TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Transverse momentum spectra of charged particles in proton-proton collisions at s=900\sqrt{s} = 900 GeV with ALICE at the LHC

The inclusive charged particle transverse momentum distribution is measured in proton-proton collisions at $\sqrt{s} = 900$ GeV at the LHC using the ALICE detector. The measurement is performed in the central pseudorapidity region $(|\eta|<0.8)$ over the transverse momentum range $0.15<p_{\rm T}<10$ GeV/$c$. The correlation between transverse momentum and particle multiplicity is also studied. Results are presented for inelastic (INEL) and non-single-diffractive (NSD) events. The average transverse momentum for $|\eta|<0.8$ is $\left<p_{\rm T}\right>_{\rm INEL}=0.483\pm0.001$ (stat.) $\pm0.007$ (syst.) GeV/$c$ and \left_{\rm NSD}=0.489\pm0.001 (stat.) $\pm0.007$ (syst.) GeV/$c$, respectively. The data exhibit a slightly larger $\left<p_{\rm T}\right>$ than measurements in wider pseudorapidity intervals. The results are compared to simulations with the Monte Carlo event generators PYTHIA and PHOJET.Comment: 20 pages, 8 figures, 2 tables, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/390

Production of pions, kaons and protons in pp collisions at s=900\sqrt{s}=900 GeV with ALICE at the LHC

The production of $\pi^+$, $\pi^-$, $K^+$, $K^-$, p, and pbar at mid-rapidity has been measured in proton-proton collisions at $\sqrt{s} = 900$ GeV with the ALICE detector. Particle identification is performed using the specific energy loss in the inner tracking silicon detector and the time projection chamber. In addition, time-of-flight information is used to identify hadrons at higher momenta. Finally, the distinctive kink topology of the weak decay of charged kaons is used for an alternative measurement of the kaon transverse momentum ($p_{\rm T}$) spectra. Since these various particle identification tools give the best separation capabilities over different momentum ranges, the results are combined to extract spectra from $p_{\rm T}$ = 100 MeV/$c$ to 2.5 GeV/$c$. The measured spectra are further compared with QCD-inspired models which yield a poor description. The total yields and the mean $p_{\rm T}$ are compared with previous measurements, and the trends as a function of collision energy are discussed.Comment: 24 pages, 18 captioned figures, 5 tables, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/388

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 research.Peer reviewe