1,721,033 research outputs found
Reindeer movement, habitat preference and road permeability model data
No full textReindeer movement, habitat preference and road permeability model dataOverview: GPS data for wild reindeer were collected within a larger project in Rondane-South and Rondane-North wild reindeer management areas, a mountainous region of central-southern Norway (10 46’ E, 61 38’ N). We used locations collected from five adult female reindeer every three hours between 1 June to 29 September 2012 (N = 973, 960, 871, 971 and 974 locations, respectively). Around 60% of the area is located above tree-line between 1000 and 1500 m, and is dominated by rocks and lichen heath; lower elevations (above 500 m) are characterised by a mix of meadows, grass and willow communities. The area occupied by the reindeer used in this study extends between ca. 400 and 1900 m, and is fragmented by public and private roads (access to the latter is often restricted, so is characterised by lower traffic volumes than the former).The data we provide can be used in conjunction with the R code in the Supplementary Materials of the published paper to fit the models presented in that paper.The zip file contains five R data files corresponding to five reindeer. The files are named "data_" followed by one of the ID numbers of the reindeer (11264, 11265, 9397, 7625, 9406). Each R data (*.RData) file contains an R list object called "fitdata" in R, composed of the following data structures:Definitions:N_s = the number of movement "steps" (sequential telemetry locations) for a reindeerN_a = the number of grid cells in the landscape (all cells falling within 5km of any telemetry point.(i) fitdataavailhabA matrix (dimensions: N_a rows, 4 columns) of 'available' habitat types (the habitat value at each raster grid cell in the spatial domain of the analysis). The four columns are: elevation (km), elevation^2, distance to road (km), distance to road^2.(iii) fitdatause.xyA matrix (dimensions N_s + 1 rows, 2 columns) of the x and y coordinates (km) of the reindeer telemetry locations. This matrix is used to precalculate a large distance matrix ("dm") representing the distances among all use and available points (dimensions: N_s rows, N_a columns). Although this 'dm' matrix is very large, pre-calculating the distance matrix greatly improves the speed of model fitting by eliminating the need to repeatedly re-calculate distances. (iv) fitdatastepdstA vector of length N_s - 1 representing the Euclidean distance (km) between consecutive telemetry locations.(v) fitdatause.rdznA vector of length N_s containing arbitrary ID numbers corresponding to regions of space that are bounded by a network of roads. This could be conceptualised in GIS terms as forming polygons based on the lines contained in the road network, and assigning an arbitrary unique ID to each of those polygons. Road crossings are indicated by a change in the ID number in this time series. The reason for using this approach to identifying road crossings is computational efficiency, and to avoid the assumption that the straight-line connected two consecutive telemetry locations can be used to calculate road crossings. For example, if this straight line 'clips' a bend in a road then that would indicate 2 crossings, while the animal may in fact have remained in the same region without ever crossing a road.(vii) fitdata$avail.rdznA vector of length N_a containing arbitrary ID numbers corresponding to regions of space that are bounded by a network of roads (see vi) for each grid cell in the availability sample.Telemetry and habitat data for 5 reindeer over 1 summer; R data objects
Koala survival analysis and Leslie matrices
No full textThese data relate to survival analysis and population simulations.The following three components are included:Joey survival outcomes (joey_survival.csv)Fields:1. joeyid: a unique identifier for the joey (each row is a unique joey)2. dob: Estimated date of birth3. date: Date of the current observation4. daysdob: The number of days between the date of the observation and the date of birth5. daysorigin: The number of days between the date of the observation and the origin date of the project (2013-03-18)6. dead: A binary field indicating whether the joey was dead (1) or alive (0) at the date of the observation7. motherdead: A categorical variable indicating the status of the joey's mother at the observation date: (0 = mother alive; 1 = mother dead, so joey died; 2 = joey died independent of mother)Adult koala survival outcomes (koala_survival.csv)Each record in this table corresponds to an interval of time, and the history of a single individual may span multiple records. Note that the time intervals may not be contiguous as any interval in which the koala remained in care and, therefore, not exposed to threats in the wild, has been removed. The interval period is reported with respect to the origin date of the project, and with respect to the estimated date of birth of the koala.Fields:1. starttime: The start of the interval, measured as the number of days since the origin date of the project (2013-03-18)2. stoptime: The end of the interval, measured as the number of days since the origin date of the project (2013-03-18)3. startage: The age of the koala at the start of the interval, measured as the number of days since the estimated date of birth4. stopage: The age of the koala at the end of the interval, measured as the number of days since the estimated date of birth5. event: The status of the koala at the end of the interval (0 = alive, 1 = dead)6. uuid: A unique identified for the koala7. sex: The sex of the koala8. trans: A binary variable indicating whether the koala was translocated (1) or not (0).Leslie matrices (leslie_matrices.RData)This R (www.r-project.org) data file contains four Leslie matrices, corresponding to each of the four years of the project. See the Supplementary Material for a description of these matrices. Specifically, when you load this data object using:load(file="leslie_matrices.RData")an object called "leslie" will be created in R. This is a list of four matrices. So the matrix for each of the four years can be accessed as follows:leslie[[1]]leslie[[2]]leslie[[3]]leslie[[4]
Australia's threatened species, life history characteristics, and threatening processes
No full textFields in the dataset:GENUS_SPECIES_SUBSPP: The genus and species names, with a subspecies name included if relevant to the EPBC listing, and occasionally with a text note in parentheses to clarify further details of the listing (e.g. "Carcharias taurus (east coast population"). Occasionally synonyms will be represented using an equals sign (e.g. "Thalassarche eremita = Thalassarche cauta eremita").GENUS_SPECIES: A version of the genus and species name that lacks synonyms or other comments.COMMON_NAME: A comma delimited list of common names associated with each species.STATUS_EPBC: The EPBC status at the time of construction of the database (2017).STATUS_IUCN: The IUCN status at the time of construction of the database (2017).PHYLUM, CLASS: The phylum and class of the species.COMMON_GROUP: A categorisation of species into general groups that may be useful for analysis and communications (e.g. birds, mammals, reptiles, fish, etc).BODYFORM: This field is only implemented for reptiles for the purpose of distinguishing between turtles and the skink and snake body forms. ADULT_BODY_MASS_RANGE_G: Notes on the range of adult body sizes for each species (in grams), distinguishing among males and females if that data is available.ADULT_BODY_MASS_MEAN_G: An estimate of the mean adult body size of each species (in grams). This field is intended to be suitable for quantitative analysis.ADULT_BODY_MASS_SOURCE: Notes on the sources of information for body size data.BODY_LENGTH_RANGE_MM: Notes on the range of adult body length for each species (mm), distinguishing among males and females if that data is available.MEAN_BODY_LENGTH_MM: An estimate of the mean adult body length for each species (mm). This field is intended to be suitable for quantitative analysis.BODY_LENGTH_SOURCE: Notes on the sources of information for body length data.GEN_TIME_YRS: An estimate of the generation time for each species. This may be represented as a range, and it may distinguish among makes and females. Some interpretation of these data would be required prior to statistical analysis.GEN_TIME_SOURCE: Notes on the sources of information for the generation time data.N_OFFSPRING_YR_LITTER: Notes on the number of offspring in each breeding event/cycle. Methods of reproduction vary widely among classes, so these data may have most relevance for within-class comparisons among species. Some interpretation of these data would be required prior to statistical analysis.N_OFFSPRING_SOURCE: Notes on the sources of information for the number of offspring data.EXTENT_OCC_KM2: Notes on the extent of occurrence of the species (km^2). Where an explicit area estimate is not available, notes related to the geographic distribution of the species are included. Extent of occurrence estimates are particularly problematic for wide-ranging species (e.g. sharks, whales, long-distance migrant birds).AREA_OCC_KM2: Notes on the area off occupancy of the species (km^2)AREA_OCC_SOURCE: Notes on the sources of information for the area of occupancy data.POP_SIZE: Notes on any estimates of the remaining population size.POP_SIZE_SOURCE: Notes on the sources of information for the population size data.LIFESPAN_YEARS: Notes on the lifespan of each species, differentiating between males and females where those data are available.LIFESPAN_SOURCE: Notes on the sources of information for the lifespan data.THREATS_EPBC: A semi-colon delimited list of threat codes associated with each species. (Refer to the Allek et al 2018 paper for details on the threat codes).THREATS_EPBC_POSS: A semi-colon delimited list of additional threat codes that were indicated as potentially being associated with each species.THREATS_IUCN: A semi-colon delimited list of threat codes associated with the IUCN records on each species.N_PAP_SPP: The number of peer reviewed papers making reference to each species, excluding conference proceedings and reviews (recorded 2016 using Web of Science).STATES: A semi-colon delimited list of the Australian states or territories within which the species occurs. In the case of isolated remote islands, the island name is included instead.Other notes: The records are not sorted in any particular order. Cells are encoded with NA to indicate no data availability. For some species little or no data on life history parameters are available, and for some other the only source of data comes from captive populations. The dataset is included in both Excel and plain-text (comma delimited text file) formats. There is no difference in the data content of these two files
Global assessment of ecoregion intactness
No full textThis datasets consists of: (1) intactness scores for the world's terrestrial ecoregions in 1993 and 2009, recorded as a comma delimited text file, and as attribute fields in a terrestrial ecoregion polygon shapefile (Ecoregions2017_intactness.shp); and (2) two raster datasets representing derived intactness metrics (Q' and delta Q') for terrestrial areas at a 1 km resolution. To reduce file sizes the values in both rasters are multiplied by 1,000 and rounded to integers. Users should divide the values in each raster by 1,000 to recover the original scale of these variables. All spatial data uses the Mollweide projection.The spatial ecoregion data is sourced from Dinerstein et al 2017 BioScience DOI: 10.1093/biosci/bix014, with data available for download at: https://ecoregions2017.appspot.comFields in the tabular data include: Q1993_LL, Q1993, Q1993_UL, Q2009_LL, Q2009, Q2009_UL: intactess metric for 1993 and 2009, including estimates of the lower and upper limits (LL and UL respectively) based on alternative parameterisations of the distance scaling parameter. The fields Q1993 and Q2009 are likely to be of greatest relevance to most users.NCELLS: the number of cells used in the intactness calculation.PRHAB1993, PRHAB2009: estimates of the proportion of habitat in 1993 and 2009 using a binary interpretation of habitat (used to evaluate the properties of the intactness metric relative to more traditional measures).PLOTCAT: Nine categories of change in ecoregion intactness:2, 1, 3: High proportion of habitat with degrading, stable or increasing intactness respectively5, 4, 6: Moderate proportion of habitat with degrading, stable or increasing intactness respectively8, 7, 9: Low proportion of habitat with degrading, stable or increasing intactness respectivelySee the Beyer et al 2019 Conservation Letters paper for further details
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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
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