1,721,216 research outputs found
Antarctic Specially Protected Areas and Antarctic Specially Managed Areas in the Australian Antarctic Territory - GIS polygon dataset.
No full textProgress Code: completedStatement: Data quality information for each feature is included in the attributes table.
The dates provided in temporal coverage are approximate only.This record describes GIS polygon data (a shapefile) representing the boundaries of Antarctic Specially Protected Areas (ASPAs) and an Antarctic Specially Managed Area (ASMA) in the Australian Antarctic Territory for which Australia was the proponent or co-proponent. Also included are the boundaries of ASPA 168, for which China was the proponent, and ASPA 127 for which Russia was the proponent. <br/><br/>Updated ASPA boundaries were sourced from Terauds, A. (2019) Antarctic Specially Protected Areas (Points and Polygons) 2018 Update, Ver. 1, Australian Antarctic Data Centre. Full details can be found at: http://dx.doi.org/doi:10.26179/5c1b10c534c19<br/><br/>The following is a list of the ASPAs and ASMA in this dataset:<br/>ASPA 101 Taylor Rookery<br/>ASPA 102 Rookery Islands<br/>ASPA 103 Ardery Island and Odbert Island<br/>ASPA 127 Haswell Island<br/>ASPA 135 North-east Bailey Peninsula<br/>ASPA 136 Clark Peninsula<br/>ASPA 143 Marine Plain<br/>ASPA 160 Frazier Islands<br/>ASPA 162 Mawson's Huts<br/>ASPA 164 Scullin and Murray Monoliths<br/>ASPA 167 Hawker Island<br/>ASPA 168 Mt Harding<br/>ASPA 169 Amanda Bay<br/>ASPA 174 Stornes<br/>ASMA 6 Larsemann Hills<br/><br/>GIS data representing these boundaries, as well as the boundaries of other ASPAs and ASMAs, is also available from the Antarctic Treaty Secretariat's Antarctic Protected Areas Database (https://www.ats.aq/devph/en/apa-database).<br/><br/>A minor data update (version 4) was made on 2023-02-28
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
No full text“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Pure point measures with sparse support and sparse Fourier–Bohr support
No full textBaake M, Strungaru N, Terauds V. Pure point measures with sparse support and sparse Fourier–Bohr support. Transactions of the London Mathematical Society. 2020;7(1):1-32.Fourier‐transformable Radon measures are called doubly sparse when both the measure and its transform are pure point measures with sparse support. Their structure is reasonably well understood in Euclidean space, based on the use of tempered distributions. Here, we extend the theory to second countable, locally compact Abelian groups, where we can employ general cut and project schemes and the structure of weighted model combs, along with the theory of almost periodic measures. In particular, for measures with Meyer set support, we characterise sparseness of the Fourier–Bohr spectrum via conditions of crystallographic type, and derive representations of the measures in terms of trigonometric polynomials. More generally, we analyse positive definite, doubly sparse measures in a natural cut and project setting, which results in a Poisson summation type formula
Antarctic Specially Protected Areas (Points and Polygons) 2024 Update
No full textProgress Code: completedStatement: See the readme file in the dataset download for more information.<b>Purpose</b><br/>Provide the most accurate spatial layer of Antarctic Specially Protected Areas.This is the 2024 data update. For the 2018 update, please see the link below.<br/><br/>Starting with the most recently updated polygon shapefile of ASPAs (Terauds and Lee 2016), which contained some minor improvement on the original ASPA spatial layer first made publicly available in 2011, we first cross-checked the location of ASPA polygons with the spatially explicit locations provided in the ASPAs Management Plans. Once polygons were aligned with the Management Plans, we then georeferenced the maps provided in the management plans to check the ASPA boundaries in relation to known landscape features, In some cases, there was a lack of concurrence between co-ordinates, PDF map, coastline, rock layer or Google Earth. In these cases the following protocol was followed: snap to coordinates (unless clearly wrong), otherwise align to rock outcrop layer based on the PDF map, otherwise align to coastline. Full details of the updates made to each ASPA can be found in the README file accompanying the updated layer.<br/><br/>The downloadable dataset contains a folder with a points dataset, a folder with a polygon dataset, and a word document with further information.<br/><br/>For ASPAS and ASMAs within the AAT please see: https://data.aad.gov.au/metadata/aspas_asmas_aat - doi:10.4225/15/5a963cbd74a3a.<br/><br/>For the 2018 data update please see: https://data.aad.gov.au/metadata/AAS_4296_Updated_ASPAs_2018 - doi:10.26179/5c1b10c534c19
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Environmental drivers of Antarctic biodiversity in ice-free areas at a continental scale
No full textProgress Code: completed<b>Purpose</b><br/>Such information is vital for progressing protecting area designation in terrestrial Antarctica and is a direct contribution toward underpinning a systematic environmental-geographical framework as called for by Annex V of the Protocol on Environmental Protection to the Antarctic Treaty.The many long-held hypotheses on the environmental variables driving distributions and abundance of terrestrial biodiversity in Antarctica are rarely underpinned by empirical tests, particularly at the continental scale. Here we use sophisticated species distribution models, with the most comprehensive compilation of biodiversity data, to identify the key environmental drivers of Antarctic biodiversity and predict the likelihood of biodiversity occurrence across the continent.<br/><br/>We included 25 taxonomic groups, ranging from lichens to penguins, finding that elevation, slope, precipitation, and rugosity are significant drivers for all taxa. Other drivers, such as total degree days and distance to coast likewise had significant relationships, but responses varied between and within taxonomic groups. Our results also provide some of the first measures of species richness and empirically derived biodiversity hotspots in ice free areas across the continent.<br/><br/>The model:<br/><br/>We use area interaction processes (a member of the family of Gibbs processes), and an extension of the inhomogeneous Poisson point process (the most well-known and used point process) to model and predict species occurrences across the continent. One the main advantages of this point process is that it can account for inter-point interaction (spatial dependence). This interpoint interaction allows us to relax the assumption of independence between points, often a commonly used but unrealistic assumption of species distribution.<br/><br/>Environmental Predictors:<br/><br/>The environmental and climate data used as predictors included: distance to coast (coast), elevation (elev), an indicator of snow melt (melt), terrain roughness (rugosity), slope, solar radiation (solrad), total degree days (dd), total precipitation (precip), distance to nearest geothermal site (geothermal), mean cloud cover (cloud), mean wind speed (wind), and distance to nearest human infrastructure (base). Base, geothermal and coast covariates were derived from either point or polygon data sets which gave the locations of these features. In order to include these data in the area interaction process model, they were converted to raster data, with each cell containing the euclidean distance of that cell to the nearest point in the feature data set. The distance to coast map was generated from the SCAR Antarctic Digital Database medium resolution coastline dataset. All other covariates were interpolated or downscaled to 1 km raster spatial layers (covering the extent of ice-free Antarctica). Fixing the base covariate at a certain level during predictions was used to account for sampling bias. These spatial layers were chosen as predictors they cover most abiotic features that are believed to be relevant factors in determining species distribution (see Introduction) and were available at a continental scale for ice-free areas.<br/><br/>A geotiff of each environmental predictor used in the models (at 1km resolution ) is provided in Ice_Free_Drivers_PREDICTORS folder.<br/><br/>Model outputs<br/><br/>We were able to model 25 taxonomic groups, ranging from lichens to penguins, finding that elevation, slope, precipitation, and rugosity are significant drivers for all taxa. Other drivers, such as total degree days and distance to coast likewise had significant relationships, but responses varied between and within taxonomic groups. Our results also provide some of the first measures of species richness and empirically derived biodiversity hotspots in ice free areas across the continent.<br/><br/>For each species two predictive outputs were generated (at 2km scale).<br/><br/>1) Conditional relative intensity all predictors – the predicted number of presences of a taxa per unit area (with all predictors from the best model) provided in the folder: Ice_Free_biodiversity_drivers_OUTPUTS_ALL<br/><br/>2) Conditional relative intensity (no base) - the predicted number of presences of a taxa per unit area (with all predictors from the best model but not “distance to base”) provided in the folder: Ice_Free_biodiversity_drivers_OUTPUTS_ENV<br/><br/>The habitat suitability maps were used as an input to the Antarctic Ecosystem Inventory (Toth et al. in review). The interpolated layers (for each taxa) that were used in this study are provided in the folder - Habitat_Suitability_Inputs_for_Antarctic_Ecosystem_Inventory.<br/><br/>References<br/>Tóth, A.B., Terauds. A., Chown S.L., Hughes K.A., Convey P., Hodgson D.A., Cowan D.A., Gibson, J., Leihy. R.I., Murray N.J., Robinson, S.A., Shaw, J.D., Stark J.S., Stevens M.I., van den Hoff, J., Wasley J. and Keith, D.A. (in review). The Antarctic Ecosystem Inventory: A classification, descriptions and map of Antarctica’s ice-free lands. Submitted to Scientific Data (June 2024)
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