The “dirty dozen” of freshwater science: Detecting then reconciling hydrological data biases and errors

Sound water policy and management rests on sound hydrometeorological and ecological data. Conversely, unrepresentative, poorly collected or erroneously archived data introduces uncertainty regarding the magnitude, rate and direction of environmental change, in addition to undermining confidence in decision-making processes. Unfortunately, data biases and errors can enter the information flow at various stages, starting with site selection, instrumentation, sampling/ measurement procedures, post-processing and ending with archiving systems. Techniques such as visual inspection of raw data, graphical representation and comparison between sites, outlier and trend detection, and referral to metadata can all help uncover spurious data. Tell-tale signs of ambiguous and/or anomalous data are highlighted using 12 carefully chosen cases drawn mainly from hydrology (‘the dirty dozen’). These include evidence of changes in site or local conditions (due to land management, river regulation or urbanisation); modifications to instrumentation or inconsistent observer behaviour; mismatched or misrepresentative sampling in space and time; treatment of missing values, post-processing and data storage errors. As well as raising awareness of pitfalls, recommendations are provided for uncovering lapses in data quality after the information has been gathered. It is noted that error detection and attribution are more problematic for very large data sets, where observation networks are automated, or when various information sources have been combined. In these cases, more holistic indicators of data integrity are needed that reflect the overall information life-cycle and application(s) of the hydrological data

Representative result of high-throughput screening of BSH inhibitors.

<p>(A) Plate layout for screening BSH inhibitors. Pink boxes (columns 3–22) indicate test wells that contain library compounds of interest, BSH, and reaction mix containing substrate. Library compounds were shot into the well bottom using Echo 550/555 and enzyme and reaction mix were added using Multidrop Combi. Controls were added manually to the side wells (columns 1–2 and 23–24): blue boxes indicate activity controls (BSH, reaction mix containing substrate, and solvent DMSO), yellow boxes correspond to inhibition controls (BSH, reaction mix containing substrate, and NaIO₃), and white boxes are negative controls with no BSH added but include reaction mix as well as substrate. (B) The HTS results represented by one 384-well plate. Control wells indicate the assay proceeded normally. The wells in columns 3–22 that appeared clear, regardless of alternative color due to compound, and had low absorbance readings were considered hits (putative BSH inhibitors).</p

Effect of selected HTS hits on BSH activity^a.

<p>^a Unless specified, the final concentration of compound in the reaction mix was 5 mM to achieve optimal resolution with the quantitative BSH activity assay.</p><p>^b The final concentration of riboflavin in reaction mix was 1 mM.</p><p>^c The final concentration of chrysophanol in reaction mix was 1.25 mM.</p><p>^d The final concentration of folic acid in reaction mix was 1.5 mM.</p

Dosing effects of oxytetracycline and roxarsone on BSH activity.

<p>Black bars represent Inhibition of BSH activity by oxytetracyline and white bars by roxarsone. All assays were performed in triplicate. The procedure is detailed in Materials and Methods.</p

Optimization of conditions for high-throughput screening of BSH inhibitors.

<p>(A) Principal of the strategy for HTS screening of BSH inhibitors. (B) Proof of concept experiment to demonstrate the feasibility of the HTS methodology in 96-well plate. Using 200 µl of total reaction volume, BSH inhibitor was added at a final concentration of 0.5 mM in column 1 with 2-fold serial dilution through column 10. KIO₃ is used as an example in duplicate rows. The purified BSH was added at final concentration of 8 µg/ml in column wells 1–11. Column 12 served as a negative control with no enzyme and BSH inhibitor added.</p

Dosing effects of selected BSH inhibitors on BSH activity.

<p>(A) Inhibition of BSH activity by caffeic acid phenethyl ester (CAPE). (B) Inhibition of BSH activity by riboflavin. All assays were performed in triplicate. The procedure is detailed in Materials and Methods.</p

Effect of different classes of antibiotics on BSH activity.

<p>The antibiotic hits by HTS.</p

Photographs in life and habitat of <i>Tympanocryptis condaminensis</i> sp. nov.

<p>(a) Kunari Station, Darling Downs; (b) & (c) Bongeen, Darling Downs (photos S. Wilson); and (d) typical habitat - Bongeen, Darling Downs (photo S. Wilson).</p

Holotypes of: (a) <i>Tympanocryptis pentalineata</i> sp. nov.; (b) <i>Tympanocryptis condaminensis</i> sp. nov.; and (c) <i>Tympanocryptis wilsoni</i> sp. nov.

<p>Dorsal, ventral and lateral views are shown for each holotype. Details on types series for these species are in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101847#pone-0101847-t003" target="_blank">Table 3</a>.</p

Bayesian phylogenetic tree for <i>Tympanocryptis tetraporophora</i> based on ∼1200 bp nuclear DNA (<i>RAG1</i>).

<p>Samples sequenced in the current study are designated by museum registration numbers and previously published sequences are designated by GENBANK numbers.</p