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Computational chemistry experiments performed directly on a blockchain virtual computer

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Blockchain technology has had a substantial impact across multiple disciplines, creating new methods for storing and processing data with improved transparency, immutability, and reproducibility. These developments come at a time when the reproducibility of many scientific findings has been called into question, including computational studies. Here we present a computational chemistry simulation run directly on a blockchain virtual machine, using a harmonic potential to model the vibration of carbon monoxide. The results demonstrate for the first time that computational science calculations are feasible entirely within a blockchain environment and that they can be used to increase transparency and accessibility across the computational sciences.
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Computational chemistry experiments performed
directly on a blockchain virtual computer
Magnus W. D. Hanson-Heine *
a
and Alexander P. Ashmore
b
Blockchain technology has had a substantial impact across multiple disciplines, creating new methods for
storing and processing data with improved transparency, immutability, and reproducibility. These
developments come at a time when the reproducibility of many scienticndings has been called into
question, including computational studies. Here we present a computational chemistry simulation run
directly on a blockchain virtual machine, using a harmonic potential to model the vibration of carbon
monoxide. The results demonstrate for the rst time that computational science calculations are feasible
entirely within a blockchain environment and that they can be used to increase transparency and
accessibility across the computational sciences.
Distributed ledger technology has become an area of signicant
interest since the release of the rst blockchain based crypto-
currency, Bitcoin, in 2008.
1
Since then blockchains have been
used to increase transparency, immutability, and resistance to
censorship in many areas outside of nance, including
improving the reliability of medical trials,
2
increasing energy
eciency,
3
and allowing transparent and censorship resistant
computation.
4
The development of blockchain computation
opens up the possibility of running computational science
experiments. However, physical simulations have not previously
been performed directly on blockchain virtual machines, and
the rst simulation of this kind is presented here.
Open public blockchains aim to create an electronic ledger
with a provably tamperproof record of data that is available for
anyone to review or add to in perpetuity without the possibility of
censorship by a third party. This is carried out principally using
a cryptographic hashing function that maps variable length input
data to a xed-length output called a hash. Any change to the
input results in an unpredictable change to the hash, and blocks
of newly appended data are required to contain a hash of the
previous block so that revisions will invalidate the hash of the
subsequent block and allow changes to be identied and removed
automatically using a process known as proof-of-work.
1
The
details of how proof-of-work operates to maintain these properties
have been discussed elsewhere.
1,4
Several studies now indicate that a signicant amount of the
scientic literature cannot be replicated across a wide range of
disciplines. In 2015 Aarts et al. examined reproducibility in the
psychological scientic literature and found just over a third of
the reproduced studies yielded statistically signicant results
compared to 97% of the original publications, with replication
rates of roughly half in cognitive psychology and roughly
a quarter in social psychology.
5
A 2016 study of the economics
literature by Camerer et al. found that ca. 39% of the sampled
studies could not be replicated,
6
and a 2018 study found that ca.
38% of social and behavioural science papers could not be
replicated even when sampled exclusively from the journals
Nature and Science.
7
A 2008 meta-analysis by Fanelli also indi-
cated that roughly 2% of the scientists surveyed had admitted to
fabricating, falsifying or modifying data or results at least once,
with roughly a third admitting to other questionable research
practices,
8
further emphasising the importance of replication.
Although reproducibility rates are expected to be higher in
the computational sciences where simulations made up of
anite set of operations carried out on a deterministic
computer allow for bit-for-bit replication, several publications
have indicated that replication diculties persist in elds as
diverse as computational chemistry,
9
harmonic analysis,
10
and
neuroscience.
11,12
Computational replication is oen hindered
by a lack of access to the original output data, input les, so-
ware, hardware, and workow, which can be dicult to main-
tain over extended periods of time.
13
Reliably storing and
accessing data can also be complicated by scientic censor-
ship.
14
Many rejections in peer-reviewed journals are due to
quality control. However, there is evidence that some journal
editors and referees can be hostile to work that challenges their
current beliefs,
1522
which can delay or even prevent researchers
from gaining access to peer-reviewed archiving services. In
extreme cases, governments have also been known to remove or
restrict access to the data provided by peer-reviewed
journals.
2325
a
School of Chemistry, University of Nottingham, University Park, Nottingham NG7
2RD, UK. E-mail: magnus.hansonheine@nottingham.ac.uk
b
School of Computing and Communications, The Open University, Walton Hall, Kents
Hill, Milton Keynes MK7 6AA, UK
Electronic supplementary information (ESI) available. See DOI:
10.1039/d0sc01523g
Cite this: DOI: 10.1039/d0sc01523g
All publication charges for this article
have been paid for by the Royal Society
of Chemistry
Received 13th March 2020
Accepted 15th April 2020
DOI: 10.1039/d0sc01523g
rsc.li/chemical-science
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In 2015 the Ethereum network became the rst instance of
a blockchain acting as a virtual computer capable of performing
general computation.
4
Blockchain based computational science
experiments can in principle solve many of the problems dis-
cussed for physical simulations by providing an eectively
unchangeable record of the computational environment,
including the exact piece of soware used, a complete record of
the associated computational steps, and open access for review
and replication. However, computational science experiments
using blockchains have not previously been performed. So-
ware has not yet been developed to facilitate this kind of
calculation, blockchains are not currently optimized for
running these types of calculation, and the computational
power of blockchains is still very limited compared to that of
most conventional computers.
In order to prove that physical simulations can be performed
using a blockchain, an atomistic molecular dynamics simula-
tion was performed for the carbon monoxide molecule over a 40
fs time scale with a harmonic potential used to model the
carbonoxygen molecular bond. Soware compatible with the
Ethereum blockchain was written in the Solidity programming
language in order to run a diatomic molecular dynamics
trajectory with a variation of the velocity Verlet algorithm used
to integrate Newton's equations of motion in atomic units.
26
The simulation was executed for 400 time steps of 0.1 fs with an
initial bond length of 120 pm. The model used an equilibrium
bond length parameter of 112.8 pm and a force constant of
1855 N m
1
, together with the masses of
12
C and
16
O assigned to
the atoms, in order to model carbon monoxide. An equivalent
simulation was written using the C# programming language,
and executed on a local machine for comparison.
The molecular dynamics trajectories in Fig. 1 show that
simulations of this kind can be performed entirely within
a blockchain environment, and that doing so produces an
identical output to local execution on a conventional machine
within user specied precision of 1 10
10
a
0
. The details of
this precision threshold, both algorithms, and their outputs can
be found in the ESI.The simulation that was carried out on the
Ethereum network was also recorded on the blockchain in real
time. The addition of both the code and simulation output into
blocks of data on the blockchain happened as part of the
process of running the simulation, and these entries can be
used to track the provenance of the data for review and repli-
cation studies. The hashes and block numbers corresponding
to these data on the Ethereum blockchain are included in the
ESIin addition to the discussion below, and can be used to
both access the data and validate that the simulation record
remains unchanged.
Timing when experiments occur can also be important for
a number of reasons. When similar discoveries are made by
independent researchers, the claims to the discoveries are oen
adjudicated based on when the specic observations or calcu-
lations were made, with famous examples including the
controversy between Leibniz and Newton over who invented
calculus. Knowing the order in which experiments were carried
out is also useful when analyzing methods of hypothesis testing
and conclusion formation that can dier depending on the
order in which observations happen. An important property of
many blockchains, including Ethereum, is therefore the crea-
tion of an internal chronology. New blocks are appended
regularly, and the designed immutability of old data means that
the position which calculations have in the blockchain acts as
an automatic time stamp that can be used to verify the order in
which they were performed. The computational complexity of
solving the hashing function needed to append data is also
commonly modied to give a regular time interval between
blocks that can be used to approximate timings between
dierent blockchains.
1,4
In this case a preliminary trajectory of
10 time steps (1 fs) was run on the Ethereum blockchain prior to
the main production run. The two trajectories have been
recorded in blocks 9 360 161 and 9 360 178, respectively. This
information provides an eective time stamp showing the order
in which these simulations were performed. The output data
and blockchain address of the preliminary simulation are also
given in the ESI,and the transaction and block details for both
simulations are shown in Fig. 2.
The complexity and length of these simulations are currently
limited by the capacity of the available blockchains. The Ether-
eum block for the production simulation was generated in ca.
11 s compared to a ca. 135 ms execution time for the C# simu-
lation when executed on a standalone desktop machine running
with a i7-4790k CPU and 32 GB of 1333 MHz DDR3 RAM. Each
simulation was also recorded in a single block so as to avoid the
need for manual interaction with the blockchain during their
execution. While the exact computational power of the Ethereum
network is variable, the network has a xed limit to the maximum
amount of computation that can be performed as part of gener-
ating a single block. This computational limit is measured in
units known as gas, and is currently set at 10 000 000 gas at the
time of writing. A more detailed description of the relationship
between gas and computational operations can be found in the
ESIand associated resources. However, at this time we were
unable to signicantly increase the complexity of the simulation
beyond the level reported. By comparison, large scale distributed
computational science resources operating without distributed
ledger technology can have signicant computational
throughput and storage requirements, with the well known
example of the Folding@Home protein folding network report-
edly calculating over 1 10
18
operations per second earlier this
Fig. 1 Molecular dynamics trajectories showing bond length variation
over time for (a) the trajectory coded in C# and run on a local
computer, and (b) the rescaled trajectory coded in Solidity and run on
the Ethereum blockchain.
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year. Signicant advancements in blockchain computer science
and developments in combining on-chainand o-chain
calculation data are therefore necessary before blockchain
calculations can become a routine method for performing
computational experiments.
Conclusions
These results show that simulations of this kind are possible
and that there can be signicant benets to using blockchains
for computational science. The ability to run computational
experiments with the properties outlined is expected to have an
increasing impact across the computational sciences as the
capacity of these blockchains continues to scale, and current
plans to introduce blockchain database sharding to the Ether-
eum network are expected to produce a greater than 1000 fold
increase in the computational throughput in the near future.
Furthermore, running hybrid computational experiments that
use blockchain based calculation and storage for certain parts
of an experiment, and conventional o-chain computers for
others, may allow some of these advantages to be introduced
selectively at a signicantly reduced computational cost.
Conicts of interest
The authors declare no competing nancial interest.
References
1 S. Nakamoto, Bitcoin: A Peer-to-Peer Electronic Cash System,
2008, https://bitcoin.org/bitcoin.pdf, accessed June 22, 2019.
2 D. R. Wong, S. Bhattacharya and A. J. Butte, Nat. Commun.,
2019, 10, 917.
3 M. Andoni, V. Robu, D. Flynn, S. Abram, D. Geach,
D. Jenkins, P. McCallum and A. Peacock, Renewable
Sustainable Energy Rev., 2019, 100, 143174.
4 V. Buterin, A Next-Generation Smart Contract and
Decentralized Application Platform, 2014, https://
cryptorating.eu/whitepapers/Ethereum/
Ethereum_white_paper.pdf, accessed November 13, 2019.
5 A. A. Aarts, J. E. Anderson, C. J. Anderson, P. R. Attridge,
A. Attwood, J. Axt, M. Babel, ˇ
S. Bahn´
ık, E. Baranski,
M. Barnett-Cowan, E. Bartmess, J. Beer, R. Bell, H. Bentley,
L. Beyan, G. Binion, D. Borsboom, A. Bosch, F. A. Bosco,
S. D. Bowman, M. J. Brandt, E. Braswell, H. Brohmer,
B. T. Brown, K. Brown, J. Br¨
uning, A. Calhoun-Sauls,
S. P. Callahan, E. Chagnon, J. Chandler, C. R. Chartier,
F. Cheung, C. D. Christopherson, L. Cillessen, R. Clay,
H. Cleary, M. D. Cloud, M. Cohn, J. Cohoon, S. Columbus,
A. Cordes, G. Costantini, L. D. C. Alvarez, E. Cremata,
J. Crusius, J. DeCoster, M. A. DeGaetano, N. D. Penna,
B. d. Bezemer, M. K. Deserno, O. Devitt, L. Dewitte,
D. G. Dobolyi, G. T. Dodson, M. B. Donnellan, R. Donohue,
Fig. 2 Transaction and block data for the 1 fs preliminary molecular dynamics trajectory (top panel) and the 40 fs production molecular dynamics
trajectory (bottom panel).
This journal is © The Royal Society of Chemistry 2020 Chem. Sci.
Edge Article Chemical Science
Open Access Article. Published on 21 April 2020. Downloaded on 4/27/2020 1:01:20 PM.
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R. A. Dore, A. Dorrough, A. Dreber, M. Dugas, E. W. Dunn,
K. Easey, S. Eboigbe, C. Eggleston, J. Embley, S. Epskamp,
T. M. Errington, V. Estel, F. J. Farach, J. Feather, A. Fedor,
B. Fern´
andez-Castilla, S. Fiedler, J. G. Field, S. A. Fitneva,
T. Flagan, A. L. Forest, E. Forsell, J. D. Foster, M. C. Frank,
R. S. Frazier, H. Fuchs, P. Gable, J. Galak, E. M. Galliani,
A. Gampa, S. Garcia, D. Gazarian, E. Gilbert, R. Giner-
Sorolla, A. Gl¨
ockner, L. Goellner, J. X. Goh, R. Goldberg,
P. T. Goodbourn, S. Gordon-McKeon, B. Gorges, J. Gorges,
J. Goss, J. Graham, J. A. Grange, J. Gray, C. Hartgerink,
J. Hartshorne, F. Hasselman, T. Hayes, E. Heikensten,
F. Henninger, J. Hodsoll, T. Holubar, G. Hoogendoorn,
D. J. Humphries, C. O.-Y. Hung, N. Immelman, V. C. Irsik,
G. Jahn, F. J¨
akel, M. Jekel, M. Johannesson, L. G. Johnson,
D. J. Johnson, K. M. Johnson, W. J. Johnston, K. Jonas,
J. A. Joy-Gaba, H. B. Kappes, K. Kelso, M. C. Kidwell,
S. K. Kim, M. Kirkhart, B. Kleinberg, G. Kneˇ
zevi´
c,
F. M. Kolorz, J. J. Kossakowski, R. W. Krause, J. Krijnen,
T. Kuhlmann, Y. K. Kunkels, M. M. Kyc, C. K. Lai,
A. Laique, D. Lakens, K. A. Lane, B. Lassetter,
L. B. Lazarevi´
c, E. P. LeBel, K. J. Lee, M. Lee, K. Lemm,
C. A. Levitan, M. Lewis, L. Lin, S. Lin, M. Lippold,
D. Loureiro, I. Luteijn, S. Mackinnon, H. N. Mainard,
D. C. Marigold, D. P. Martin, T. Martinez, E. J. Masicampo,
J. Matacotta, M. Mathur, M. May, N. Mechin, P. Mehta,
J. Meixner, A. Melinger, J. K. Miller, M. Miller, K. Moore,
M. M¨
oschl, M. Motyl, S. M. M¨
uller, M. Munafo,
K. I. Neijenhuijs, T. Nervi, G. Nicolas, G. Nilsonne,
B. A. Nosek, M. B. Nuijten, C. Olsson, C. Osborne,
L. Ostkamp, M. Pavel, I. S. Penton-Voak, O. Perna,
C. Pernet, M. Perugini, R. N. Pipitone, M. Pitts, F. Plessow,
J. M. Prenoveau, R.-M. Rahal, K. A. Ratli, D. Reinhard,
F. Renkewitz, A. A. Ricker, A. Rigney, A. M. Rivers,
M. Roebke, A. M. Rutchick, R. S. Ryan, O. Sahin, A. Saide,
G. M. Sandstrom, D. Santos, R. Saxe, R. Schlegelmilch,
K. Schmidt, S. Scholz, L. Seibel, D. F. Selterman, S. Shaki,
W. B. Simpson, H. C. Sinclair, J. L. M. Skorinko, A. Slowik,
J. S. Snyder, C. Soderberg, C. Sonnleitner, N. Spencer,
J. R. Spies, S. Steegen, S. Stieger, N. Strohminger,
G. B. Sullivan, T. Talhelm, M. Tapia, A. t. Dorsthorst,
M. Thomae, S. L. Thomas, P. Tio, F. Traets, S. Tsang,
F. Tuerlinckx, P. Turchan, M. Val´
aˇ
sek, A. E. v. t. Veer,
R. V. Aert, M. v. Assen, R. v. Bork, M. v. d. Ven,
D. v. d. Bergh, M. v. d. Hulst, R. v. Dooren, J. v. Doorn,
D. R. v. Renswoude, H. v. Rijn, W. Vanpaemel,
A. V. Echeverr´
ıa, M. Vazquez, N. Velez, M. Vermue,
M. Verschoor, M. Vianello, M. Voracek, G. Vuu,
E.-J. Wagenmakers, J. Weerdmeester, A. Welsh,
E. C. Westgate, J. Wissink, M. Wood, A. Woods, E. Wright,
S. Wu, M. Zeelenberg and K. Zuni, Science, 2015, 349,
aac4716.
6 C. F. Camerer, A. Dreber, E. Forsell, T.-H. Ho, J. Huber,
M. Johannesson, M. Kirchler, J. Almenberg, A. Altmejd,
T. Chan, E. Heikensten, F. Holzmeister, T. Imai,
S. Isaksson, G. Nave, T. Pfeier, M. Razen and H. Wu,
Science, 2016, 351, 1433.
7 C. F. Camerer, A. Dreber, F. Holzmeister, T.-H. Ho, J. Huber,
M. Johannesson, M. Kirchler, G. Nave, B. A. Nosek,
T. Pfeier, A. Altmejd, N. Buttrick, T. Chan, Y. Chen,
E. Forsell, A. Gampa, E. Heikensten, L. Hummer, T. Imai,
S. Isaksson, D. Manfredi, J. Rose, E.-J. Wagenmakers and
H. Wu, Nat. Hum. Behav., 2018, 2, 637644.
8 D. Fanelli, PLoS One, 2009, 4, e5738.
9 F.-X. Coudert, Chem. Mater., 2017, 29, 26152617.
10 D. L. Donoho, A. Maleki, I. U. Rahman, M. Shahram and
V. Stodden, Comput. Sci. Eng., 2009, 11,818.
11 T. Manninen, R. Havela and M.-L. Linne, Front. Neuroinf.,
2017, 11, 11.
12 M. Miłkowski, W. M. Hensel and M. Hohol, J. Comput.
Neurosci., 2018, 45, 163172.
13 N. P. Rougier, K. Hinsen, F. Alexandre, T. Arildsen,
L. A. Barba, F. C. Y. Benureau, C. T. Brown, P. de Buyl,
O. Caglayan, A. P. Davison, M. A. Delsuc, G. Detorakis,
A. K. Diem, D. Drix, P. Enel, B. Girard, O. Guest,
M. G. Hall, R. N. Henriques, X. Hinaut, K. S. Jaron,
M. Khamassi, A. Klein, T. Manninen, P. Marchesi,
D. McGlinn, C. Metzner, O. Petchey, H. E. Plesser,
T. Poisot, K. Ram, Y. Ram, E. Roesch, C. Rossant,
V. Rostami, A. Shifman, J. Stachelek, M. Stimberg,
F. Stollmeier, F. Vaggi, G. Viejo, J. Vitay, A. E. Vostinar,
R. Yurchak and T. Zito, PeerJ Comput. Sci., 2017, 3, e142.
14 B. Martin, Account. Res., 1999, 6, 333372.
15 B. Barber, Science, 1961, 134, 596.
16 M. J. Mahoney, Soc. Stud. Sci., 1979, 9, 349375.
17 D. F. Horrobin, J. Am. Med. Assoc., 1990, 263, 14381441.
18 W. M. Epstein, Sci. Technol. Hum. Values, 1990, 15,938.
19 J. M. Campanario, Sci. Commun., 1995, 16, 304325.
20 J. S. Armstrong, INFORMS J. Appl. Anal., 1996, 26,2555.
21 J. S. Armstrong, Sci. Eng. Ethics, 1997, 3,6384.
22 J. M. Campanario, Scientometrics, 2009, 81, 549565.
23 W. D. Carey, Science, 1983, 219, 911.
24 U. Hoßfeld and L. Olsson, Nature, 2006, 443, 271.
25 M. Y. H. Wong and Y.-h. Kwong, PS Political Sci. Politics,
2019, 52, 287292.
26 W. C. Swope, H. C. Andersen, P. H. Berens and K. R. Wilson,
J. Chem. Phys., 1982, 76, 637649.
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Computational science experiments within an open blockchain environment have recently been demonstrated, and can improve transparency, reproducibility, and censorship resistance in theoretical scientific work. However, the append-only nature of these records also means that historical calculation errors cannot be effectively removed or changed. This process preserves otherwise unavailable data on the scientific process of error correction, and is shown here for simulations of carbon monoxide.
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Replicability and reproducibility of computational models has been somewhat understudied by “the replication movement.” In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that model replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without original code, serve different functions and fail for different reasons. This means that measures designed to improve model replicability may not enhance (and, in some cases, may actually damage) model reproducibility. We claim that although both are undesirable, low model reproducibility poses more of a threat to long-term scientific progress than low model replicability. In our opinion, low model reproducibility stems mostly from authors' omitting to provide crucial information in scientific papers and we stress that sharing all computer code and data is not a solution. Reports of computational studies should remain selective and include all and only relevant bits of code.
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Being able to replicate scientific findings is crucial for scientific progress. We replicate 21 systematically selected experimental studies in the social sciences published in Nature and Science between 2010 and 2015. The replications follow analysis plans reviewed by the original authors and pre-registered prior to the replications. The replications are high powered, with sample sizes on average about five times higher than in the original studies. We find a significant effect in the same direction as the original study for 13 (62%) studies, and the effect size of the replications is on average about 50% of the original effect size. Replicability varies between 12 (57%) and 14 (67%) studies for complementary replicability indicators. Consistent with these results, the estimated true-positive rate is 67% in a Bayesian analysis. The relative effect size of true positives is estimated to be 71%, suggesting that both false positives and inflated effect sizes of true positives contribute to imperfect reproducibility. Furthermore, we find that peer beliefs of replicability are strongly related to replicability, suggesting that the research community could predict which results would replicate and that failures to replicate were not the result of chance alone.
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Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results, however computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. James Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested, hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests.
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The scientific community across all disciplines faces the same challenges of ensuring accessibility, reproducibility, and efficient comparability of scientific results. Computational neuroscience is a rapidly developing field, where reproducibility and comparability of research results have gained increasing interest over the past years. As the number of computational models of brain functions is increasing, we chose to address reproducibility using four previously published computational models of astrocyte excitability as an example. Although not conventionally taken into account when modeling neuronal systems, astrocytes have been shown to take part in a variety of in vitro and in vivo phenomena including synaptic transmission. Two of the selected astrocyte models describe spontaneous calcium excitability, and the other two neurotransmitter-evoked calcium excitability. We specifically addressed how well the original simulation results can be reproduced with a reimplementation of the models. Additionally, we studied how well the selected models can be reused and whether they are comparable in other stimulation conditions and research settings. Unexpectedly, we found out that three of the model publications did not give all the necessary information required to reimplement the models. In addition, we were able to reproduce the original results of only one of the models completely based on the information given in the original publications and in the errata. We actually found errors in the equations provided by two of the model publications; after modifying the equations accordingly, the original results were reproduced more accurately. Even though the selected models were developed to describe the same biological event, namely astrocyte calcium excitability, the models behaved quite differently compared to one another. Our findings on a specific set of published astrocyte models stress the importance of proper validation of the models against experimental wet-lab data from astrocytes as well as the careful review process of models. A variety of aspects of model development could be improved, including the presentation of models in publications and databases. Specifically, all necessary mathematical equations, as well as parameter values, initial values of variables, and stimuli used should be given precisely for successful reproduction of scientific results.
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The recent censorship requests made by Chinese authorities to Western academic publishers have sent shockwaves throughout the academic world. This article examines the high-profile The China Quarterly incident as a case in point. Because the censorship is expected to be followed by similar demands to other publications, it is important for the academic community to explore the logic behind it. This research article provides a preliminary analysis of publications on the censorship list and compares them to uncensored articles on similar themes. This exercise allows us to draw important insights. Theoretically, this article makes an original contribution by going beyond the censorship within to outside China. Empirically, it offers a comprehensive analysis of what China wants to censor and the context for its actions.
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The reproducibility of scientific findings has been called into question. To contribute data about reproducibility in economics, we replicate 18 studies published in the American Economic Review and the Quarterly Journal of Economics in 2011-2014. All replications follow predefined analysis plans publicly posted prior to the replications, and have a statistical power of at least 90% to detect the original effect size at the 5% significance level. We find a significant effect in the same direction as the original study for 11 replications (61%); on average the replicated effect size is 66% of the original. The reproducibility rate varies between 67% and 78% for four additional reproducibility indicators, including a prediction market measure of peer beliefs.
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Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.