A key tenet of the scientific method is that we learn from previous work. In principle we observe something about the world and generate a hypothesis. We then design an experiment to test that hypothesis, set up the experiment, collect the data and analyse the results. And when we report our results and interpretation of them in a paper, we make it possible for other researchers to build on our work.

In practice, there are impediments at every step of the process. In particular, our work depends on published research that often does not contain all the information required to reproduce what was reported. There are too many possible experimental parameters to test under our time and budget constraints, so we make decisions that affect how we interpret the outcomes of our experiments. As researchers, we should not be complacent about these obstacles: rather, we should always look towards new technologies, such as data science, to help us improve the quality and efficiency of scientific research.

Data science could easily be dismissed as a simple rebranding of "science" – after all, nearly all scientists analyse data in some form. An alternative definition of a data scientist is someone who develops new computational or statistical analysis techniques that can easily be adapted to a wide range of scenarios, or who can apply these techniques to answer a specific scientific question. While there is no clear dividing line between data science and statistics, data science generally involves larger datasets. Moreover, data scientists often think in terms of training predictive models that can be applied to other datasets, rather than limiting the analysis to an existing dataset.

Data science emerged as a discipline largely because the internet led to the creation of incredibly large datasets (such as ImageNet, a database of 14 million annotated images; Krizhevsky et al., 2012). The availability of these datasets enabled researchers to apply a variety of machine learning algorithms which, in turn, led to the development of new techniques for analysing large datasets. One area in which progress has been rapid is the automated annotation and interpretation of images and texts on the internet, and these techniques are now being applied to other data-rich domains, including genetics and genomics (Libbrecht and Noble, 2015) and the study of gravitational waves (Abbott et al., 2016).

It is clear that data science can inform the analysis of an experiment, either to test a specific hypothesis or to make sense of large datasets that have been collected without a specific hypothesis in mind. What is less obvious, albeit equally important, is how these techniques can improve other aspects of the scientific method, such as the generation of hypotheses and the design of experiments.

Data science is an inherently interdisciplinary approach to science. New experimental techniques have revolutionised biology over the years, from DNA sequencing and microarrays in the past to CRISPR and cryo-EM more recently. Data science differs in that it is not a single technique, but rather a framework for solving a whole range of problems. The potential for data science to answer questions in a range of different disciplines is what excites so many researchers. That said, however, there are social challenges that cannot be fixed with a technical solution, and it is all too easy for expertise to be "lost in translation" when people from different academic backgrounds come together.

In October 2018, we brought together statisticians, experimental researchers, and social scientists who study the behaviour of academics in the lab (and in the wild) at a workshop at the Alan Turing Institute in London to discuss how we can harness the power of data science to make each stage of the scientific life cycle more efficient and effective. Here we summarise the key points that emerged from the workshop, and propose a framework for integrating data science techniques into every part of the research process (Figure 1). Statistical methods can optimise the power of an experiment by selecting which observations should be collected. Robotics and software pipelines can automate data collection and analysis, and incorporate machine learning analyses to adaptively update the experimental design based on incoming data. And the traditional output of research, a static PDF manuscript, can be enhanced to include analysis code and well-documented datasets to make the next iteration of the cycle faster and more efficient. We also highlight several of the challenges, both technical and social, that must be overcome to translate theory into practice, and share our vision for the laboratory of the future.

Figure 1

Download asset Open asset

Hypothesis-driven research usually requires a scientist to change an independent variable and measure a dependent variable. However, there are often too many parameters to take account of. In plant science, for instance, these parameters might include temperature, exposure to light, access to water and nutrients, humidity and so on, and the plant might respond to a change in each of these in a context-dependent way.

Data scientists interested in designing optimal experiments must find ways of transforming a scientific question into an optimisation problem. For instance, let us say that a scientist wants to fit a regression model of how temperature and light exposure influence wheat growth. Initially they might measure the height of the wheat at a number of combinations of temperature and light exposure. Then, the scientist could ask: what other combinations of temperature and light exposure should I grow the wheat at in order to improve my ability to predict wheat growth, considering the cost and time constraints of the project?

At the workshop Stefanie Biedermann (University of Southampton) discussed how to transform a wide range of experimental design questions into optimisation problems. She and her colleagues have applied these methods to find optimal ways of selecting parameters for studies of enzyme kinetics (Dette and Biedermann, 2003) and medical applications (Tompsett et al., 2018). Other researchers have used data science to increase the production of a drug while reducing unwanted by-products (Overstall et al., 2018). The process iteratively builds on small number of initial experiments that are conducted with different choices of experimental parameters (such as reagent concentrations, temperature or timing): new experimental parameters are then suggested until the optimal set are identified.

Optimal experimental design can also help researchers fit parameters of dynamical models, which can help them develop a mechanistic understanding of biological systems. Ozgur Akman (University of Exeter) focuses on dynamic models that can explain how gene expression changes over time and where the model parameters represent molecular properties, such as the transcription rates or mRNA degradation rates. As an example he explained how he had used this approach to find the optimal parameters for a mathematical model for the circadian clock (Aitken and Akman, 2013). Akman also described how it is possible to search for possible gene regulatory networks that can explain existing experimental data (Doherty, 2017), and then select new experiments to help distinguish between these alternative hypotheses (Sverchkov and Craven, 2017). For instance, the algorithm might suggest performing a certain gene knockout experiment, followed by RNA-seq, to gain more information about the network structure.

A clear message from the workshop was that statisticians need to be involved in the experimental design process as early as possible, rather than being asked to analyze the data at the end of a project. Involving statisticians before data collection makes it more likely the scientist will be able to answer the research questions they are interested in. Another clear message was that the data, software, infrastructure and the protocols generated during a research project were just as important as the results and interpretations that constitute a scientific paper.

In order to effectively plan an experiment, it is necessary to have some preliminary data as a starting point. Moreover, ensuring that the data collected during a particular experiment is used to inform the planning process for future experiments will make the whole process more efficient. For standard molecular biology experiments, this kind of feedback loop can be achieved through laboratory automation.

Ross King (University of Manchester) and co-workers have developed the first robot scientists – laboratory robots that physically perform experiments and use machine learning to generate hypothesis, plan experiments, and perform deductive reasoning to come to scientific conclusions. The first of these robots, Adam, successfully identified the yeast genes encoding orphan enzymes (King et al., 2009), and the second, Eve, intelligently screened drug targets for neglected tropical diseases (Williams et al., 2015). King is convinced that robot scientists improves research productivity, and also helps scientists to develop a better understanding of science as a process (King et al., 2018). For instance, an important step towards building these robotic scientists was the development of a formal language for describing scientific discoveries. Humans might enjoy reading about scientific discoveries that are described in English or some other human language, but such languages are subject to ambiguity and exaggeration. However, translating the deductive logic of research projects into the formal languages of "robotic scientists" should lead to a more precise description of our scientific conclusions (Sparkes et al., 2010).

Let us imagine that a research team observe that plants with a gene knockout are shorter than wild type plants. Their written report of the experiment will state that this gene knockout results in shorter plants. They are likely to leave unsaid the caveat that this result was only observed under their experimental set-up and, therefore, that this may not be the case under all possible experimental parameters. The mutant might be taller than a wild type plant under certain lighting conditions or temperatures that were not tested in the original study. In the future, researchers may be able to write their research outcomes in an unambiguous way so that it is clear that the evidence came from a very specific experimental set-up. The work done to communicate these conditions in a computer-readable format will benefit the human scientists who extend and replicate the original work.

Even though laboratory automation technology has existed for a number of years, it has yet to be widely incorporated into academic research environments. Laboratory automation is full of complex hardware that is difficult to use, but a few start-ups are beginning to build tools to help researchers communicate with their laboratory robots more effectively. Vishal Sanchania (Synthace) discussed how their software tool Antha enables scientists to easily develop workflows for controlling laboratory automation. Furthermore, these workflows can be iterative: that is, data collected by the laboratory robots can be used within the workflow to plan the next experimental procedure (Fell et al., 2018).

One benefit of having robotic platforms perform experiments as a service is that researchers are able to publish their experimental protocols as executable code, which any other researcher, from anywhere around the world, can run on another automated laboratory system, improving the reproducibility of experiments.

As the robot scientists (and their creators) realised, there is a lot more information that must be captured and shared for another researcher to reproduce an experiment. It is important that data collection and its analysis are reproducible. All too often, there is no way to verify the results in published papers because the reader does not have access to the data, nor to the information needed to repeat the same, often complex, analyses (Ioannidis et al., 2014). At our workshop Rachael Ainsworth (University of Manchester) highlighted Peng’s description of the reproducibility spectrum, which ranges from “publication only” to "full replication" with linked and executable code and data (Peng, 2011). Software engineering tools and techniques that are commonly applied in data science projects can nudge researchers towards the full replication end of the spectrum. These tools include interactive notebooks (Jupyter, Rmarkdown), version control and collaboration tools (git, GitHub, GitLab), package managers and containers to capture computational environments (Conda, Docker), and workflows to test and continuously integrate updates to the project (Travis CI). See Beaulieu-Jones and Greene, 2017 for an overview of how to repurpose these tools for scientific analyses.

Imaging has always been a critical technology for cell and developmental biology (Burel et al., 2015), ever since scientists looked at samples through a microscope and made drawings of what they saw. Photography came next, followed by digital image capture and analysis. Sébastien Besson (University of Dundee) presented a candidate for the next technology in this series, a set of open-source software and format standards called the Open Microscopy Environment (OME). This technology has already supported projects as diverse as the development a deep learning classifier to identify patients with clinical heart failure (Nirschl et al., 2018), to the generation of ultra-large high resolution electron microscopy maps in human, mouse and zebrafish tissue (Faas et al., 2012).

The OME project also subscribes to the philosophy that data must be FAIR: findable, accessible, interoperable and reusable (Wilkinson et al., 2016). It does this as follows: i) data are made findable by hosting them online and providing links to the papers the data have been used in; ii) data are made accessible through an open API (application programming interface) and the availability of highly curated metadata; iii) data are made interoperable via the Bio-Formats software, which allows more than 150 proprietary imaging file formats to be converted into a variety of open formats using a common vocabulary (Linkert et al., 2010); iv) data, software and other outputs are made reusable under permissive open licences or through "copyleft" licences which require the user to release anything they derive from the resource under the same open licence. (Alternatively, companies can pay for private access through Glencoe Software which provides a commercially licenced version of the OME suite of tools.).

Each group who upload data to be shared through OME's Image Data Resource can choose their own license for sharing their data, although they are strongly encouraged to use the most open of the creative commons licenses (CC-BY or CC0). When shared in this way, these resources open up new avenues for replication and verification studies, methods development, and exploratory work that leads to the generation of new hypotheses.

A hypothesis is essentially an "educated guess" by a researcher about what they think will happen when they do an experiment. A new hypothesis usually comes from theoretical models or from a desire to extend previously published experimental research. However, the traditional process of hypothesis generation is limited by the amount knowledge an individual researcher can hold in their head and the number of papers they can read each year, and it is also susceptible to their personal biases (van Helden, 2013).

In contrast, machine learning techniques such as text mining of published abstracts or electronic health records (Oquendo et al., 2012), or exploratory meta-analyses of datasets pooled from laboratories around the world, can be used for automated, reproducible and transparent hypothesis generation. For example, teams at IBM Research have mined 100,000 academic papers to identify new protein kinases that interact with a protein tumour suppressor (Spangler, 2014) and predicted hospital re-admissions from the electronic health records of 5,000 patients (Xiao et al., 2018). However, the availability of datasets, ideally datasets that are FAIR, is a prerequisite for automated hypothesis generation (Hall and Pesenti, 2017).

When we use data science techniques to design and analyse experiments, we need to ensure that the techniques we use are transparent and interpretable. And when we use robot scientists to design, perform and analyse experiments, we need to ensure that science continues to explore a broad range of scientific questions. Other challenges include avoiding positive feedback loops and algorithmic bias, equipping scientists with the skills they need to thrive in this new multidisciplinary environment, and ensuring that scientists in the global south are not left behind. We discuss all these points in more detail below.

1. 1. Abbott BP
   2. Abbott R
   3. Abbott TD
   4. Abernathy MR
   5. Acernese F
   6. Ackley K
   7. Adams C
   8. Adams T
   9. Addesso P
   10. Adhikari RX
   11. Adya VB
   12. Affeldt C
   13. Agathos M
   14. Agatsuma K
   15. Aggarwal N
   16. Aguiar OD
   17. Aiello L
   18. Ain A
   19. Ajith P
   20. Allen B
   21. Allocca A
   22. Altin PA
   23. Anderson SB
   24. Anderson WG
   25. Arai K
   26. Arain MA
   27. Araya MC
   28. Arceneaux CC
   29. Areeda JS
   30. Arnaud N
   31. Arun KG
   32. Ascenzi S
   33. Ashton G
   34. Ast M
   35. Aston SM
   36. Astone P
   37. Aufmuth P
   38. Aulbert C
   39. Babak S
   40. Bacon P
   41. Bader MK
   42. Baker PT
   43. Baldaccini F
   44. Ballardin G
   45. Ballmer SW
   46. Barayoga JC
   47. Barclay SE
   48. Barish BC
   49. Barker D
   50. Barone F
   51. Barr B
   52. Barsotti L
   53. Barsuglia M
   54. Barta D
   55. Bartlett J
   56. Barton MA
   57. Bartos I
   58. Bassiri R
   59. Basti A
   60. Batch JC
   61. Baune C
   62. Bavigadda V
   63. Bazzan M
   64. Behnke B
   65. Bejger M
   66. Belczynski C
   67. Bell AS
   68. Bell CJ
   69. Berger BK
   70. Bergman J
   71. Bergmann G
   72. Berry CP
   73. Bersanetti D
   74. Bertolini A
   75. Betzwieser J
   76. Bhagwat S
   77. Bhandare R
   78. Bilenko IA
   79. Billingsley G
   80. Birch J
   81. Birney R
   82. Birnholtz O
   83. Biscans S
   84. Bisht A
   85. Bitossi M
   86. Biwer C
   87. Bizouard MA
   88. Blackburn JK
   89. Blair CD
   90. Blair DG
   91. Blair RM
   92. Bloemen S
   93. Bock O
   94. Bodiya TP
   95. Boer M
   96. Bogaert G
   97. Bogan C
   98. Bohe A
   99. Bojtos P
   100. Bond C
   101. Bondu F
   102. Bonnand R
   103. Boom BA
   104. Bork R
   105. Boschi V
   106. Bose S
   107. Bouffanais Y
   108. Bozzi A
   109. Bradaschia C
   110. Brady PR
   111. Braginsky VB
   112. Branchesi M
   113. Brau JE
   114. Briant T
   115. Brillet A
   116. Brinkmann M
   117. Brisson V
   118. Brockill P
   119. Brooks AF
   120. Brown DA
   121. Brown DD
   122. Brown NM
   123. Buchanan CC
   124. Buikema A
   125. Bulik T
   126. Bulten HJ
   127. Buonanno A
   128. Buskulic D
   129. Buy C
   130. Byer RL
   131. Cabero M
   132. Cadonati L
   133. Cagnoli G
   134. Cahillane C
   135. Calderón Bustillo J
   136. Callister T
   137. Calloni E
   138. Camp JB
   139. Cannon KC
   140. Cao J
   141. Capano CD
   142. Capocasa E
   143. Carbognani F
   144. Caride S
   145. Casanueva Diaz J
   146. Casentini C
   147. Caudill S
   148. Cavaglià M
   149. Cavalier F
   150. Cavalieri R
   151. Cella G
   152. Cepeda CB
   153. Cerboni Baiardi L
   154. Cerretani G
   155. Cesarini E
   156. Chakraborty R
   157. Chalermsongsak T
   158. Chamberlin SJ
   159. Chan M
   160. Chao S
   161. Charlton P
   162. Chassande-Mottin E
   163. Chen HY
   164. Chen Y
   165. Cheng C
   166. Chincarini A
   167. Chiummo A
   168. Cho HS
   169. Cho M
   170. Chow JH
   171. Christensen N
   172. Chu Q
   173. Chua S
   174. Chung S
   175. Ciani G
   176. Clara F
   177. Clark JA
   178. Cleva F
   179. Coccia E
   180. Cohadon PF
   181. Colla A
   182. Collette CG
   183. Cominsky L
   184. Constancio M
   185. Conte A
   186. Conti L
   187. Cook D
   188. Corbitt TR
   189. Cornish N
   190. Corsi A
   191. Cortese S
   192. Costa CA
   193. Coughlin MW
   194. Coughlin SB
   195. Coulon JP
   196. Countryman ST
   197. Couvares P
   198. Cowan EE
   199. Coward DM
   200. Cowart MJ
   201. Coyne DC
   202. Coyne R
   203. Craig K
   204. Creighton JD
   205. Creighton TD
   206. Cripe J
   207. Crowder SG
   208. Cruise AM
   209. Cumming A
   210. Cunningham L
   211. Cuoco E
   212. Dal Canton T
   213. Danilishin SL
   214. D'Antonio S
   215. Danzmann K
   216. Darman NS
   217. Da Silva Costa CF
   218. Dattilo V
   219. Dave I
   220. Daveloza HP
   221. Davier M
   222. Davies GS
   223. Daw EJ
   224. Day R
   225. De S
   226. DeBra D
   227. Debreczeni G
   228. Degallaix J
   229. De Laurentis M
   230. Deléglise S
   231. Del Pozzo W
   232. Denker T
   233. Dent T
   234. Dereli H
   235. Dergachev V
   236. DeRosa RT
   237. De Rosa R
   238. DeSalvo R
   239. Dhurandhar S
   240. Díaz MC
   241. Di Fiore L
   242. Di Giovanni M
   243. Di Lieto A
   244. Di Pace S
   245. Di Palma I
   246. Di Virgilio A
   247. Dojcinoski G
   248. Dolique V
   249. Donovan F
   250. Dooley KL
   251. Doravari S
   252. Douglas R
   253. Downes TP
   254. Drago M
   255. Drever RW
   256. Driggers JC
   257. Du Z
   258. Ducrot M
   259. Dwyer SE
   260. Edo TB
   261. Edwards MC
   262. Effler A
   263. Eggenstein HB
   264. Ehrens P
   265. Eichholz J
   266. Eikenberry SS
   267. Engels W
   268. Essick RC
   269. Etzel T
   270. Evans M
   271. Evans TM
   272. Everett R
   273. Factourovich M
   274. Fafone V
   275. Fair H
   276. Fairhurst S
   277. Fan X
   278. Fang Q
   279. Farinon S
   280. Farr B
   281. Farr WM
   282. Favata M
   283. Fays M
   284. Fehrmann H
   285. Fejer MM
   286. Feldbaum D
   287. Ferrante I
   288. Ferreira EC
   289. Ferrini F
   290. Fidecaro F
   291. Finn LS
   292. Fiori I
   293. Fiorucci D
   294. Fisher RP
   295. Flaminio R
   296. Fletcher M
   297. Fong H
   298. Fournier JD
   299. Franco S
   300. Frasca S
   301. Frasconi F
   302. Frede M
   303. Frei Z
   304. Freise A
   305. Frey R
   306. Frey V
   307. Fricke TT
   308. Fritschel P
   309. Frolov VV
   310. Fulda P
   311. Fyffe M
   312. Gabbard HA
   313. Gair JR
   314. Gammaitoni L
   315. Gaonkar SG
   316. Garufi F
   317. Gatto A
   318. Gaur G
   319. Gehrels N
   320. Gemme G
   321. Gendre B
   322. Genin E
   323. Gennai A
   324. George J
   325. Gergely L
   326. Germain V
   327. Ghosh A
   328. Ghosh A
   329. Ghosh S
   330. Giaime JA
   331. Giardina KD
   332. Giazotto A
   333. Gill K
   334. Glaefke A
   335. Gleason JR
   336. Goetz E
   337. Goetz R
   338. Gondan L
   339. González G
   340. Gonzalez Castro JM
   341. Gopakumar A
   342. Gordon NA
   343. Gorodetsky ML
   344. Gossan SE
   345. Gosselin M
   346. Gouaty R
   347. Graef C
   348. Graff PB
   349. Granata M
   350. Grant A
   351. Gras S
   352. Gray C
   353. Greco G
   354. Green AC
   355. Greenhalgh RJ
   356. Groot P
   357. Grote H
   358. Grunewald S
   359. Guidi GM
   360. Guo X
   361. Gupta A
   362. Gupta MK
   363. Gushwa KE
   364. Gustafson EK
   365. Gustafson R
   366. Hacker JJ
   367. Hall BR
   368. Hall ED
   369. Hammond G
   370. Haney M
   371. Hanke MM
   372. Hanks J
   373. Hanna C
   374. Hannam MD
   375. Hanson J
   376. Hardwick T
   377. Harms J
   378. Harry GM
   379. Harry IW
   380. Hart MJ
   381. Hartman MT
   382. Haster CJ
   383. Haughian K
   384. Healy J
   385. Heefner J
   386. Heidmann A
   387. Heintze MC
   388. Heinzel G
   389. Heitmann H
   390. Hello P
   391. Hemming G
   392. Hendry M
   393. Heng IS
   394. Hennig J
   395. Heptonstall AW
   396. Heurs M
   397. Hild S
   398. Hoak D
   399. Hodge KA
   400. Hofman D
   401. Hollitt SE
   402. Holt K
   403. Holz DE
   404. Hopkins P
   405. Hosken DJ
   406. Hough J
   407. Houston EA
   408. Howell EJ
   409. Hu YM
   410. Huang S
   411. Huerta EA
   412. Huet D
   413. Hughey B
   414. Husa S
   415. Huttner SH
   416. Huynh-Dinh T
   417. Idrisy A
   418. Indik N
   419. Ingram DR
   420. Inta R
   421. Isa HN
   422. Isac JM
   423. Isi M
   424. Islas G
   425. Isogai T
   426. Iyer BR
   427. Izumi K
   428. Jacobson MB
   429. Jacqmin T
   430. Jang H
   431. Jani K
   432. Jaranowski P
   433. Jawahar S
   434. Jiménez-Forteza F
   435. Johnson WW
   436. Johnson-McDaniel NK
   437. Jones DI
   438. Jones R
   439. Jonker RJ
   440. Ju L
   441. Haris K
   442. Kalaghatgi CV
   443. Kalogera V
   444. Kandhasamy S
   445. Kang G
   446. Kanner JB
   447. Karki S
   448. Kasprzack M
   449. Katsavounidis E
   450. Katzman W
   451. Kaufer S
   452. Kaur T
   453. Kawabe K
   454. Kawazoe F
   455. Kéfélian F
   456. Kehl MS
   457. Keitel D
   458. Kelley DB
   459. Kells W
   460. Kennedy R
   461. Keppel DG
   462. Key JS
   463. Khalaidovski A
   464. Khalili FY
   465. Khan I
   466. Khan S
   467. Khan Z
   468. Khazanov EA
   469. Kijbunchoo N
   470. Kim C
   471. Kim J
   472. Kim K
   473. Kim NG
   474. Kim N
   475. Kim YM
   476. King EJ
   477. King PJ
   478. Kinzel DL
   479. Kissel JS
   480. Kleybolte L
   481. Klimenko S
   482. Koehlenbeck SM
   483. Kokeyama K
   484. Koley S
   485. Kondrashov V
   486. Kontos A
   487. Koranda S
   488. Korobko M
   489. Korth WZ
   490. Kowalska I
   491. Kozak DB
   492. Kringel V
   493. Krishnan B
   494. Królak A
   495. Krueger C
   496. Kuehn G
   497. Kumar P
   498. Kumar R
   499. Kuo L
   500. Kutynia A
   501. Kwee P
   502. Lackey BD
   503. Landry M
   504. Lange J
   505. Lantz B
   506. Lasky PD
   507. Lazzarini A
   508. Lazzaro C
   509. Leaci P
   510. Leavey S
   511. Lebigot EO
   512. Lee CH
   513. Lee HK
   514. Lee HM
   515. Lee K
   516. Lenon A
   517. Leonardi M
   518. Leong JR
   519. Leroy N
   520. Letendre N
   521. Levin Y
   522. Levine BM
   523. Li TG
   524. Libson A
   525. Littenberg TB
   526. Lockerbie NA
   527. Logue J
   528. Lombardi AL
   529. London LT
   530. Lord JE
   531. Lorenzini M
   532. Loriette V
   533. Lormand M
   534. Losurdo G
   535. Lough JD
   536. Lousto CO
   537. Lovelace G
   538. Lück H
   539. Lundgren AP
   540. Luo J
   541. Lynch R
   542. Ma Y
   543. MacDonald T
   544. Machenschalk B
   545. MacInnis M
   546. Macleod DM
   547. Magaña-Sandoval F
   548. Magee RM
   549. Mageswaran M
   550. Majorana E
   551. Maksimovic I
   552. Malvezzi V
   553. Man N
   554. Mandel I
   555. Mandic V
   556. Mangano V
   557. Mansell GL
   558. Manske M
   559. Mantovani M
   560. Marchesoni F
   561. Marion F
   562. Márka S
   563. Márka Z
   564. Markosyan AS
   565. Maros E
   566. Martelli F
   567. Martellini L
   568. Martin IW
   569. Martin RM
   570. Martynov DV
   571. Marx JN
   572. Mason K
   573. Masserot A
   574. Massinger TJ
   575. Masso-Reid M
   576. Matichard F
   577. Matone L
   578. Mavalvala N
   579. Mazumder N
   580. Mazzolo G
   581. McCarthy R
   582. McClelland DE
   583. McCormick S
   584. McGuire SC
   585. McIntyre G
   586. McIver J
   587. McManus DJ
   588. McWilliams ST
   589. Meacher D
   590. Meadors GD
   591. Meidam J
   592. Melatos A
   593. Mendell G
   594. Mendoza-Gandara D
   595. Mercer RA
   596. Merilh E
   597. Merzougui M
   598. Meshkov S
   599. Messenger C
   600. Messick C
   601. Meyers PM
   602. Mezzani F
   603. Miao H
   604. Michel C
   605. Middleton H
   606. Mikhailov EE
   607. Milano L
   608. Miller J
   609. Millhouse M
   610. Minenkov Y
   611. Ming J
   612. Mirshekari S
   613. Mishra C
   614. Mitra S
   615. Mitrofanov VP
   616. Mitselmakher G
   617. Mittleman R
   618. Moggi A
   619. Mohan M
   620. Mohapatra SR
   621. Montani M
   622. Moore BC
   623. Moore CJ
   624. Moraru D
   625. Moreno G
   626. Morriss SR
   627. Mossavi K
   628. Mours B
   629. Mow-Lowry CM
   630. Mueller CL
   631. Mueller G
   632. Muir AW
   633. Mukherjee A
   634. Mukherjee D
   635. Mukherjee S
   636. Mukund N
   637. Mullavey A
   638. Munch J
   639. Murphy DJ
   640. Murray PG
   641. Mytidis A
   642. Nardecchia I
   643. Naticchioni L
   644. Nayak RK
   645. Necula V
   646. Nedkova K
   647. Nelemans G
   648. Neri M
   649. Neunzert A
   650. Newton G
   651. Nguyen TT
   652. Nielsen AB
   653. Nissanke S
   654. Nitz A
   655. Nocera F
   656. Nolting D
   657. Normandin ME
   658. Nuttall LK
   659. Oberling J
   660. Ochsner E
   661. O'Dell J
   662. Oelker E
   663. Ogin GH
   664. Oh JJ
   665. Oh SH
   666. Ohme F
   667. Oliver M
   668. Oppermann P
   669. Oram RJ
   670. O'Reilly B
   671. O'Shaughnessy R
   672. Ott CD
   673. Ottaway DJ
   674. Ottens RS
   675. Overmier H
   676. Owen BJ
   677. Pai A
   678. Pai SA
   679. Palamos JR
   680. Palashov O
   681. Palomba C
   682. Pal-Singh A
   683. Pan H
   684. Pan Y
   685. Pankow C
   686. Pannarale F
   687. Pant BC
   688. Paoletti F
   689. Paoli A
   690. Papa MA
   691. Paris HR
   692. Parker W
   693. Pascucci D
   694. Pasqualetti A
   695. Passaquieti R
   696. Passuello D
   697. Patricelli B
   698. Patrick Z
   699. Pearlstone BL
   700. Pedraza M
   701. Pedurand R
   702. Pekowsky L
   703. Pele A
   704. Penn S
   705. Perreca A
   706. Pfeiffer HP
   707. Phelps M
   708. Piccinni O
   709. Pichot M
   710. Pickenpack M
   711. Piergiovanni F
   712. Pierro V
   713. Pillant G
   714. Pinard L
   715. Pinto IM
   716. Pitkin M
   717. Poeld JH
   718. Poggiani R
   719. Popolizio P
   720. Post A
   721. Powell J
   722. Prasad J
   723. Predoi V
   724. Premachandra SS
   725. Prestegard T
   726. Price LR
   727. Prijatelj M
   728. Principe M
   729. Privitera S
   730. Prix R
   731. Prodi GA
   732. Prokhorov L
   733. Puncken O
   734. Punturo M
   735. Puppo P
   736. Pürrer M
   737. Qi H
   738. Qin J
   739. Quetschke V
   740. Quintero EA
   741. Quitzow-James R
   742. Raab FJ
   743. Rabeling DS
   744. Radkins H
   745. Raffai P
   746. Raja S
   747. Rakhmanov M
   748. Ramet CR
   749. Rapagnani P
   750. Raymond V
   751. Razzano M
   752. Re V
   753. Read J
   754. Reed CM
   755. Regimbau T
   756. Rei L
   757. Reid S
   758. Reitze DH
   759. Rew H
   760. Reyes SD
   761. Ricci F
   762. Riles K
   763. Robertson NA
   764. Robie R
   765. Robinet F
   766. Rocchi A
   767. Rolland L
   768. Rollins JG
   769. Roma VJ
   770. Romano JD
   771. Romano R
   772. Romanov G
   773. Romie JH
   774. Rosińska D
   775. Rowan S
   776. Rüdiger A
   777. Ruggi P
   778. Ryan K
   779. Sachdev S
   780. Sadecki T
   781. Sadeghian L
   782. Salconi L
   783. Saleem M
   784. Salemi F
   785. Samajdar A
   786. Sammut L
   787. Sampson LM
   788. Sanchez EJ
   789. Sandberg V
   790. Sandeen B
   791. Sanders GH
   792. Sanders JR
   793. Sassolas B
   794. Sathyaprakash BS
   795. Saulson PR
   796. Sauter O
   797. Savage RL
   798. Sawadsky A
   799. Schale P
   800. Schilling R
   801. Schmidt J
   802. Schmidt P
   803. Schnabel R
   804. Schofield RM
   805. Schönbeck A
   806. Schreiber E
   807. Schuette D
   808. Schutz BF
   809. Scott J
   810. Scott SM
   811. Sellers D
   812. Sengupta AS
   813. Sentenac D
   814. Sequino V
   815. Sergeev A
   816. Serna G
   817. Setyawati Y
   818. Sevigny A
   819. Shaddock DA
   820. Shaffer T
   821. Shah S
   822. Shahriar MS
   823. Shaltev M
   824. Shao Z
   825. Shapiro B
   826. Shawhan P
   827. Sheperd A
   828. Shoemaker DH
   829. Shoemaker DM
   830. Siellez K
   831. Siemens X
   832. Sigg D
   833. Silva AD
   834. Simakov D
   835. Singer A
   836. Singer LP
   837. Singh A
   838. Singh R
   839. Singhal A
   840. Sintes AM
   841. Slagmolen BJ
   842. Smith JR
   843. Smith MR
   844. Smith ND
   845. Smith RJ
   846. Son EJ
   847. Sorazu B
   848. Sorrentino F
   849. Souradeep T
   850. Srivastava AK
   851. Staley A
   852. Steinke M
   853. Steinlechner J
   854. Steinlechner S
   855. Steinmeyer D
   856. Stephens BC
   857. Stevenson SP
   858. Stone R
   859. Strain KA
   860. Straniero N
   861. Stratta G
   862. Strauss NA
   863. Strigin S
   864. Sturani R
   865. Stuver AL
   866. Summerscales TZ
   867. Sun L
   868. Sutton PJ
   869. Swinkels BL
   870. Szczepańczyk MJ
   871. Tacca M
   872. Talukder D
   873. Tanner DB
   874. Tápai M
   875. Tarabrin SP
   876. Taracchini A
   877. Taylor R
   878. Theeg T
   879. Thirugnanasambandam MP
   880. Thomas EG
   881. Thomas M
   882. Thomas P
   883. Thorne KA
   884. Thorne KS
   885. Thrane E
   886. Tiwari S
   887. Tiwari V
   888. Tokmakov KV
   889. Tomlinson C
   890. Tonelli M
   891. Torres CV
   892. Torrie CI
   893. Töyrä D
   894. Travasso F
   895. Traylor G
   896. Trifirò D
   897. Tringali MC
   898. Trozzo L
   899. Tse M
   900. Turconi M
   901. Tuyenbayev D
   902. Ugolini D
   903. Unnikrishnan CS
   904. Urban AL
   905. Usman SA
   906. Vahlbruch H
   907. Vajente G
   908. Valdes G
   909. Vallisneri M
   910. van Bakel N
   911. van Beuzekom M
   912. van den Brand JF
   913. Van Den Broeck C
   914. Vander-Hyde DC
   915. van der Schaaf L
   916. van Heijningen JV
   917. van Veggel AA
   918. Vardaro M
   919. Vass S
   920. Vasúth M
   921. Vaulin R
   922. Vecchio A
   923. Vedovato G
   924. Veitch J
   925. Veitch PJ
   926. Venkateswara K
   927. Verkindt D
   928. Vetrano F
   929. Viceré A
   930. Vinciguerra S
   931. Vine DJ
   932. Vinet JY
   933. Vitale S
   934. Vo T
   935. Vocca H
   936. Vorvick C
   937. Voss D
   938. Vousden WD
   939. Vyatchanin SP
   940. Wade AR
   941. Wade LE
   942. Wade M
   943. Waldman SJ
   944. Walker M
   945. Wallace L
   946. Walsh S
   947. Wang G
   948. Wang H
   949. Wang M
   950. Wang X
   951. Wang Y
   952. Ward H
   953. Ward RL
   954. Warner J
   955. Was M
   956. Weaver B
   957. Wei LW
   958. Weinert M
   959. Weinstein AJ
   960. Weiss R
   961. Welborn T
   962. Wen L
   963. Weßels P
   964. Westphal T
   965. Wette K
   966. Whelan JT
   967. Whitcomb SE
   968. White DJ
   969. Whiting BF
   970. Wiesner K
   971. Wilkinson C
   972. Willems PA
   973. Williams L
   974. Williams RD
   975. Williamson AR
   976. Willis JL
   977. Willke B
   978. Wimmer MH
   979. Winkelmann L
   980. Winkler W
   981. Wipf CC
   982. Wiseman AG
   983. Wittel H
   984. Woan G
   985. Worden J
   986. Wright JL
   987. Wu G
   988. Yablon J
   989. Yakushin I
   990. Yam W
   991. Yamamoto H
   992. Yancey CC
   993. Yap MJ
   994. Yu H
   995. Yvert M
   996. Zadrożny A
   997. Zangrando L
   998. Zanolin M
   999. Zendri JP
   1000. Zevin M
   1001. Zhang F
   1002. Zhang L
   1003. Zhang M
   1004. Zhang Y
   1005. Zhao C
   1006. Zhou M
   1007. Zhou Z
   1008. Zhu XJ
   1009. Zucker ME
   1010. Zuraw SE
   1011. Zweizig J
   1012. LIGO Scientific Collaboration and Virgo Collaboration

   (2016) Observation of gravitational waves from a binary black hole merger

   Physical Review Letters 116:061102.

   https://doi.org/10.1103/PhysRevLett.116.061102

   • PubMed
   • Google Scholar
2. Conference

   1. Ainsworth R

   (2018) Reproducibility and open science

   Data Science for Experimental Design (DSED).

   https://doi.org/10.5281/zenodo.1464853

   • Google Scholar
3. 1. Aitken S
   2. Akman OE

   (2013) Nested sampling for parameter inference in systems biology: application to an exemplar circadian model

   BMC Systems Biology 7:72.

   https://doi.org/10.1186/1752-0509-7-72

   • PubMed
   • Google Scholar
4. 1. Angermueller C
   2. Pärnamaa T
   3. Parts L
   4. Stegle O

   (2016) Deep learning for computational biology

   Molecular Systems Biology 12:878.

   https://doi.org/10.15252/msb.20156651

   • PubMed
   • Google Scholar
5. Website

   1. Beaulieu-Jones B
   2. Greene C

   (2017) Reproducibility: automated

   Accessed February 26, 2019.

   https://elifesciences.org/labs/e623676c/reproducibility-automated
6. 1. Bezuidenhout L
   2. Kelly AH
   3. Leonelli S
   4. Rappert B

   (2017) ‘$100 Is Not Much To You’: Open Science and neglected accessibilities for scientific research in Africa

   Critical Public Health 27:39–49.

   https://doi.org/10.1080/09581596.2016.1252832

   • Google Scholar
7. Website

   1. Buolamwini J
   2. Gebru T

   (2018) Gender shades: intersectional accuracy disparities in commercial gender classification (PMLR 81:77-91)

   Accessed February 26, 2019.

   http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf
8. 1. Burel JM
   2. Besson S
   3. Blackburn C
   4. Carroll M
   5. Ferguson RK
   6. Flynn H
   7. Gillen K
   8. Leigh R
   9. Li S
   10. Lindner D
   11. Linkert M
   12. Moore WJ
   13. Ramalingam B
   14. Rozbicki E
   15. Tarkowska A
   16. Walczysko P
   17. Allan C
   18. Moore J
   19. Swedlow JR

   (2015) Publishing and sharing multi-dimensional image data with OMERO

   Mammalian Genome 26:441–447.

   https://doi.org/10.1007/s00335-015-9587-6

   • PubMed
   • Google Scholar
9. 1. Dette H
   2. Biedermann S

   (2003) Robust and efficient designs for the Michaelis–Menten model

   Journal of the American Statistical Association 98:679–686.

   https://doi.org/10.1198/016214503000000585

   • Google Scholar
10. Conference

    1. Doherty K

    (2017) Optimisation and landscape analysis of computational biology models: a case study

    In: Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '17). pp. 1644–1651.

    https://doi.org/10.1145/3067695.3084609

    • Google Scholar
11. 1. Extance A

    (2018) How AI technology can tame the scientific literature

    Nature 561:273–274.

    https://doi.org/10.1038/d41586-018-06617-5

    • PubMed
    • Google Scholar
12. Preprint

    1. Ezer D
    2. Keir JC

    (2018) Selection of time points for costly experiments: a comparison between human intuition and computer-aided experimental design

    bioRxiv.

    https://doi.org/10.1101/301796

    • Google Scholar
13. 1. Faas FG
    2. Avramut MC
    3. van den Berg BM
    4. Mommaas AM
    5. Koster AJ
    6. Ravelli RB

    (2012) Virtual nanoscopy: generation of ultra-large high resolution electron microscopy maps

    Journal of Cell Biology 198:457–469.

    https://doi.org/10.1083/jcb.201201140

    • PubMed
    • Google Scholar
14. Website

    1. Fell T
    2. Ward S
    3. Gershater M
    4. Watson M
    5. Crane P
    6. Wiederhold R

    (2018) Computer-Aided biology

    Accessed February 26, 2019.

    https://static1.squarespace.com/static/5af46322620b851d41f3f64f/t/5bb1d987e5e5f08a8c7fb24a/1538383791006/Computer_Aided_Biology_Synthace_10_18.pdf
15. Conference

    1. Hajian S
    2. Bonchi F
    3. Castillo C

    (2016) Algorithmic bias: from discrimination discovery to Fairness-Aware data mining part 1 & 2

    Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

    https://doi.org/10.1145/2939672.2945386

    • Google Scholar
16. Website

    1. Hall W
    2. Pesenti J

    (2017) Growing the artificial intelligence industry in the UK

    Accessed February 26, 2019.

    https://www.gov.uk/government/publications/growing-the-artificial-intelligence-industry-in-the-uk
17. 1. Ioannidis JP
    2. Munafò MR
    3. Fusar-Poli P
    4. Nosek BA
    5. David SP

    (2014) Publication and other reporting biases in cognitive sciences: detection, prevalence, and prevention

    Trends in Cognitive Sciences 18:235–241.

    https://doi.org/10.1016/j.tics.2014.02.010

    • PubMed
    • Google Scholar
18. Book

    1. Kasparov G

    (2017)

    Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins

    London: John Murray.

    • Google Scholar
19. Preprint

    1. Keshavan A
    2. Yeatman J
    3. Rokem A

    (2018) Combining citizen science and deep learning to amplify expertise in neuroimaging

    bioRxiv.

    https://doi.org/10.1101/363382

    • Google Scholar
20. 1. King RD
    2. Rowland J
    3. Aubrey W
    4. Liakata M
    5. Markham M
    6. Soldatova LN
    7. Whelan KE
    8. Clare A
    9. Young M
    10. Sparkes A
    11. Oliver SG
    12. Pir P

    (2009) The robot scientist Adam

    Computer 42:46–54.

    https://doi.org/10.1109/MC.2009.270

    • Google Scholar
21. 1. King RD
    2. Schuler Costa V
    3. Mellingwood C
    4. Soldatova LN

    (2018) Automating sciences: philosophical and social dimensions

    IEEE Technology and Society Magazine 37:40–46.

    https://doi.org/10.1109/MTS.2018.2795097

    • Google Scholar
22. 1. Kleyman M
    2. Sefer E
    3. Nicola T
    4. Espinoza C
    5. Chhabra D
    6. Hagood JS
    7. Kaminski N
    8. Ambalavanan N
    9. Bar-Joseph Z

    (2017) Selecting the most appropriate time points to profile in high-throughput studies

    eLife 6:e18541.

    https://doi.org/10.7554/eLife.18541

    • PubMed
    • Google Scholar
23. 1. Kramer B
    2. Bosman J

    (2018) Rainbow of open science practices

    Zenodo.

    https://doi.org/10.5281/zenodo.1147025

    • Google Scholar
24. Book

    1. Krizhevsky A
    2. Sutskever I
    3. Hinton GE

    (2012)

    ImageNet Classification with Deep Convolutional Neural Network

    In: Weinberger K. Q, Pereira F, Burges C. J. C, Bottou L, editors. Advances in Neural Information Processing Systems, 25.  Curran Associates, Inc. pp. 1097–1105.

    • Google Scholar
25. 1. Libbrecht MW
    2. Noble WS

    (2015) Machine learning applications in genetics and genomics

    Nature Reviews Genetics 16:321–332.

    https://doi.org/10.1038/nrg3920

    • PubMed
    • Google Scholar
26. 1. Linkert M
    2. Rueden CT
    3. Allan C
    4. Burel JM
    5. Moore W
    6. Patterson A
    7. Loranger B
    8. Moore J
    9. Neves C
    10. Macdonald D
    11. Tarkowska A
    12. Sticco C
    13. Hill E
    14. Rossner M
    15. Eliceiri KW
    16. Swedlow JR

    (2010) Metadata matters: access to image data in the real world

    Journal of Cell Biology 189:777–782.

    https://doi.org/10.1083/jcb.201004104

    • PubMed
    • Google Scholar
27. 1. Markowetz F

    (2015) Five selfish reasons to work reproducibly

    Genome Biology 16:274.

    https://doi.org/10.1186/s13059-015-0850-7

    • PubMed
    • Google Scholar
28. Website

    1. Mellingwood C

    (2017) What about the frogs?: reflections on 'Community and Identity in the Techno-Sciences' workshop

    Accessed February 26, 2019.

    https://blogs.sps.ed.ac.uk/engineering-life/2017/03/30/what-about-the-frogs-reflections-on-community-and-identity-in-the-techno-sciences-workshop/
29. 1. Nirschl JJ
    2. Janowczyk A
    3. Peyster EG
    4. Frank R
    5. Margulies KB
    6. Feldman MD
    7. Madabhushi A

    (2018) A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue

    PloS One 13:e0192726.

    https://doi.org/10.1371/journal.pone.0192726

    • PubMed
    • Google Scholar
30. 1. Oquendo MA
    2. Baca-Garcia E
    3. Artés-Rodríguez A
    4. Perez-Cruz F
    5. Galfalvy HC
    6. Blasco-Fontecilla H
    7. Madigan D
    8. Duan N

    (2012) Machine learning and data mining: strategies for hypothesis generation

    Molecular Psychiatry 17:956–959.

    https://doi.org/10.1038/mp.2011.173

    • PubMed
    • Google Scholar
31. Preprint

    1. Overstall A
    2. Woods D
    3. Adamou M

    (2017) Acebayes: an R package for bayesian optimal design of experiments via approximate coordinate exchange

    arXiv.

    https://arxiv.org/abs/1705.08096

    • Google Scholar
32. Website

    1. Overstall A
    2. Woods D
    3. Martin KJ

    (2018) Bayesian prediction for physical models with application to the optimization of the synthesis of pharmaceutical products using chemical kinetics computational statistics & data analysis

    Accessed February 26, 2019.

    https://eprints.soton.ac.uk/425529/
33. 1. Peng RD

    (2011) Reproducible research in computational science

    Science 334:1226–1227.

    https://doi.org/10.1126/science.1213847

    • PubMed
    • Google Scholar
34. Website

    1. Snow J

    (2017) Amazon's face recognition falsely matched 28 members of congress with mugshots

    Accessed February 26, 2019.

    https://www.aclu.org/blog/privacy-technology/surveillance-technologies/amazons-face-recognition-falsely-matched-28
35. Conference

    1. Spangler S

    (2014) Automated hypothesis generation based on mining scientific literature

    In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

    https://doi.org/10.1145/2623330.2623667

    • Google Scholar
36. 1. Sparkes A
    2. Aubrey W
    3. Byrne E
    4. Clare A
    5. Khan MN
    6. Liakata M
    7. Markham M
    8. Rowland J
    9. Soldatova LN
    10. Whelan KE
    11. Young M
    12. King RD

    (2010) Towards robot scientists for autonomous scientific discovery

    Automated Experimentation 2:1.

    https://doi.org/10.1186/1759-4499-2-1

    • PubMed
    • Google Scholar
37. 1. Stoeger T
    2. Gerlach M
    3. Morimoto RI
    4. Nunes Amaral LA

    (2018) Large-scale investigation of the reasons why potentially important genes are ignored

    PLOS Biology 16:e2006643.

    https://doi.org/10.1371/journal.pbio.2006643

    • PubMed
    • Google Scholar
38. 1. Sverchkov Y
    2. Craven M

    (2017) A review of active learning approaches to experimental design for uncovering biological networks

    PLOS Computational Biology 13:e1005466.

    https://doi.org/10.1371/journal.pcbi.1005466

    • PubMed
    • Google Scholar
39. 1. Tompsett DM
    2. Biedermann S
    3. Liu W

    (2018) Simultaneous confidence sets for several effective doses

    Biometrical Journal 60:703–720.

    https://doi.org/10.1002/bimj.201700161

    • PubMed
    • Google Scholar
40. 1. van Helden P

    (2013) Data-driven hypotheses

    EMBO Reports 14:104.

    https://doi.org/10.1038/embor.2012.207

    • PubMed
    • Google Scholar
41. 1. Wilkinson MD
    2. Dumontier M
    3. Aalbersberg IJ
    4. Appleton G
    5. Axton M
    6. Baak A
    7. Blomberg N
    8. Boiten JW
    9. da Silva Santos LB
    10. Bourne PE
    11. Bouwman J
    12. Brookes AJ
    13. Clark T
    14. Crosas M
    15. Dillo I
    16. Dumon O
    17. Edmunds S
    18. Evelo CT
    19. Finkers R
    20. Gonzalez-Beltran A
    21. Gray AJ
    22. Groth P
    23. Goble C
    24. Grethe JS
    25. Heringa J
    26. 't Hoen PA
    27. Hooft R
    28. Kuhn T
    29. Kok R
    30. Kok J
    31. Lusher SJ
    32. Martone ME
    33. Mons A
    34. Packer AL
    35. Persson B
    36. Rocca-Serra P
    37. Roos M
    38. van Schaik R
    39. Sansone SA
    40. Schultes E
    41. Sengstag T
    42. Slater T
    43. Strawn G
    44. Swertz MA
    45. Thompson M
    46. van der Lei J
    47. van Mulligen E
    48. Velterop J
    49. Waagmeester A
    50. Wittenburg P
    51. Wolstencroft K
    52. Zhao J
    53. Mons B

    (2016) The FAIR guiding principles for scientific data management and stewardship

    Scientific Data 3:160018.

    https://doi.org/10.1038/sdata.2016.18

    • PubMed
    • Google Scholar
42. 1. Williams K
    2. Bilsland E
    3. Sparkes A
    4. Aubrey W
    5. Young M
    6. Soldatova LN
    7. De Grave K
    8. Ramon J
    9. de Clare M
    10. Sirawaraporn W
    11. Oliver SG
    12. King RD

    (2015) Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases

    Journal of the Royal Society Interface 12:20141289.

    https://doi.org/10.1098/rsif.2014.1289

    • PubMed
    • Google Scholar
43. 1. Xiao C
    2. Ma T
    3. Dieng AB
    4. Blei DM
    5. Wang F

    (2018) Readmission prediction via deep contextual embedding of clinical concepts

    PLOS ONE 13:e0195024.

    https://doi.org/10.1371/journal.pone.0195024

    • Google Scholar

Author details

1. Daphne Ezer

   Daphne Ezer is at the Alan Turing Institute, London, and in the Department of Statistics, University of Warwick, Coventry, United Kingdom

   Contribution

   Conceptualization, Writing—original draft, Writing—review and editing

   Contributed equally with

   Kirstie Whitaker

   For correspondence

   dezer@turing.ac.uk

   Competing interests

   No competing interests declared

   Additional information

   Corresponding author

   "This ORCID iD identifies the author of this article:" 0000-0002-1685-6909

2. Kirstie Whitaker

   Kirstie Whitaker is at the Alan Turing Institute, London, and in the Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Conceptualization, Writing—original draft, Writing—review and editing

   Contributed equally with

   Daphne Ezer

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8498-4059

• 5,076

  views

• 491

  downloads

• 11

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.