Philippa Kaur

Chemicals and reagentsThe fluopyram standard was purchased from the Environmental Protection Monitoring Institute of the Ministry of Agriculture of China at a concentration of 1000 mg/L. Analytical grade acetonitrile, acetone, dichloromethane, and sodium chloride were purchased from the Guangzhou Chemical Reagent Factory. Chromatographic grade Methanol and n-hexane were available from Thermo Fisher Scientific. Purified water was prepared using a Milli-Q reverse osmosis system (Millipore, Milford, MA, USA). Strata Florisil (FL-PR) 500 mg/6 mL SPE manufactured by Strata™ (5.0 mL n-hexane–acetone (9:1, V/V) solution pre-rinsing cartridge).A standard solution of 1000 μg/mL fluopyram was diluted in n-hexane, and the matrix extract of the blank sample was obtained by the extraction method. The matrix standard solutions of 0.025, 0.05, 0.10, 0.15 and 0.50 μg/mL were obtained by the step dilution. All prepared solutions were stored at temperature of 4 °C until further use.Soil sample collectionHainan latosol was collected from the Bailian banana experimental base in Chengmai (Hainan), Yunnan sandy soil was collected from Taoyuan banana experimental base in Longtou Street, Kunming (Yunnan) and Fujian plain alluvial soil was collected from the Zhangzhou banana experimental base (Fujian). 5–10 soil sampling points were randomly selected in each banana experimental base; the soil samples were collected from depths of 0–10 cm, and debris such as gravel, weeds, and plant roots were removed from each sample. The soil samples were obtained by the quarter method after mixing, dried, and stored after 20 mesh screening.Extraction and purification of flupyramSoil sample extraction was conducted as follows: in a 200 mL conical flask, 20.0 g of the drying soil sample and 40.0 mL acetonitrile was added. After shaking on a reciprocating shaker for 2 h, the mixture was filtered through filter paper. The filtrate was transferred to a stoppered measuring cylinder with 6.0 g NaCl. The stopper was inserted, and the mixture was vigorously shaken for 2 min. The mixture was left at 25 ± 2 °C for more than 30 min to separate the acetonitrile and aqueous solutions. Meanwhile, 10.0 mL of the supernatant were accurately transferred into a 100 mL round-bottom flask and concentrated by a rotatory evaporator at 40 °C to near dryness, which was dissolved in a 5.0 mL n-hexane–acetone (9:1, v/v) solution, vortexed, and mixed well for purification.Water sample extraction is shown below. A 20 mL water sample was transferred to a separatory funnel with 40.0 mL dichloromethane. After vigorously shaking it for 2 min and then letting it stand for 30 min, the lower layer solution was collected in a 100 mL round-bottom flask. The collected fluid was concentrated by a rotatory evaporator at 40 °C to near dryness and dissolved in 5.0 mL n-hexane–acetone (9:1, v/v) solution, vortexed, and mixed well for purification.Sample purification is described below. A 5.0 mL n-hexane–acetone (9:1, v/v) was used to preach the Strata Florisil (FL-PR) 500 mg/6 mL extraction column. When the leaching solvent level reached the surface of the column adsorption layer, the solution sample was immediately poured into the column be purified. Then, the purified solution was collected in a 100 mL round-bottom flask. A 5.0 mL n-hexane–acetone (9:1, v/v) solution was used to rinse the round-bottom flask residuum, after which the rinse solution was applied to elute the Florisil column. The rinsing and elution steps were repeated three times. The collected fluid was concentrated by a rotatory evaporator at 40 °C to near dryness and dissolved in 2.5 mL n-hexane for analysis.Instrumental conditionThe test was performed using the Theomer DSQII gas chromatography-mass spectrometer (GC–MS) with Xcalibur 2.0, software for data acquisition and analysis. A SLB-5MS analytical column (30 m × 0.25 mm × 0.25 μm) was used as chromatographic column. The injection volume was 1 μL without split injection, the carrier gas was helium (He, ≥ 99.999% purity), and the carrier gas flow rate was set to 1.0 mL/min. The protective gas was nitrogen (N2, ≥ 99.999% purity), and the injection port temperature was 250 °C. The chromatographic column temperature program was set as follows: the initial temperature at 80 °C was maintained for 1 min; then it was raised to 240 °C at a speed of 20 °C/min and maintained for 3 min; finally, the temperature was raised at a rate of 50 °C/min until 280 °C, where it was maintained for 7 min.The MS was operated in electron ionisation (EI) mode with an ionising energy of 70 eV. MS data were acquired in both full scan (m/z 50–500) mode for identification and selected ion monitoring (SIM) mode for quantification. The temperatures of the ion source and transfer line were 250 °C and 280 °C, respectively. The retention time of fluopyram was 10.59 min. The quantifier ions were m/z 223, and the qualifier ions were m/z 195 and m/z 173.Analytical method validationFirst, we addressed the linearity. The matrix standard of fluopyram was prepared in the range of 0.025–0.50 μg/mL and the determination was carried out, with the concentration of fluopyram matrix standard solution as the abscissa and the peak area obtained from the GC–MS as the ordinate. Linearity was calculated by plotting the relationship between the concentration and the peak area.The sensitivity analysis relied on the LOD and the limit of quantitation (LOQ). To evaluate the sensitivity of the method, they were obtained by adding the standard solution of fluopyram at the lowest concentration level in line with the requirements of the analytical method for blank samples. The LOD was the corresponding concentration when the signal-to-noise ratio (S/N) was 3, and S/N = 10 corresponds to the LOQ.Accuracy and precision were estimated as well. To determine the reliability of the method, fluopyram standard solutions with different concentrations were added to the blank sample for the recovery experiment. Fluopyram standard solutions with concentrations of 0.008, 0.600, and 1.000 mg/kg were added to the blank samples. This procedure was repeated five times for each concentration. The samples were subjected to extract, purify and analysis under the method the same conditions as described above. The recovery was calculated for the accuracy of the method, and the RSD was calculated for the precision.Soil dissipation experimentIn a number of 100 mL clean and sterilized conical flasks with covers, 20.0 g of soil was added (net weight converted by water content); then, 0.1 mL 1000 μg/mL fluopyram standard solution was pipetted into the conical flasks. Ultrapure water was added. The water was controlled to occupy 60% of the total volume. The flasks were shaken on a constant temperature oscillator for 2 min to mix the fluopyram evenly. Then, they were placed in an artificial climate incubator and exposed to light at 25 ± 2 °C for 12 h per day. According to the different soil types, they were divided into three treatment groups: Hainan, Yunnan, and Fujian. Each treatment group had three parallels and three blanks. The detection intervals were 2 h, 1, 3, 7, 14, 21, 28, 42 and 60 day, while the detection of fluopyram was performed based on the interval according to the shown methods. The dissipation kinetics of fluopyram in banana planting soil conformed to the first-order kinetic equation Ct = C0e−kt, where Ct is a pesticide concentration (mg/kg) at different times (day), C0 is an initial concentration (mg/kg), and k is the dissipation rate constant. The half-life of fluopyram is determined using Eq. (1).$$T_{1/2} = , ln 2/k$$
(1)
Soil adsorption experimentUsing the oscillation balance method, 5.0 g of soil was put into the 250 mL conical flasks with cover, which contained 25 mL fluopyram aqueous solutions with mass concentrations of 0.02, 0.1, 0.5, 2.5 and 4.0 mg/L (containing 0.01 mol/L CaCl2), respectively. The soils were divided into three treatment groups: Hainan, Yunnan, and Fujian (based on the different soil types). The fluopyram aqueous solution and the blank soil aqueous solution (both containing 0.01 mol/L CaCl2) were used as controls. Each treatment group had three replicates. The conical flasks were then placed in a constant temperature oscillator at 25 ± 2 °C for 24 h to prepare the suspension. The suspension was transferred to a centrifuge tube for high-speed centrifugation, and 80% of the total volume of the supernatant was used for determination. The fluopyram in the supernatant was extracted and determined under the methods as described above, and the Freundlich equation model (see Eq. 2) was used to describe the adsorption law for fluopyram in soil.$${text{Freundlich: }}C_{s} = K_{f} times C_{e}^{1/n}$$
(2)
where Cs is adsorption content of pesticide in soil (mg/kg), Ce is concentration of the pesticide in aqueous solution at adsorption equilibrium (mg/L), Kf is the soil adsorption coefficient of the Freundlich model (L/kg), indicating the pesticide adsorption capacity of the soil and 1/n is a slope rate of the curve between Cs and Ce, reflecting the heterogeneity of the adsorbent surface.The relationship between the adsorption free energy of soil to pesticides (ΔG, kJ/mol) and the soil adsorption coefficient Koc is expressed using Eq. (3).$$Delta G , = – RTln K_{oc}$$
(3)
where Koc is the soil adsorption coefficient (Koc = Kf/OC × 100) expressed by organic carbon content (L/kg), OC is soil organic carbon content (%), R is the molar gas constant (J/K mol), and T is absolute temperature (K).Soil leaching experimentA plexiglass tube with an inner diameter of 5 cm and a length of 40 cm was used as a packed column. A layer of cotton, a 1 cm thick quartz sand layer, and a layer of filter paper were added at the bottom of the column. Dry soil (700–800.0 g) was weighed for filling, and the column was fully wetted with ultrapure water to prepare a 30 ± 0.2 cm high leaching soil column. 0.1 mL of 1000 μg/mL fluopyram solution was further added to 5.0 g of soil. After the solution completely volatilized, it was evenly spread on the top of the soil column, and a layer of filter paper and a layer of 1 cm thick quartz sand were added to the top of the soil. During the test, ultrapure water was used for washing the soil column for 10 h at a speed of 30 mL/h, and the leaching solution was collected. After washing, the soil column was removed and was cut into four sections of 1–5, 5–10, 10–20 and 20–30 cm. The residues of fluopyram in the soil samples and leaching solutions were extracted and determined under the methods as described above. According to the three soil types, they were divided into Hainan, Yunnan and Fujian treatment groups, where each group received another parallel treatment. 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

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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