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Sampling and dataThe data consist of gillnet catches of brown trout (N = 5733, caught during 2008–2009) from 21 lakes situated along an altitudinal gradient (30 m above sea level, m.a.s.l.-800 m.a.s.l.) in mid-Norway and Sweden (Fig. 5). The lakes were sampled within three main types of vegetation zonation in the catchment area that ranged from the southern boreal to the alpine zone. The lowland lakes were situated in the southern boreal zone dominated by coniferous woodland and forest, but there were also large areas of alder (Alnus sp.) as well as some broad-leaved deciduous woodland. Average annual and July air temperature are 4–6 and 12–16 °C, respectively46. Middle boreal catchment area is dominated by coniferous woodland, forest and mires. Average annual and July air temperature are, respectively, 2–4 and 8–12°C46. Vegetation around the high altitude lakes were dominated by bilberry (Vaccinium myrtillus), grass heaths and dwarf birch (Betula nana) scrub, with annual and July air temperature of − 2 to 0 and 6–12°C46. The clustering of lakes within vegetations zones can be seen in Fig. 5. The epilimnetic water temperature across a sample of the lakes in the altitudinal gradient in this study seems to be within the general trends in the air temperature47.Figure 5Study lake positions (filled dots) and names. Unfilled large circles connects the different lakes with the most representative weather stations (stars) in the area (in terms of altitude, vegetation zones and landscape). The dashed line constitute the national border between Norway and Sweden. The figure was produced using Adobe illustrator.Full size imageAll lakes were sampled using standardised gillnet series consisting of single mesh nets (25 × 1.5 m) with mesh sizes 12.5, 16, 19.5, 24, 29 and 35 mm47. Three nets were linked together making chains with alternating mesh sizes in order to represent all mesh sizes at different depths in each lake at each sampling. This gillnet series catches brown trout with a slight bias in favour of larger individuals48 that was assumed similar in all lakes. The nets were distributed along the shoreline, and the lakes were fished during summer, with different effort (i.e., number of gillnet series) depending on lake size. Weight per unit catch effort (CPUE) based on total weight of the brown trout catch per 100 m2 gillnet area per night was used as a proxy for biomass density. Since, differences in environmental conditions across lakes cause large variations in body size and hence per capita resource demands, biomass was considered a better measure of population density than number of individuals for among-lake comparisons. Length (total length, mm) and weight (g) at catch were measured for every individual in the full data set. Age, sex, maturation status and back-calculation of length-at-age was undertaken for a randomly selected representative subset (N = 889) of the data. Growth and spawning probability ogives49 were modeled based on this subset. Scale samples and otoliths were taken and used for age determination, of which scales were used primarily, and scales were used for back-calculation of growth50. Distance between the annuli was measured, and a direct proportional relationship between the length of the fish and the scale radius was assumed51. If the scales were difficult to read, which was the case for more slow-growing individuals from the low altitude lakes were the annuli were less distinct, otoliths were used for determining the age. As we did not have complete records of water temperature, area and time specific summer air temperature and precipitation measurements were obtained from an online database (www.eklima.no, Norwegian Metrological Institute). The database contained historical weather data from the closest representative (i.e., corresponding in distance, altitude and operational period) weather stations to the respective lakes (Fig. 5). This resulted in overlapping temperature and precipitation regimes for some of the lakes as there were in total five different weather stations that were most representative within the area containing the 21 lakes. Further, as there was some variation in how complete the different measurements were within years, we also had to calculate the sum of summer precipitation for a shorter period of the summer compared to the average mean air temperature. Both measurements still being good proxies for experienced summer conditions in the bulk of the growth season. The effect of temperature and precipitation was thus derived from the spatio-temporal variation in observations between these five weather stations, where the historic temporal variation corresponds to recorded climate components relevant to years for the back calculated age of the individual fish in the specific lakes (resulting in a total of 29 distinct measurements, see variation in Table 2) Epilimnetic water of lakes usually reflects warming trends in air temperature well, however hypolimnetic temperature variation might not be very correlated to the air temperature. Yet, changes in air temperature might indeed influence the thermal stratification of a lake and thus the environment and conditions for a fish52. There are good reasons to believe that most of the lakes in our study obtained some sort of thermal stratification during the summer season. Nonetheless, we chose not to model air to water temperature for the few measurements of water temperatures we had, and extrapolate this relationship to the full spatio-temporal resolution of the data. The rationale for this was threefold: (1) We were interested in exploring potential effects and relationships of easily available climate components, such as air temperature, simplifying the model concept; (2) we did not have access to detailed data on lake bathymetry so that hypothetical modeled air-to-water relationships would be rather uncertain; (3) we had no detailed information on how the brown trout was distributed in the water column during the summer period in study lakes. However, compared to similar lakes, there are reasons to believe that brown trout mainly feed and stay in the upper six meters of the water column, as well as epibenthic areas with high invertebrate abundances53,54, where both areas often are overlapping and highly influenced by the air temperature.Table 2 Description of candidate variables used in the model selection process determining the most supported model for individual growth of brown trout.Full size tableData analysis and model descriptionsOverall processWe used linear mixed model approaches to parameterize environmental effects on key life history traits for brown trout. Specifically: Length at age was parameterized as function of the environment (e.g., summer temperature, population density, winter NAO and summer precipitation). Spawning probability were modeled as functions of individual length and age. We also allowed either the age effect or length effect on spawning probability to vary with temperature or summer precipitation. Individual fecundity (number of eggs produced) was predicted as a function of length and spawning probability. Annual survival estimates from age 1 and up was accessed using catch curve analysis, while first year survival was estimated based on a stock-recruitment function. The estimated parameters were utilized to feed an age structured matrix projection model23, enabling long-term population viability projections in an changing environment (see overview in Fig. 6). Although there are several choices of population models that might be utilized for inferring the population dynamics, such as IBMs55 and IPMs56, the age structured matrix model was deemed especially suited to model our systems as they are highly seasonal (with very reduced growth during winter) and thus producing a clear age structure in the data. Further description of the various modeling approaches are described below. All statistical analyses was done in R57.Figure 6A schematic overview of the processes involved in our model-setup. Red lines indicate drivers and connections acting on individual life history traits, blue lines indicates traits driving the population model and green lines indicates links to climate variables. In short, existing area and time specific climate data on summer precipitation (Prec) and mean summer air temperature (Temp), as well as time specific data on winter NAO-index (recorded NAO values during December, January, February and March, NAO.DJFM), were used to parameterize models for length at age 1 and length at age  > 1, as well as spawning probability at age. Length at age 1 was allowed to affect length at age  > 1, and in the simulations achieved length at age  > 1 was also influenced by the achieved length the previous year (L*). Length at age and spawning probability, both defined by climate variables, interacted in defining how many eggs a female was likely to produce (i.e. fecundity). Survival from eggs to small juvenile fish was based on a stock-recruitment relationship, where the stock was defined by the results from the population model (expected number of fish). Expected number of fish across all ages was also allowed to affect length at age  > 1. The model parameters was used to simulate long term population dynamics, where we also varied expected temperature change scenarios (steadily increasing mean temperatures and temperature variation, respectively, as well as a combination of the two latter scenarios). The populations long term rate of increase (λ) was inferred using the age structured population matrix model.Full size imageSize at ageData inspections prior to model development showed length at age to be surprisingly linear within the size and age distribution in our data (i.e. no obvious signs of asymptotic growth for fish in any of the sampled lakes). Length (L) was thus explored using a linear mixed effects model approach with the lme4-package58. Denoted, length for individual j in population i (Lij) could thus be expressed as:$${L}_{ij}={sumlimits_{k=1}^{p}}{chi }_{ijk}{beta }_{k}$$Here, β = (β1, …, βp)T is px1 vector (one column matrix) of unknown regression parameters, χiT = (χi1, …, χip) ∈ ℝp is the explanatory variables of interest (k + p  1, age was always included as a variable, and we also tested models including an effect of CPUE and first year growth on subsequent growth trajectories. Multiple candidate models where the different environmental effects were allowed to vary with age were constructed (Supplementary information S1). Population ID and individual ID were included as nested random effects in all candidate models exploring size at age  > 1, and population ID was included as a random effect for the models exploring size at age 1. The most supported models were selected based on AIC-values59. During the population simulation the variation in the predictions attributed to the random effect(s) was treated as random noise, and not explicitly included in the simulations.Spawning probabilityBrown trout is an iteroparous species, however under normal food conditions and harsh winters in Norway it might not spawn every year following maturity. Accordingly, we modelled likelihood of spawning at age, derived from the number of female individuals that was going to spawn the following autumn, rather than probability of maturation at age. Aging effects on spawning probability was included in the modelling as skipped-spawning individuals (i.e., mature females that skip spawning episodes, sensu Rideout and Tomkiewicz 60) were coded as non-spawners in the analysis. Probability of spawning (P) was calculated based on a maturation-ogives approach61, utilizing generalized linear mixed effects models in the lme4-package58. Binomial models as two-dimensional ogives, o(A, L) were considered in the model selection. Here, A and L represent age and length, respectively. In addition, we also explored how these ogives might change due to either a temperature effect, summer precipitation effect, or a measure of fish abundance (CPUE) including either as an additive effect in some candidate models (see Supplementary information S2). Population ID was always included as a random effect. In general, the probability of spawning could thus be described as:$${mathrm{Pr}left(spawningright)}_{ij}={beta }_{0i}+{beta }_{1i}{A}_{ij}+{beta }_{2i}{L}_{ij}+{beta }_{3i}{A}_{ij}{L}_{ij}+{beta }_{4i}{x}_{1i}+{a}_{i}+{varepsilon }_{ij}$$
where βs represent coefficients under estimation, Aij = age of individual j in population i, L = individual length, x1 represent a lake-specific environmental variable (if present in the candidate model, either summer temperature, CPUE or precipitation), ai is the estimated random lake-specific intercept and εij is the random residual variation assumed normally distributed on logit scale. The most supported model was selected based on AICc-values59.FecundityFemale fecundity (i.e., number of eggs per female) was predicted as a function of female length (mm) and two constants based upon published values for brown trout from Norway (F = e log(l)*2.21–6.15)62 multiplied by the probability of spawning (P) at size and age.SurvivalAnnual survival rates (s) for fish age ≥ 1 were based on estimations from catch-curve slopes utilizing the Chapman-Robson function in the FSA-package63. The survival was estimated based on descending catch curves, i.e., where numbers of caught individuals decreased as a function of age in the catch. Based on this slope we can derive an instantaneous mortality rate (Z), and from this the annual survival rate could be estimated from S = e-Z. Due to a restricted number of populations available for survival rates, the survival was estimated across all population. As it is unlikely that S would be constant across all age classes we choose to make age specific survival rates, Sa, where the S1 (survival from age one to age two) was reduced, and S3-5 was slightly increased whereas all other Sa = S. The respective reduction and increase are described more in detail below. Survival rates for age 0–1, S0, was based on a stock-recruitment function (see further description under “Climate scenarios, calibration and population projections”).The projection matrixPopulation projections were derived utilizing an age-structured matrix population model23 in the popbio-package in R64. Changes in the age structure and abundance of brown trout was modelled from Nt+1 = K(E,N,t)Nt or rather:$${left[begin{array}{c}{N}_{1}\ {N}_{2}\ vdots \ vdots \ {N}_{{a}_{max}}end{array}right]}_{t+1}=left[begin{array}{ccccc}{f}_{1}left(L,P,{N}_{t}right){s}_{0}left({E}_{t}right)& {f}_{2}left(L,P,{N}_{t}right){s}_{0}left({E}_{t}right)& cdots & cdots & {f}_{{a}_{max}}left(L,P,{N}_{t}right){s}_{0}left({E}_{t}right)\ {s}_{a}& 0& cdots & cdots & 0\ 0& {s}_{a}& cdots & cdots & 0\ vdots & vdots & vdots & vdots & vdots \ 0& 0& 0& {s}_{a}& 0end{array}right]times {left[begin{array}{c}{N}_{1}\ {N}_{2}\ vdots \ vdots \ {N}_{{a}_{max}}end{array}right]}_{t}$$
where Nt is the abundance of brown trout across all age classes a = 1,…, amax at year t. Census time is chosen so that reproduction occurs at the beginning of each annual season. fa is the fecundity at age a (i.e., the number of offspring produced per individual of age a during a year). More specifically, f varies according to f(L,P,N), where variations in L (length) and P (probability to spawn) in turn is defined by climate variables and the number of individuals N. s is a constant and represent the survival probability of individuals from age a to age a + 1, and amax is the maximum age considered in the model. amax was set to 10 years in the simulations, as was also was the age of the oldest fish in the aged subset of the data (see frequency table in Supplementary information S2). Although varying between systems, the maximum age observed and simulated also corresponds to expected maximum age found in other systems in Norway65. S0 is a function of E, the numbers of eggs laid, where the relationship is determined by a stock-recruitment function.Consequently, K(E,N,t), the Leslie matrix, is a function of N and E. In each time step, the survival of individuals in age class amax is 0, whereas individuals at all other ages spawn and experience mortality as defined above. From the Leslie matrix K, we can infer the population’s long-term rate of increase, λ, from the dominant eigenvector of the matrix23.Climate scenarios, calibration and population projectionsTo explore the population effects of changes in summer air temperature or winter conditions we simulated different 100-years climate-change scenarios for a single lake, which included variations the climate variables in focus. The first scenario represented a status quo setting. Here, annual average summer air temperatures were randomly drawn from a normal distribution with mean and standard deviation from observed summer air temperatures from 1998–2009 in the study area. The second climate scenario randomly assigned temperatures as in scenario one, as well as allowing for more and more fluctuating annual summer temperatures as time progressed. This was done by adding a random variable t (~ N(0,0.03) times the number of the specific year (i.e., 1–100) in the 100-years climate change scenario. The third climate scenario, drew annual summer temperatures as in the first scenario, but included an increase in the average air summer temperature by 0.04 °C each year (i.e., 4 °C in total for the 100-year-scenario which is close to the expected mean increase in regional temperature following the regionally down-scaled RCP8.5 IPCC scenario66). The fourth climate scenario included an average summer temperature increase of 0.02 °C each year (close to the expected average temperature increase following the regionally down-scaled RCP4.5 IPCC scenario66), as well as allowing for more and more fluctuating annual summer temperatures as time progressed (as in scenario two). For all climate scenarios above, annual winter NAO-values was randomly drawn from a uniform distribution between − 1.5 and 1.5.We also simulated a second set of climate change scenarios, where summer temperatures were as described in the four scenarios above, however in all these scenarios we also included a trend of higher winter NAO values (meaning a general trend of warmer winters with more precipitation/snow in the study area, as predicted by the down scaled climate scenarios66). This was done by letting annual NAO-values be drawn from a random normal distribution with mean = 0.5, and standard deviation of 0.5.During the calibration process for the simulations, we altered the age specific survival estimates S1 and S3-5 so that average lambdas for the status quo climate scenario was relatively stable and close to 1 (i.e. no large changes in population size) based on 100 iterations of a 100 year-climate scenario. Specifically, S1 = S*0.6 and S3-5 = S*1.2, which is also assumed to be within the realistic range of survival rates for the specific age classes in the focal populations. S0 was derived from a stock recruitment function, and was thus allowed to vary as a function of density in the population. Specifically, from the total egg number (Et) at year t and the number of one-year olds at year t + 1 (N1,t+1) the stock-recruitment function could be estimated by fitting a Shepherd function67:$${N}_{1,t+1}=frac{a{E}_{t}}{{left(1+b{E}_{t}right)}^{c}}$$
where a = 0.04, b = 0.0000003 and c = 3.5. E is number of eggs deposited during t-1 spawning season, estimated as the total fecundity. The estimated N1,t+1 was used to estimate first-year survival (s0) from:$${s}_{0,t}=mathrm{ln}left({E}_{t-1}right)-mathrm{ln}left({N}_{1,t}right)$$All 100-years scenarios were simulated with 100 iterations to extract the variation in the expected population projections. CPUE in the simulations was included as a dynamic variable in the growth model, recalculated through the matrix projection model for each time step, i.e. year. Length at age, spawning probability and fecundity was predicted for each time step (i.e. pr year) as described above. The spawning probability did however not vary annually according to changes in the environment but was predicted according to the mean values of the environmental variables across all years the climate scenario. However, for climate scenarios with increasing mean temperature over time, the expected spawning probability was a function of the gradual mean temperature increase. Thus, by allowing the spawning probability reaction norm gradually to follow changes in the temperature, as predicted from the spawning model, we allowed the populations to gradually adapt the reaction norm to the respective changes. More

Degree of hydrolysisOrganic silages prepared from fat fish (FFS) and lean fish (LFS) had a characteristic tawny brown colour which was accompanied with a strong characteristic salty-fishy odour. At the end of 5 DoF, both FFS and LFS exhibited sluggish liquefaction which increased progressively concomitant with the DoF (Table S1). Liquefaction is an indicator of tissue hydrolysis due to the action of acid. During 35 DoF, the degree of hydrolysis (measured in terms of liquefaction volume) increased progressively with the DoF in both types of ensilages and was relatively higher in LFS compared to FFS on all sampled DoF (Table S1). In general, lipolysis supersedes the proteolysis in all major biochemical processes23. A relatively higher degree of hydrolysis recorded in LFS may be attributed to the presence of a greater proportion of light muscles compared to dark muscles. Relatively greater susceptibility of light muscles to hydrolysis compared to dark muscles might be due to lower lipid content in the former23.Irrespective of fish type, the measured pH values in both types of ensilages (FFS and LFS) were similar (data not shown) and the values showed a progressive increase from 1.0 ± 0.03 (0 DoF) to 6.0 ± 0.03 (35 DoF). Such an increasing trend in pH with the advancement in DoF could be attributable to gradual solubilisation of boney material with the advancement fermentation time24,25,26.Changes in principal biochemical constituentsDuring the 35 DoF, the concentrations of total protein (TP) in both FFS and LFS progressively increased with the DoF and showed significant differences with the advancement of DoF (p  phenylalanine (2.6 ± 0.033)  > serine (2.4 ± 0.033)  > aspartic acid (2.3 ± 0.033)  > alanine (2.1 ± 0.033)  > histidine (1.8 ± 0.033)  > valine (1.6 ± 0.033)  > methionine (1.5 ± 0.033)  > isoleucine (1.5 ± 0.033)  > threonine (1.4 ± 0.033)  > cysteine (0.946 ± 0.033).Figure 1Composition of total amino acids (mg/g) in two types of fish ensilages (FFS—fat fish silage; LFS—lean fish silage) during 35 days of fermentation (DoF). Data are mean ± SD. * p  glutamic acid (4.97 ± 0.033)  > arginine (4.5 ± 0.033)  > phenylalanine (3.38 ± 0.033)  > aspartic acid (2.92 ± 0.033)  > alanine (2.23 ± 0.033)  > methionine (2.19 ± 0.033)  > lysine (1.882 ± 0.033)  > serine (1.881 ± 0.033)  > tyrosine (1.410 ± 0.033)  > glycine (1.219 ± 0.033)  > threonine (0.953 ± 0.033)  > valine (0.945 ± 0.033)  > isoleucine (0.864 ± 0.033)  > histidine (0.417 ± 0.033).A comparative assessment of profiles of TAA in both FSS and LFS during all DoF revealed a similar pattern, albeit with obvious differences in the concentration of few amino acids (Fig. 1). It has been hypothesised that the occurrence of decarboxylation that follows transamination of amino acids as a consequence of increase in pH during fermentation is known to cause a decrement in the concentration of few amino acids, especially valine and isoleucine34. During the present study, the concentrations of histidine, valine, isoleucine, glycine and lysine were significantly higher (p  leucine (3.09 ± 0.003)  > glutamic acid (2.61 ± 0.003)  > alanine (1.83 ± 0.003)  > phenylalanine (1.79 ± 0.003)  > cysteine (1.67 ± 0.003)  > histidine (1.56 ± 0.003)  > aspartic acid (1.54 ± 0.003)  > serine (1.32 ± 0.003)  > lysine (1.16 ± 0.003)  > threonine (1.09 ± 0.003)  > valine (1.07 ± 0.003)  > isoleucine (1.06 ± 0.003) followed by methionine (0.93 ± 0.003)  > tyrosine (0.92 ± 0.003)  > tryptophan (0.72 ± 0.003)  > asparagine (0.57 ± 0.003)  > glutamine (0.15 ± 0.003).Figure 2Composition of free amino acids (mg/g) in two types of fish ensilages (FFS—fat fish silage; LFS—lean fish silage) during 35 days of fermentation (DoF). Data are mean ± SD. * p  More

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Our study uniquely points to differences among scientific disciplines in data availability as published along with the article and upon request from the authors. We demonstrate that in several disciplines such as forestry, materials for energy and catalysis and psychology, critical data are still unavailable for re-analysis or meta-analysis for more than half of the papers published in Nature and Science in the last decade. These overall figures roughly match those reported for other journals in various research fields8,11,13,22, but exceed the lowest reported values of around 10% available data13,23,24. Fortunately, data availability tends to improve, albeit slowly, in nearly all disciplines (Figs. 3, 7), which confirms recent implications from psychological and ecological journals13,31. Furthermore, the reverse trend we observed in microbiology corroborates the declining metagenomics sequence data availability22. Typically, such large DNA sequence data sets are used to publish tens of articles over many years by the teams producing these data; hence releasing both raw data and datasets may jeopardise their expectations of priority publishing. The weak discipline-specific differences among Nature and Science (Fig. 2) may be related to how certain subject editors implemented and enforced stringent data sharing policies.After rigorous attempts to contact the authors, data availability increased by one third on average across disciplines, with full and at least partial availability reaching 70% and 83%, respectively. These figures are in the top end of studies conducted thus far8,22 and indicate the relatively superior overall data availability in Science and Nature compared with other journals. However, the relative rates of data retrieval upon request, decline sharing data and ignoring the requests were on par with studies covering other journals and specific research fields10,12,25,26,28. Across 20 years, we identified the overall loss of data at an estimated rate of 3.5% and 5.9% for initially available data and data effectively available upon request, respectively. This rate of data decay is much less than 17% year−1 previously reported in plant and animal sciences based on a comparable approach24.While the majority of data are eventually available, it is alarming that less than a half of the data clearly stated to be available upon request could be effectively obtained from the authors. Although there may be objective reasons such as force majeure, these results suggest that many authors declaring data availability upon contacting may have abused the publishers’ or funders’ policy that allows statements of data availability upon request as the only means of data sharing. We find that this infringes research ethics and disables fair competition among research groups. Researchers hiding their own data may be in a power position compared with fair players in situations of big data analysis, when they can access all data (including their own), while others have more limited opportunities. Data sharing is also important for securing a possibility to re-analyse and re-interpret unexpected results9,32 and detect scientific misconduct25,33. More rigorous control of data release would prevent manuscripts with serious issues in sampling design or analytical procedures from being prepared, reviewed and eventually accepted for publication.Our study uniquely recorded the authors’ concerns and specific requests when negotiating data sharing. Concerns and hesitations about data sharing are understandable because of potential drawbacks and misunderstandings related to data interpretation and priority of publishing17,34 that may outweigh the benefits of recognition and passive participation in broader meta-studies. Nearly one quarter of researchers expressed various concerns or had specific requests depending on the discipline, especially about the specific objectives of our study. Previous studies with questionnaires about hypothetical data sharing unrelated to actual data sharing reveal that financial interests, priority of additional publishing and fear of challenging the interpretations after data re-analysis constitute the authors’ major concerns12,35,36. Another study indicated that two thirds of researchers sharing biomedical data expected to be invited as co-authors upon use of their data37 although this does not fulfil the authorship criteria6,38. At least partly related to these issues, the reasons for declining data sharing differed among disciplines: while social scientists usually referred to the loss of data, psychologists most commonly pointed out ethical/legal issues. Recently published data were, however, more commonly declined due to ethical/legal issues, which indicates rising concerns about data protection and potential misuse. Although we offered a possibility to share anonymised data sets, such trimmed data sets were never obtained from the authors, suggesting that ethical issues were not the only reason for data decline. Because research fields strongly differed in the frequency of no response to data requests, most unanswered requests can be considered declines that avoid official replies, which may harm the authors’ reputation.Because we did not sample randomly across journals, our interpretations are limited to the journals Nature and Science. Our study across disciplines did not account for the particular academic editor, which may have partly contributed to the differences among research fields and journals. Not all combinations of disciplines, journals and time periods received the intended 25 replicate articles because of the poor representation of certain research fields in the 2000–2009 period. This may have reduced our ability to detect statistically significant differences among the disciplines. We also obtained estimates for the final data availability for seven out of nine disciplines. Although we excluded the remaining two disciplines from comparisons of initial and final data availability, it may have slightly altered the overall estimates. The process of screening the potentially relevant articles chronologically backwards resulted in overrepresentation of more recent articles in certain relatively popular disciplines, which may have biased comparisons across disciplines. However, the paucity of residual year effect and year x discipline interaction in overall models and residual time effect in separate analyses within research fields indicate a minimal bias (Figure S1).We recorded the concerns and requests of authors that had issues with initial data sharing. Therefore, these responses may be relatively more sceptic than the opinions of the majority of the scientific community publishing in these journals. It is likely that the authors who did not respond may have concerns and reasons for declining similar to those who refused data sharing.Our experience shows that receiving data typically required long email exchanges with the authors, contacting other referred authors or sending a reminder. Obtaining data took on average 15 days, representing a substantial effort to both parties39. This could have been easily avoided by releasing data upon article acceptance. On the other hand, we received tips for analysis, caution against potential pitfalls and the authors’ informed consent upon contacting. According to our experience, more than two thirds of the authors need to be contacted for retrieving important metadata, variance estimates or specifying methods for meta-analyses40. Thus, contacting the authors may be commonly required to fill gaps in the data, but such extra specifications are easier to provide compared with searching and converting old datasets into a universally understandable format.Due to various concerns and tedious data re-formatting and uploading, the authors should be better motivated for data sharing41. Data formatting and releasing certainly benefits from clear instructions and support from funders, institutions and publishers. In certain cases, public recognition such as badges of open data for articles following the best data sharing practices and increasing numbers of citations may promote data release by an order of magnitude42. Citable data papers are certainly another way forward43,44, because these provide access to a well-organised dataset and add to the authors’ publication record. Encouraging enlisting published data sets with download and citation metrics in grant and job applications alongside with other bibliometric indicators should promote data sharing. Relating released data in publicly available research accounts such as ORCID, ResearcherID and Google Scholar would benefit both authors, other researchers and evaluators. To account for many authors’ fear of data theft17 and to prioritise the publishing options of data owners, setting a reasonable embargo period for third-party publishing may be needed in specific cases such as immediate data release following data generation45 and dissertations.All funders, research institutions, researchers, editors and publishers should collectively contribute to turn data sharing into a win-win situation for all parties and the scientific endeavour in general. Funding agencies may have a key role here due to the lack of conflicting interests and a possibility of exclusive allocation to depositing and publishing huge data files46. Funders have efficient enforcing mechanisms during reports periods, with an option to refuse extensions or approving forthcoming grant applications. We advocate that funders should include published data sets, if relevant, as an evaluation criterion besides other bibliometric information. Research institutions may follow the same principles when issuing institutional grants and employing research staff. Institutions should also insist their employees on following open data policies45.Academic publishers also have a major role in shaping data sharing policies. Although deposition and maintenance of data incur extra costs to commercial publishers, they should promote data deposition in their servers or public repositories. An option is to hire specific data editors for evaluating data availability in supplementary materials or online repositories and refusing final publishing before the data are fully available in a relevant format47. For efficient handling, clear instructions and a machine-readable data availability statement option (with a QR code or link to the data) should be provided. In non-open access journals, the data should be accessible free of charge or at reduced price to unsubscribed users. Creating specific data journals or ‘data paper’ formats may promote publishing and sharing data that would otherwise pile up in the drawer because of disappointing results or the lack of time for preparing a regular article. The leading scientometrics platforms Clarivate Analytics, Google Scholar and Scopus should index data journals equally with regular journals to motivate researchers publishing their data. There should be a possibility of article withdrawal by the publisher, if the data availability statements are incorrect or the data have been removed post-acceptance30. Much of the workload should stay on the editors who are paid by the supporting association, institution or publisher in most cases. The editors should grant the referees access to these data during the reviewing process48, requesting them a second opinion about data availability and reasons for declining to do so. Similar stringent data sharing policies are increasingly implemented by various journals26,30,47.In conclusion, data availability in top scientific journals differs strongly by discipline, but it is improving in most research fields. As our study exemplifies, the ‘data availability upon request’ model is insufficient to ensure access to datasets and other critical materials. Considering the overall data availability patterns, authors’ concerns and reasons for declining data sharing, we advocate that (a) data releasing costs ought to be covered by funders; (b) shared data and the associated bibliometric records should be included in the evaluation of job and grant applications; and (c) data sharing enforcement should be led by both funding agencies and academic publishers. More

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Computational sustainability research has been supported by an Expedition in Computing from the US National Science Foundation (NSF; CCF-1522054). eBird has been supported by the Leon Levy Foundation, the Wolf Creek Foundation, and NSF (DBI-1939187). Materials science research has also been supported by the AFOSR Multidisciplinary University Research Initiative (MURI) Program FA9550-18-1-0136, US DOE Award No.DE-SC0020383, and an award from the Toyota Research Institute. More