Sustainable knowledge management: The role of knowledge management audits

Sustainable knowledge management: The role of knowledge management audit

Mean reading times necessary for the participants to read the non-repeated triads (A) and the repeated triads (B) of mirrored-inverted words.

<p>The mean reading time (in seconds, log-transformed) was plotted as a function of the experimental blocks (1 to 10) for the young adults (grey), the older adults (black), the PD ON their medication (purple) and the PD OFF their medication (cyan). The blocks 6 to 10 were performed following a 50-minutes break represented by the dashed vertical line. Error bars are standard-error of the mean.</p

Characteristics of the people with Parkinson’s disease.

<p>Characteristics of the people with Parkinson’s disease.</p

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science

Mean reading time in seconds for the non-repeated triads of all 10 blocks.

<p>Mean reading time in seconds for the non-repeated triads of all 10 blocks.</p

Data from one subject off levodopa showing LFPs with extracted amplitude and phase.

<p>The first column shows 3(A = left STN, B = right STN) and their respective power spectra (C; left STN - purple, right STN - green). The second column shows the LFP pass-band filtered around the corresponding beta peak (blue) of each STN (D = left STN, E = right STN) with the amplitude shown in red. The crosses show the average amplitude for each 1 second window and the final graph shows the correlation of these 1 s average amplitudes across the two sides over 74 s duration record, with a linear regression line through them (F). The r value of this linear regression line is taken as the value of the amplitude co-modulation for any given subject. In this example r = 0.57, p<0.001. The right column shows the superimposed phase of the two LFP signals (red = left STN and blue = right STN) over 3 s (G) with the phase difference over this period shown below (H). A rose plot underneath shows the proportion of phase difference vectors at all points for the whole recording around the unit circle (I). The length of the average of these vectors is then taken as the value of the phase locking value (PLV), which in this case was 0.22. Note low frequency oscillations at about 1 Hz likely to be cardiac pulse artefact in A and B. Despite this, modulations in the amplitude envelopes of the beta band filtered LFP activity shown in D and E are not time-locked to the low frequency cardiac pulse artifacts in 1A and B.</p

Coherence between STNs.

<p>Top panel shows mean ± SEM coherence of all 23 subjects in the off (blue) and on (red) medication state. Bottom panel shows the mean ± SEM % change between the two states (on –off medication) in the beta sub-bands. Only the coherence suppression in the beta 1 band following levodopa was significant (t22 = −2.7; p = 0.01).</p

Amplitude co-modulation between STNs.

<p>Top panel shows mean ± SEM amplitude co-modulation of all 23 subjects in the off (blue) and on (red) medication state. Bottom panel shows the mean ± SEM % change between the two states (on – off medication) in the beta sub-bands. There was no significant effect of levodopa, frequency band or interaction between the two (see results).</p

Histogram of beta phase differences between bilateral STN.

<p>Histogram of all phase differences across 23 subjects at peak beta frequency off medication, demonstrating predominance of zero phase lag.</p

Phase synchronisation between STNs.

<p>Top panel shows mean ± SEM amplitude PLV of all 23 subjects in the off (blue) and on (red) medication state. Bottom panel shows the mean ± SEM % change in PLV between the two states (on – off medication) in the beta sub-bands. Only the beta 1 band PLV was suppressed following levodopa (t₂₂ = −2.8, p = 0.01).</p