ed in o he pipeline o Sec ion 3.1 o u he ex end he
analysis. While neu al ne wo ks we e no included in his ini ial
benchma king due o hei ela i ely poo pe o mances in la ge
abula benchma king s udies (Shmuel e al., 2024), ecen ad ance-
men s in neu al ne wo ks designed o abula da a such as TabPFN
(Hollmann e al., 2025) ha e demons a ed p omising esul s. Fi s
success ul applica ions o TabPFN in DSM can be ound by Ba ko e al.
(2024) bu i s applica ion has o be u he e alua ed. Las ly, we
encou age ex ending he benchma king wi h mo e open da ase s, such
as hose a ailable om OSSL, in addi ion o LimeSoDa.
Wi hin he scope o his s udy, we ocused en i ely on he need o
open da ase s o benchma king pu poses o add ess cu en ly p esen
sho comings. None heless, he usage o LimeSoDa should no be
es ic ed o s a is ical benchma king o me hod de elopmen . Open
da ase s om a ious geog aphical con ex wi h di e en sensing ech-
niques may enhance ou gene al unde s anding o pedological p o-
cesses. I allows o he c i ical e alua ion o soil mapping in
ag onomical decision making and asses he obus ness o a ious soil
sensing echniques. Especially he liming con ex o he da ase can be
use ul o s udies in he ield o p ecision ag icul u e. Consequen ly,
LimeSoDa is a aluable ool o add essing key challenges o pedo-
me ics ou side o DSM benchma king (Wadoux e al., 2021). In o he
academic ields, seconda y da a analysis has al eady answe ed a ious
esea ch ques ions un ela ed o he o iginal esea ch pu pose o he
da ase (G eene e al., 2017). Las ly, spec oscopy da a om LimeSoDa
can be ha monized and added o o he spec al lib a ies as addi ional
aining da a o imp o e global modeling.
5. Conclusion
Cu en benchma king p ac ices in DSM su e om da a limi a ions
ha lead o incomple e o po en ially biased conclusions. The e is a lack
o open da ase s om a ious domains, spa ial dimensions and ypes o
senso s. To add ess his p oblem, we in oduced an open-access da a
collec ion called LimeSoDa, which cu en ly consis s o 31 ield- and
a m-scale da ase s. LimeSoDa o e s da ase s ha a e eady- o-use o
modeling and co e s a ious ypes o ea u es om di e en sensing
Fig. 3. Line plo showing he mean o dinal ank based on he lowes RMSE (i.e., lowe ank numbe indica es be e pe o mance) in dependence o he sample size.
Lines a e di e en ia ed by he ou lea ning algo i hms and he ype o ea u es p esen in he da ase o he p edic ion ask.
J. Schmidinge e al.
Geode ma 459 (2025) 117337
7
echniques. This enables hose who a e wo king in he ield o DSM o
benchma k s a is ical me hods on a di e se ange o soil da ase s.
Fu he , he open license is in ended o imp o e he ep oducibili y.
The u ili y o LimeSoDa was demons a ed h ough a benchma king
s udy wi h ou lea ning algo i hms. The esul s showed ha no algo-
i hm signi ican ly exceeded he o he s ac oss all da ase s. Ins ead,
di e en lea ning algo i hms had ad an ages depending on he ype o
ea u es and sample size o a da ase . On a e age, ee-based algo i hms,
i.e., Ca Boos and RF, pe o med be e on da ase s wi hou is-NIR, NIR
and MIR ea u es, p o ing hei sui abili y o con en ional abula
da ase s. In con as , SVR and MLR ou pe o med Ca Boos and RF o
da ase s wi h is-NIR, NIR and MIR due o hei be e compa ibili y
wi h PCA- ans o med da a. Addi ionally, he aining sample size
in luenced he anking o a lea ning algo i hm. The ela i e pe o mance
mos no ably dec eased o MLR and inc eased o SVR wi h mo e
aining samples.
A benchma king s udy based on a single da ase could no ha e
e ealed such con ex -dependen pe o mance o lea ning algo i hms.
Mo e so, elying on singula da ase s isks o e in e p e ing inciden al
ou comes and a po en ial publica ion bias a o ing newe me hods
canno be uled ou . In con as , LimeSoDa acili a es mo e in-dep h
analyses and enables comp ehensi e conclusions. Addi ionally, he
benchma king can eadily be ex ended wi h u he lea ning algo i hms
o p e-p ocessing echniques because da ase s and code a e openly
a ailable.
Beyond benchma king, he e a e u he applica ions and challenges
in pedome ics ha bene i om open da ase s. LimeSoDa can be used o
in es iga e pedological p ocesses ac oss di e se geog aphical con ex s
o he spec al da a can be in eg a ed in o o he spec oscopy lib a ies o
imp o e global modeling. In summa y, by p o iding a ich and di e se
collec ion o open da ase s, LimeSoDa has he po en ial o signi ican ly
ad ance ML applica ions in pedome ics.
Funding
This esea ch was suppo ed by he Lowe Saxony Minis y o Science
and Cul u e (MWK), unded h ough he zukun .niede sachsen p og am
o he Volkswagen Founda ion (ZN4072) as well as he Fede al Minis y
o Educa ion and Resea ch o Ge many (BMBF) h ough he BonaRes
p ojec I4S: In elligence o Soil (031B1069A). Compu e esou ces we e
unded by he Deu sche Fo schungsgemeinscha (DFG, Ge man
Resea ch Founda ion) p ojec numbe 456666331.
Decla a ion o compe ing in e es
The au ho s decla e ha hey ha e no known compe ing inancial
in e es s o pe sonal ela ionships ha could ha e appea ed o in luence
he wo k epo ed in his pape .
Acknowledgemen s
We a e hank ul o a lo o scien is s, ield- and echnical assis an s
who helped us o ga he he da a and me ada a o he da ase s.
Appendix A:. Li e a u e e iew on DSM benchma king s udies
Appendix A.1: Li e a u e e iew me hodology
The “Web o Science” da abase was used o he li e a u e e iew. We sea ched o “digi al soil mapping” and “p edic i e soil mapping” as
keywo ds in he abs ac o documen s de ined as “da a pape ”, “ea ly access” o “a icle” and es ic ed he sea ch o publica ions om he yea 2023.
This esul ed in an ini ial pool o 192 pape s. F om his pool, eigh pape s we e excluded because hey we e no w i en in English, missed DSM con ex
o we e inaccessible. Ano he 73 we e emo ed because he abs ac did no indica e ha any s a is ical benchma king was conduc ed, leading o a
o al o 111 pape s o he e iew on DSM benchma king p ac ices. As benchma king we conside ed a b oad de ini ion, encompassing any compa ison
o compe ing s a is ical me hods based on quan i ied alida ion me ics. This included compa isons o : p edic ion algo i hms, ea u e selec ion
me hods, sampling designs, enginee ed ea u es e c. We did no conside he compa ison o senso s o ea u es as benchma king, i he ocus did no lie
on he s a is ical enginee ing o he ea u es (e.g., empo al s acks). In some s udies, he benchma king was no he main objec i e o he s udy bu
a he supplemen a y in o ma ion. We also conside ed hese as “benchma king s udy” when he compa ison was men ioned in he abs ac .
The code and da a a ailabili y in each o he 111 pape s was e alua ed. We di e en ia ed be ween “no sha ed”, “sha ed” and “pa ially sha ed”.
The s a emen on ma e ial a ailabili y and any supplemen a y in o ma ion p o ided we e examined alongside keywo ds such as “ eposi o y”,
“da ase ”, “Gi Hub”, “Zenodo”, “code” e c. o de e mine he a ailabili y o code o da a. We classi ied any da a o code o be “sha ed” when hey we e
di ec ly accessible h ough he supplemen a y in o ma ion o a eposi o y (e.g., Zenodo o Gi Hub). In some code- eposi o ies, da ase s we e no
di ec ly p o ided due o es ic i e da ase licenses (e.g., as in LUCAS) bu links o he websi es ha hos ed hese da ase s we e added. This was also
conside ed as “sha ed” when i was p o ided in combina ion wi h he da ase p e-p ocessing code. In o he cases, links we e gi en bu hey expi ed
since he publica ion o he da ase hos ing webpages we e no in English, which made na iga ion impossible. These cases we e conside ed “no
sha ed”. Las ly, i was e alua ed how many da ase s we e used o he benchma king. We classi ied any collec ion o da a based on a cohe en
sampling in a dis inc s udy a ea as da ase .
Appendix A.2: Li e a u e e iew esul s
A majo i y o o e 90 % nei he sha ed he da a no he code (Fig. A1). Howe e , many s udies (48 %) included a s a emen on he da a a ailabili y
wi h willingness o p o ide da a on eques . In con as , such a s a emen on he a ailabili y o code was almos ne e included apa om a single
excep ion (1 %). This can be explained by he ac ha mos jou nals ask o a s a emen on he a ailabili y o da a bu do no expec his o he code
a ailabili y.
The majo i y o benchma king was conduc ed based on a single da ase (95.5 %) and he maximum numbe o da ase s did no exceed h ee
(Fig. A2). All ma e ials used in he li e a u e e iew a e p o ided a gi hub.com/JonasSchmidinge /LimeSoDa_li e a u e. e iew, wi h addi ional
commen s o bounda y cases.
J. Schmidinge e al.
Geode ma 459 (2025) 117337
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Fig. A1. Ba plo showing he ela i e equency dis ibu ion on he a ailabili y o da a (a) o code (c) in o he DSM benchma king publica ions om 2023, as well as
he s a emen on da a (b) o code (d) a ailabili y i da ase s we e no sha ed.
Fig. A2. Ba plo showing he ela i e equency dis ibu ion on how many da ase s we e e alua ed in o he DSM benchma king publica ions om 2023.
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Geode ma 459 (2025) 117337
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