O Herbário do Museu Nacional (R) do Departamento de Botânica do Museu Nacional (Universidade Federal do Rio de Janeiro) foi fundado em 1831, sendo o primeiro herbário do Brasil e um dos maiores da América Latina. O herbário reúne um total de aproximadamente 600.000 espécimes. A coleção de tipos nomenclaturais do herbário R possui hoje um total de 5.600 exemplares organizados em armários compactadores e em ordem alfabética, está em bom estado de preservação, e integra o Projeto "Global Plants Initiative" da Andrew W. Mellon Foundation. O acervo é oriundo de coletas realizadas, principalmente, por importantes naturalistas do século XIX e início do século XX, tais como E. Ule, G.O.A. Malme, G. Gardner, A.M.F. Glaziou, J.F. Widgren, A.F. Regnell, A.C. Brade, A. Ducke, C.A.W. Schwacke, F.C. Hoehne, Alvaro da Silveira, F. Freire-Allemão, R. Reitz, dentre outros. É uma coleção ativa e em contínuo crescimento.

Replication pack, FSE2018 submission #164:
------------------------------------------

**Working title:** Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: 
A Case Study of the PyPI Ecosystem

**Note:** link to data artifacts is already included in the paper. 
Link to the code will be included in the Camera Ready version as well.


Content description
===================

- **ghd-0.1.0.zip** - the code archive. This code produces the dataset files 
 described below
- **settings.py** - settings template for the code archive.
- **dataset_minimal_Jan_2018.zip** - the minimally sufficient version of the dataset.
 This dataset only includes stats aggregated by the ecosystem (PyPI)
- **dataset_full_Jan_2018.tgz** - full version of the dataset, including project-level
 statistics. It is ~34Gb unpacked. This dataset still doesn't include PyPI packages
 themselves, which take around 2TB.
- **build_model.r, helpers.r** - R files to process the survival data 
  (`survival_data.csv` in **dataset_minimal_Jan_2018.zip**, 
  `common.cache/survival_data.pypi_2008_2017-12_6.csv` in 
  **dataset_full_Jan_2018.tgz**)
- **Interview protocol.pdf** - approximate protocol used for semistructured interviews.
- LICENSE - text of GPL v3, under which this dataset is published
- INSTALL.md - replication guide (~2 pages)

Replication guide
=================

Step 0 - prerequisites
----------------------

- Unix-compatible OS (Linux or OS X)
- Python interpreter (2.7 was used; Python 3 compatibility is highly likely)
- R 3.4 or higher (3.4.4 was used, 3.2 is known to be incompatible)

Depending on detalization level (see Step 2 for more details):
- up to 2Tb of disk space (see Step 2 detalization levels)
- at least 16Gb of RAM (64 preferable)
- few hours to few month of processing time

Step 1 - software
----------------

- unpack **ghd-0.1.0.zip**, or clone from gitlab:

   git clone https://gitlab.com/user2589/ghd.git
   git checkout 0.1.0
 
 `cd` into the extracted folder. 
 All commands below assume it as a current directory.
  
- copy `settings.py` into the extracted folder. Edit the file:
  * set `DATASET_PATH` to some newly created folder path
  * add at least one GitHub API token to `SCRAPER_GITHUB_API_TOKENS` 
- install docker. For Ubuntu Linux, the command is 
  `sudo apt-get install docker-compose`
- install libarchive and headers: `sudo apt-get install libarchive-dev`
- (optional) to replicate on NPM, install yajl: `sudo apt-get install yajl-tools`
 Without this dependency, you might get an error on the next step, 
 but it's safe to ignore.
- install Python libraries: `pip install --user -r requirements.txt` . 
- disable all APIs except GitHub (Bitbucket and Gitlab support were
 not yet implemented when this study was in progress): edit
 `scraper/init.py`, comment out everything except GitHub support
 in `PROVIDERS`.

Step 2 - obtaining the dataset
-----------------------------

The ultimate goal of this step is to get output of the Python function 
`common.utils.survival_data()` and save it into a CSV file:

  # copy and paste into a Python console
  from common import utils
  survival_data = utils.survival_data('pypi', '2008', smoothing=6)
  survival_data.to_csv('survival_data.csv')

Since full replication will take several months, here are some ways to speedup
the process:

####Option 2.a, difficulty level: easiest

Just use the precomputed data. Step 1 is not necessary under this scenario.

- extract **dataset_minimal_Jan_2018.zip**
- get `survival_data.csv`, go to the next step

####Option 2.b, difficulty level: easy

Use precomputed longitudinal feature values to build the final table.
The whole process will take 15..30 minutes.

- create a folder `

{# General information# The script runs with R (Version 3.1.1; 2014-07-10) and packages plyr (Version 1.8.1), XLConnect (Version 0.2-9), utilsMPIO (Version 0.0.25), sp (Version 1.0-15), rgdal (Version 0.8-16), tools (Version 3.1.1) and lattice (Version 0.20-29)# --------------------------------------------------------------------------------------------------------# Questions can be directed to: Martin Bulla (bulla.mar@gmail.com)# -------------------------------------------------------------------------------------------------------- # Data collection and how the individual variables were derived is described in: #Steiger, S.S., et al., When the sun never sets: diverse activity rhythms under continuous daylight in free-living arctic-breeding birds. Proceedings of the Royal Society B: Biological Sciences, 2013. 280(1764): p. 20131016-20131016. # Dale, J., et al., The effects of life history and sexual selection on male and female plumage colouration. Nature, 2015. # Data are available as Rdata file # Missing values are NA. # --------------------------------------------------------------------------------------------------------# For better readability the subsections of the script can be collapsed # --------------------------------------------------------------------------------------------------------}{# Description of the method # 1 - data are visualized in an interactive actogram with time of day on x-axis and one panel for each day of data # 2 - red rectangle indicates the active field, clicking with the mouse in that field on the depicted light signal generates a data point that is automatically (via custom made function) saved in the csv file. For this data extraction I recommend, to click always on the bottom line of the red rectangle, as there is always data available due to a dummy variable ("lin") that creates continuous data at the bottom of the active panel. The data are captured only if greenish vertical bar appears and if new line of data appears in R console). # 3 - to extract incubation bouts, first click in the new plot has to be start of incubation, then next click depict end of incubation and the click on the same stop start of the incubation for the other sex. If the end and start of incubation are at different times, the data will be still extracted, but the sex, logger and bird_ID will be wrong. These need to be changed manually in the csv file. Similarly, the first bout for a given plot will be always assigned to male (if no data are present in the csv file) or based on previous data. Hence, whenever a data from a new plot are extracted, at a first mouse click it is worth checking whether the sex, logger and bird_ID information is correct and if not adjust it manually. # 4 - if all information from one day (panel) is extracted, right-click on the plot and choose "stop". This will activate the following day (panel) for extraction. # 5 - If you wish to end extraction before going through all the rectangles, just press "escape". }{# Annotations of data-files from turnstone_2009_Barrow_nest-t401_transmitter.RData dfr-- contains raw data on signal strength from radio tag attached to the rump of female and male, and information about when the birds where captured and incubation stage of the nest1. who: identifies whether the recording refers to female, male, capture or start of hatching2. datetime_: date and time of each recording3. logger: unique identity of the radio tag 4. signal_: signal strength of the radio tag5. sex: sex of the bird (f = female, m = male)6. nest: unique identity of the nest7. day: datetime_ variable truncated to year-month-day format8. time: time of day in hours9. datetime_utc: date and time of each recording, but in UTC time10. cols: colors assigned to "who"--------------------------------------------------------------------------------------------------------m-- contains metadata for a given nest1. sp: identifies species (RUTU = Ruddy turnstone)2. nest: unique identity of the nest3. year_: year of observation4. IDfemale: unique identity of the female5. IDmale: unique identity of the male6. lat: latitude coordinate of the nest7. lon: longitude coordinate of the nest8. hatch_start: date and time when the hatching of the eggs started 9. scinam: scientific name of the species10. breeding_site: unique identity of the breeding site (barr = Barrow, Alaska)11. logger: type of device used to record incubation (IT - radio tag)12. sampling: mean incubation sampling interval in seconds--------------------------------------------------------------------------------------------------------s-- contains metadata for the incubating parents1. year_: year of capture2. species: identifies species (RUTU = Ruddy turnstone)3. author: identifies the author who measured the bird4. nest: unique identity of the nest5. caught_date_time: date and time when the bird was captured6. recapture: was the bird capture before? (0 - no, 1 - yes)7. sex: sex of the bird (f = female, m = male)8. bird_ID: unique identity of the bird9. logger: unique identity of the radio tag --------------------------------------------------------------------------------------------------------}

Overview

Data points present in this dataset were obtained following the subsequent steps: To assess the secretion efficiency of the constructs, 96 colonies from the selection plates were evaluated using the workflow presented in Figure Workflow. We picked transformed colonies and cultured in 400 μL TAP medium for 7 days in Deep-well plates (Corning Axygen®, No.: PDW500CS, Thermo Fisher Scientific Inc., Waltham, MA), covered with Breathe-Easy® (Sigma-Aldrich®). Cultivation was performed on a rotary shaker, set to 150 rpm, under constant illumination (50 μmol photons/m²s). Then 100 μL sample were transferred clear bottom 96-well plate (Corning Costar, Tewksbury, MA, USA) and fluorescence was measured using an Infinite® M200 PRO plate reader (Tecan, Männedorf, Switzerland). Fluorescence was measured at excitation 575/9 nm and emission 608/20 nm. Supernatant samples were obtained by spinning Deep-well plates at 3000 × g for 10 min and transferring 100 μL from each well to the clear bottom 96-well plate (Corning Costar, Tewksbury, MA, USA), followed by fluorescence measurement. To compare the constructs, R Statistic version 3.3.3 was used to perform one-way ANOVA (with Tukey's test), and to test statistical hypotheses, the significance level was set at 0.05. Graphs were generated in RStudio v1.0.136. The codes are deposit herein.

Info

ANOVA_Turkey_Sub.R -> code for ANOVA analysis in R statistic 3.3.3

barplot_R.R -> code to generate bar plot in R statistic 3.3.3

boxplotv2.R -> code to generate boxplot in R statistic 3.3.3

pRFU_+_bk.csv -> relative supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

sup_+_bl.csv -> supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

sup_raw.csv -> supernatant mCherry fluorescence dataset of 96 colonies for each construct.

who_+_bl2.csv -> whole culture mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

who_raw.csv -> whole culture mCherry fluorescence dataset of 96 colonies for each construct.

who_+_Chlo.csv -> whole culture chlorophyll fluorescence dataset of 96 colonies for each construct.

Anova_Output_Summary_Guide.pdf -> Explain the ANOVA files content

ANOVA_pRFU_+_bk.doc -> ANOVA of relative supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

ANOVA_sup_+_bk.doc -> ANOVA of supernatant mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

ANOVA_who_+_bk.doc -> ANOVA of whole culture mCherry fluorescence dataset of positive colonies, blanked with parental wild-type cc1690 cell of Chlamydomonas reinhardtii

ANOVA_Chlo.doc -> ANOVA of whole culture chlorophyll fluorescence of all constructs, plus average and standard deviation values.

Consider citing our work.

Molino JVD, de Carvalho JCM, Mayfield SP (2018) Comparison of secretory signal peptides for heterologous protein expression in microalgae: Expanding the secretion portfolio for Chlamydomonas reinhardtii. PLoS ONE 13(2): e0192433. https://doi.org/10.1371/journal. pone.0192433

Non-human primates respond to the death of a conspecific in diverse ways, some of which may present phylogenetic continuity with human thanatological responses. Of these responses, infant corpse carrying by mothers (ICC) is the most frequently reported. Despite its prevalence, quantitative analyses of this behaviour are scarce and inconclusive. We compiled a database of 409 published cases across 50 different primate species of mothers’ responses to their infants’ deaths and used Bayesian phylogenetic regressions with an information-theoretic approach to test hypotheses proposed to explain between- and within-species variation in ICC. We found that ICC was more likely when the infant’s death was non-traumatic (e.g. illness) versus traumatic (e.g. infanticide), and when the mother was younger. These results support the death detection hypothesis, which proposes that ICC occurs when there are fewer contextual or sensory cues indicating death. Such an interpretation suggests that primates are able to attain an awareness of death. In addition, when carried, infant age affected ICC duration, with longer ICC observed for younger infants. This result suggests that ICC is a by-product of strong selection on maternal behaviour. The findings are discussed in the context of the evolution of emotion, and implications for evolutionary thanatology are proposed.

Methods We searched the scientific literature for cases of primate mothers responding to the corpse of their dead infant. Cases were cross-referenced using three published reviews [5,7,28]. We included only events in which there was enough opportunity for the mother to carry the corpse [5]. Specifically, we recorded a case of ‘corpse not carried’ if the mother was in the vicinity of the infant when the death occurred and the corpse was not consumed or monopolized by other individuals or removed by observers after the death, but the mother did not carry it. Additionally, we classified attempted but unsuccessful lifting (e.g. [30,31]) as ‘corpse not carried’ to avoid interpretation of underlying motivation. Our definition thus does not differentiate between mothers who are unable or unwilling to carry their young. For each case, we recorded 10 variables where possible: (1) the species; (2) the site where the case was reported; (3) whether the corpse was carried or not; if carried, (4) the carry duration (in days); the mother’s (5) parity, (6) age and (7) rank; (8) the infant’s age; (9) the cause of the death; and (10) the living condition (wild, provisioned, laboratory or captive). In cases where the exact duration was not known, we used the minimum (where > N) or maximum (when

The linked bookdown contains the notes and most exercises for a course on data analysis techniques in hydrology using the programming language R. The material will be updated each time the course is taught. If new topics are added, the topics they replace will remain, in case they are useful to others.

I hope these materials can be a resource to those teaching themselves R for hydrologic analysis and/or for instructors who may want to use a lesson or two or the entire course. At the top of each chapter there is a link to a github repository. In each repository is the code that produces each chapter and a version where the code chunks within it are blank. These repositories are all template repositories, so you can easily copy them to your own github space by clicking Use This Template on the repo page.

In my class, I work through the each document, live coding with students following along.Typically I ask students to watch as I code and explain the chunk and then replicate it on their computer. Depending on the lesson, I will ask students to try some of the chunks before I show them the code as an in-class activity. Some chunks are explicitly designed for this purpose and are typically labeled a “challenge.”

Chapters called ACTIVITY are either homework or class-period-long in-class activities. The code chunks in these are therefore blank. If you would like a key for any of these, please just send me an email.

If you have questions, suggestions, or would like activity answer keys, etc. please email me at jpgannon at vt.edu

Finally, if you use this resource, please fill out the survey on the first page of the bookdown (https://forms.gle/6Zcntzvr1wZZUh6S7). This will help me get an idea of how people are using this resource, how I might improve it, and whether or not I should continue to update it.

O acervo está sendo organizado em armários compactados, em ordem alfabética de famílias Consta do Index Herbariorum com a sigla R, e estima-se que a coleção possua cerca de 550.000 exemplares, sendo 95% destes de plantas vasculares. Além dos números da coleção, o Herbário apresenta grande importância histórica por ter como depositários Glaziou, Freire Allemão, Brade, Hoehne, Lutz, Riedel, Schwacke, Sellow, e o próprio Imperador Dom Pedro II. No universo de espécimes coletados, merecem destaque às coleções procedentes da Amazônia Legal, Expedições do Marechal Rondon, Alberto Sampaio, Polo Noroeste, Alto Xingu, Fernando de Noronha, Parque Nacional da Restinga de Jurubatiba, dentre outras. A coleção de tipos nomenclaturais possui um total de 5.600 exemplares e faz parte do Projeto "Latin American Plants Iniciative" (LAPI) da Fundação Mellon.

The Aboriginal Sites Decision Support Tool ASDST extends the Aboriginal Heritage Information Management System (AHIMS) by illustrating the potential distribution of site features recorded in AHIMS.\r ASDST was first developed in 2012 by the Office of Environment and Heritage (OEH) to support landscape planning of Aboriginal Heritage. The Tool produces a suite of raster GIS modelled outputs and is held in Esri GRID format. The first suite was published in 2016 as Version 7 at 100m resolution and in Lamberts Conic Conformal Projection (LCC). The current Version 7.5 was produced by the now Department of Planning, Industry and Environment (DPIE) in 2020 at 50m resolution in Geographic Coordinate System (GCS). Each layer covers the extent of NSW. \r \r The suite of layers includes separate predictive layers for different Aboriginal site feature types. The feature codes used in layer naming conventions are:\r \r * ALL = model for all feature types combined \r * AFT = predicted likelihood for stone artefacts \r * ART = predicted likelihood for rock art \r * BUR = predicted likelihood of burials \r * ETM = predicted likelihood of western mounds and shell \r * GDG = predicted likelihood of grinding grooves \r * HTH = predicted likelihood of hearths \r * SHL = predicted likelihood of coastal middens \r * STQ = predicted likelihood of stone quarries and \r * TRE = predicted likelihood of scarred trees. \r \r The feature models have been derived in two forms:\r \r * The first form (“p1750XXX” where XXX denotes three letter feature code) predicts likelihood of feature distribution prior to European colonisation of NSW. \r \r * The second form (“curr_XXX” where XXX denotes three letter feature code) predicts feature likelihood in the current landscape. \r \r For both sets of feature likelihood layers, cell values range from 0 – 1000, where 0 indicates low likelihood and 1000 is high likelihood. \r \r Please note the scale is likelihood and NOT probability. Likelihood is defined as a relative measure indicating the likelihood that a grid cell may contain the feature of interest relative to all other cells in the layer. \r \r Additionally, there are other derived products as part of the suite. These are: \r \r * drvd_imp = which is a model of accumulated impacts, derived by summing the difference between the pre colonisation and current version of all feature models. Cell values range from 0 – 1000, where 1000 is a high accumulated impact.\r \r * drvd_rel = which is a model of the reliability of predictions based on an environmental distance algorithm that looks at recorded site density across the variables used in the models.\r \r * drvd_srv = which is a survey priority map, which considers model reliability (data gap), current likelihood and accumulated impact. Cell values range from 0 – 1000 where 1000 indicates highest survey priority relative to the rest of the layer.\r \r For more details see the technical reference on the ASDST website.\r \r NB. Old layers with a suffix of “_v7” indicate they are part of ASDST Version 7 produced in 2016. The current models (Version 7.5) do not contain a version number in their name and will continue to be named generically in future versions for seamless access.\r \r Updates applied to ASDST version 7.5\r \r For all ASDST 7.5 data sets, the resolution was increased from a 100m cell to a 50m cell. All data sets were clipped and cleaned to a refined coastal mask. Cell gaps in the mask were filled using a Nibble algorithm. The pre-settlement data sets were derived by resampling the version 7 pre-settlement data sets to 50m cell size. The present-day data sets were derived from the version 7.5 pre-settlement layers and 2017-18 land-use mapping and applying the same version 7 parameters for estimating the preservation of each feature type on each land-use. For version 7.5, the model reliability data set was derived by resampling the version 7 data set to 50m cell size. Accumulated impact and survey priority version 7.5 data sets were derived by applying the version 7 processing algorithm but substituting the version 7.5 pre-settlement and present-day ASDST models.\r

In this MANUAL FOR VISIBILITY GRAPHS MODELING USING R-STUDIO We will first present basic notions that will allow the understanding of the mapping process, then we'll show the computational idea. Finally, let's work with the R scripts inside the RStudio, exploring pseudo-random series, Brownian motion series, periodic series, series of fibonacci and series of audio signals. We'll show you: 1) how to generate time series in RS Studio and later turn them into visibility graphs. 2) how to import time series allocated in a directory, turning them into visibility graphs. 3) how to visualize networks using three types of algorithms, followed by calculation and visualization of the main properties of complex networks. About the codes included The 3 codes included generates visibility graphs of series generated by RStudio functions. This code also calculates some metrics for complex networks, generates the graph plot and its degree distribution, shows the plot of the series and its histogram.

Abstract The dataset was derived by the Bioregional Assessment Programme from multiple datasets. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. The dataset consists of an excel spreadsheet and shapefile representing the locations of simulation nodes used in the AWRA-R model. Some of the nodes correspond to gauging station locations or dam …Show full descriptionAbstract The dataset was derived by the Bioregional Assessment Programme from multiple datasets. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement. The dataset consists of an excel spreadsheet and shapefile representing the locations of simulation nodes used in the AWRA-R model. Some of the nodes correspond to gauging station locations or dam locations whereas other locations represent river confluences or catchment outlets which have no gauging. These are marked as "Dummy". Purpose Locations are used as pour points in oder to define reach areas for river system modelling. Dataset History Subset of data for the Hunter that was extracted from the Bureau of Meteorology's hydstra system and includes all gauges where data has been received from the lead water agency of each jurisdiction. Simulation nodes were added in locations in which the model will provide simulated streamflow. There are 3 files that have been extracted from the Hydstra database to aid in identifying sites in each bioregion and the type of data collected from each on. These data were used to determine the simulation node locations where model outputs were generated. The 3 files contained within the source dataset used for this determination are: Site - lists all sites available in Hydstra from data providers. The data provider is listed in the #Station as _xxx. For example, sites in NSW are _77, QLD are _66. Some sites do not have locational information and will not be able to be plotted. Period - the period table lists all the variables that are recorded at each site and the period of record. Variable - the variable table shows variable codes and names which can be linked to the period table. Relevant location information and other data were extracted to construct the spreadsheet and shapefile within this dataset. Dataset Citation Bioregional Assessment Programme (XXXX) HUN AWRA-R simulation nodes v01. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/fda20928-d486-49d2-b362-e860c1918b06. Dataset Ancestors Derived From National Surface Water sites Hydstra

AWC to 60cm is one of 18 attributes of soils chosen to underpin the land suitability assessment of the Roper River Water Resource Assessment (ROWRA) through the digital soil mapping process (DSM). AWC (available water capacity) indicates the ability of a soil to retain and supply water for plant growth. This AWC raster data represents a modelled dataset of AWC to 60cm (mm of water to 60cm of soil depth) and is derived from analysed site data, spline calculations and environmental covariates. AWC is a parameter used in land suitability assessments for rainfed cropping and for water use efficiency in irrigated land uses. This raster data provides improved soil information used to underpin and identify opportunities and promote detailed investigation for a range of sustainable regional development options and was created within the ‘Land Suitability’ activity of the CSIRO ROWRA. A companion dataset and statistics reflecting reliability of this data are also provided and can be found described in the lineage section of this metadata record. Processing information is supplied in ranger R scripts and attributes were modelled using a Random Forest approach. The DSM process is described in the CSIRO ROWRA published report ‘Soils and land suitability for the Roper catchment, Northern Territory’. A technical report from the CSIRO Roper River Water Resource Assessment to the Government of Australia. The Roper River Water Resource Assessment provides a comprehensive overview and integrated evaluation of the feasibility of aquaculture and agriculture development in the Roper catchment NT as well as the ecological, social and cultural (indigenous water values, rights and aspirations) impacts of development. Lineage: This AWC to 60cm dataset has been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO ROWRA published reports and in particular ' Soils and land suitability for the Roper catchment, Northern Territory’. A technical report from the CSIRO Roper River Water Resource Assessment to the Government of Australia. 1. Collated existing data (relating to: soils, climate, topography, natural resources, remotely sensed, of various formats: reports, spatial vector, spatial raster etc). 2. Selection of additional soil and land attribute site data locations by a conditioned Latin hypercube statistical sampling method applied across the covariate data space. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attribute to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data using predictive learning from a Random Forest approach implemented in the ranger R package. 6. Create AWC to 60cm Digital Soil Mapping (DSM) attribute raster dataset. DSM data is a geo-referenced dataset, generated from field observations and laboratory data, coupled with environmental covariate data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. 7. Companion predicted reliability data was produced from the 500 individual Random Forest attribute models created. 8. QA Quality assessment of this DSM attribute data was conducted by three methods. Method 1: Statistical (quantitative) method of the model and input data. Testing the quality of the DSM models was carried out using data withheld from model computations and expressed as OOB and R squared results, giving an estimate of the reliability of the model predictions. These results are supplied. Method 2: Statistical (quantitative) assessment of the spatial attribute output data presented as a raster of the attributes “reliability”. This used the 500 individual trees of the attributes RF models to generate 500 datasets of the attribute to estimate model reliability for each attribute. For continuous attributes the method for estimating reliability is the Coefficient of Variation. This data is supplied. Method 3: Collecting independent external validation site data combined with on-ground expert (qualitative) examination of outputs during validation field trips. Across each of the study areas a two week validation field trip was conducted using a new validation site set which was produced by a random sampling design based on conditioned Latin Hypercube sampling using the reliability data of the attribute. The modelled DSM attribute value was assessed against the actual on-ground value. These results are published in the report cited in this metadata record.

Overview Sequence R: Step AOA, No Probes (P) This sequence was designed to quantify the effect of the five-hole probes on the 3-D blade static angle-of-attack response in the presence of rotational influences by repeating Sequence K without five-hole probes. This test sequence used an upwind, rigid turbine with a 0° cone angle. The wind speeds ranged from 6 m/s to 20 m/s, and data were collected at yaw angles of 0° and 30°. The rotor rotated at 72 RPM. Blade pressure measurements were collected. The five-hole probes were removed and the plugs were installed. Plastic tape 0.03-mm-thick was used to smooth the interface between the plugs and the blade. The teeter dampers were replaced with rigid links, and these two channels were flagged as not applicable by setting the measured values in the data file to –99999.99 Nm. The teeter link load cell was pre-tensioned to 40,000 N. During post-processing, the probe channels were set to read –99999.99. The blade pitch angle ramped continuously at 0.18°/s over a wide range of increasing and decreasing pitch angles. A step sequence was also performed. The blade pitch was stepped 5°; the flow was allowed to stabilize; and the pitch angle was held for 5 seconds. Then the pitch angle step was repeated. Again, a wide range of pitch angles was obtained, both increasing and decreasing. The file lengths for this sequence varied from 96 seconds to 6 minutes, depending on the pitch angle range. Some short points were collected at 0° yaw and 3° pitch to ascertain the functionality of the instrumentation and repeatability over time. The file name convention used the initial letter R, followed by two digits specifying wind speed, followed by two digits for yaw angle, followed by RU, RD, or ST, followed by the repetition digit. The angle of attack motion was differentiated by RU (ramp up), RD (ramp down), and ST (step down, then step up). This sequence is related to Sequences K and L. Data Details File naming information can be found in the attached Word document "Sequence R Filename Key", copied from the Phase VI Test Report.

Abstract \r

\r The dataset was derived by the Bioregional Assessment Programme. This dataset was derived from multiple datasets. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.\r \r \r \r To ensure efficiency for processing speed and rendering this is a clip of the Vector Reference grid for the GAL region.\r \r It was created with a 50km buffer of the extent of the Hunter PAE and then selecting all grid cells that intersect with the extent.\r \r The unique ID field for each grid cell is AUID and starts from 1 in the reference grid. The grid also has a column id and row for easy reference\r \r The grid is in Australia Albers (GDA94) (EPSG 3577)\r \r

Purpose \r

\r This is an attempt to standardise (where possible) outputs of models from BA assessments and is the template to be used for GAL (clipped from whole of BA reference Grid) for the groundwater and potentially surface water model outputs.\r \r

Dataset History \r

\r The minimum bounding geometry tool in ArcGIS 10.1 was used to return the extent of the Bioregion boundary. This was then buffered with a 50km radius.\r \r The select location tool in ArcGIS 10.1 was then used to select all gridcells within the buffered extent.\r \r An export of the grid cells was then created to produce a rectangle reference grid of the GAL region.\r \r The file contains 2 shape files \r \r 1) The grid cells clipped to the boundary\r \r 2) The boundary extents as a reference of the Region\r \r

Dataset Citation \r

\r Bioregional Assessment Programme (XXXX) GAL Assessment Units 1000m 20160522 v01. Bioregional Assessment Derived Dataset. Viewed 12 December 2018, http://data.bioregionalassessments.gov.au/dataset/96dffeea-5208-4cfc-8c5d-408af9ac508e.\r \r

Dataset Ancestors \r

\r * Derived From BA ALL Assessment Units 1000m Reference 20160516_v01\r \r * Derived From BA ALL Assessment Units 1000m 'super set' 20160516_v01\r \r

The baseline NCS, fielded from the fall of 1990 to the spring of 1992, was the first nationally representative mental health survey in the U.S. to use a fully structured research diagnostic interview to assess the prevalences and correlates of DSM-III-R disorders. The baseline NCS respondents were re-interviewed in 2001-02 (NCS-2) to study patterns and predictors of the course of mental and substance use disorders and to evaluate the effects of primary mental disorders in predicting the onset and course of secondary substance disorders. In conjunction with this, an NCS Replication survey (NCS-R) was carried out in a new national sample of 10,000 respondents. The goals of the NCS-R are to study trends in a wide range of variables assessed in the baseline NCS and to obtain more information about a number of topics either not covered in the baseline NCS or covered in less depth than we currently desire. A survey of 10,000 adolescents (NCS-A) was carried out in parallel with the NCS-R and NCS-2 surveys. The goal of NCS-A is to produce nationally representative data on the prevalences and correlates of mental disorders among youth. The NCS-R and NCS-A, finally, are being replicated in a number of countries around the world. Centralized cross-national analysis of these surveys is being carried out by the NCS data analysis team under the auspices of the World Health Organization (WHO) World Mental Health Survey Initiative. In order to provide an easily accessible database which can be updated and checked on a regular basis, we have created a public use file system containing all the documents from the NCS and NCS-R programs. These file systems can be accessed through the Internet and either downloaded onto a disk or printed. We will update the system on a regular basis to add newly completed paper abstracts and other documents. In addition, the NCS and NCS-R data can be accessed through ICPSR (Inter-university Consortium for Political and Social Research). Any updates to the data to correct coding or classification errors will be made available along with written documentation of the changes in ICPSR''s quarterly newsletter.

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 15 release notes:Adds 2021 data.Version 14 release notes:Adds 2020 data. Please note that the FBI has retired UCR data ending in 2020 data so this will be the last Arrests by Age, Sex, and Race data they release. Version 13 release notes:Changes R files from .rda to .rds.Fixes bug where the number_of_months_reported variable incorrectly was the largest of the number of months reported for a specific crime variable. For example, if theft was reported Jan-June and robbery was reported July-December in an agency, in total there were 12 months reported. But since each crime (and let's assume no other crime was reported more than 6 months of the year) only was reported 6 months, the number_of_months_reported variable was incorrectly set at 6 months. Now it is the total number of months reported of any crime. So it would be set to 12 months in this example. Thank you to Nick Eubank for alerting me to this issue.Adds rows even when a agency reported zero arrests that month; all arrest values are set to zero for these rows.Version 12 release notes:Adds 2019 data.Version 11 release notes:Changes release notes description, does not change data.Version 10 release notes:The data now has the following age categories (which were previously aggregated into larger groups to reduce file size): under 10, 10-12, 13-14, 40-44, 45-49, 50-54, 55-59, 60-64, over 64. These categories are available for female, male, and total (female+male) arrests. The previous aggregated categories (under 15, 40-49, and over 49 have been removed from the data). Version 9 release notes:For each offense, adds a variable indicating the number of months that offense was reported - these variables are labeled as "num_months_[crime]" where [crime] is the offense name. These variables are generated by the number of times one or more arrests were reported per month for that crime. For example, if there was at least one arrest for assault in January, February, March, and August (and no other months), there would be four months reported for assault. Please note that this does not differentiate between an agency not reporting that month and actually having zero arrests. The variable "number_of_months_reported" is still in the data and is the number of months that any offense was reported. So if any agency reports murder arrests every month but no other crimes, the murder number of months variable and the "number_of_months_reported" variable will both be 12 while every other offense number of month variable will be 0. Adds data for 2017 and 2018.Version 8 release notes:Adds annual data in R format.Changes project name to avoid confusing this data for the ones done by NACJD.Fixes bug where bookmaking was excluded as an arrest category. Changed the number of categories to include more offenses per category to have fewer total files. Added a "total_race" file for each category - this file has total arrests by race for each crime and a breakdown of juvenile/adult by race. Version 7 release notes: Adds 1974-1979 dataAdds monthly data (only totals by sex and race, not by age-categories). All data now from FBI, not NACJD. Changes some column names so all columns are <=32 characters to be usable in Stata.Changes how number of months reported is calculated. Now it is the number of unique months with arrest data reported - months of data from the monthly header file (i.e. juvenile disposition data) are not considered in this calculation. Version 6 release notes: Fix bug where juvenile female columns had the same value as juvenile male columns.Version 5 release notes: Removes support for SPSS and Excel data.Changes the crimes that are stored in each file. There are more files now with fewer crimes per file. The files and their included crimes have been updated below.Adds in agencies that report 0 months of the year.Adds a column that indicates the number of months reported. This is generated summing up the number of unique months an agency reports data for. Note that this indicates the number of months an agency reported arrests for ANY crime. They may not necessarily report every crime every month. Agencies that did not report a crime with have a value of NA for every arrest column for that crime.Removes data on runaways.Version 4 release notes: Changes column names from "p

The data was collected through the (online-distributed, currently unavailable) survey to test the accuracy of the cognitive model SHARP (for more info about this model, see: https://hdl.handle.net/2077/74797 ). The code for SHARP is available at: https://doi.org/10.5281/zenodo.13759504

SHARP was designed to simulate human performance on certain deduction tasks related to the description logic ALE. More specifically, SHARP models the task of checking whether a given ABox (in the logic ALE) is inconsistent. In the survey, participants were presented (among other things) ABoxes after which they were supposed to decide their inconsistency. The data consists of: - responses to questions about previous logic experience - responses to demographic questions - responses for each presented ABox (Consistent',Inconsistent' or `I don't know') - responses for questions where the participant was asked to rate the difficulty of the previous ABox on a five-point Likert scale - response times for all questions, in seconds - a number indicating the order in which the ABoxes were presented (this order was randomised and different for each participant) See list of variable codes and their explanation below, please note that the mathematical notation used there is only properly shown when using MathJax. The documentation file Data_Description-2.pdf has been rendered with the corresponding notation and may be used for reference.

DATA-SPECIFIC INFORMATION FOR: [results-survey539252-16.csv]

Number of variables: 108

Number of cases/rows: 84

Variable List:

G01Q37: 'Do you know what a contradiction is?' G01Q36: 'Do you have experience with description logic?'' G01Q39: 'Have you seen this notation before? ( \{ a {\,:\,} (A \sqcap B) \} ) (If the expression does not show properly, please consider using a different browser)' G02Q38: 'What is your gender?' G02Q39: 'What is your age?' G02Q40: 'What is your nationality?' G01Q00: '( \{ a {\,:\,} A,\ b {\,:\,} eg A,\ a {\,:\,} eg B \} )' G01Q01: '( \{ a {\,:\,} \forall s.( eg A \sqcap B),\ (b,a) {\,:\,} r,\ c {\,:\,} ( eg B \sqcap C),\ b {\,:\,} \forall r. \exists s.(A \sqcap B),\ a {\,:\,} (B \sqcap eg C),\ (b,c) {\,:\,} s \} )' G02Q27: 'How difficult was it to decide whether the last ABox was inconsistent? 1 is very easy. 5 is very difficult.' G02Q18: '( \{ a {\,:\,} (A \sqcap (B \sqcap (C \sqcap (D \sqcap eg A)))) \} )' G04Q56: the order of the ABox presented in question G02Q18 G02Q12: '( \{ a {\,:\,} (B \sqcap (C \sqcap ( eg A \sqcap A))) \} )' G04Q57: the order of the ABox presented in question G02Q12 G02Q19: '( \{ a {\,:\,} (A \sqcap B),\ a {\,:\,} (B \sqcap C),\ a {\,:\,} (C \sqcap D),\ a{\,:\,} (D \sqcap eg A) \} )' G04Q58: the order of the ABox presented in question G02Q19 G02Q17: '( \{ a {\,:\,} ((\exists r. A \sqcap \exists r. B) \sqcap \forall r. ((\exists r. A \sqcap \exists r. B) \sqcap \forall r. (\exists r. A \sqcap \exists r. B))) \} )' G04Q59: the order of the ABox presented in question G02Q17 G02Q22: '( \{ a {\,:\,} \exists r. \exists s. eg A,\ a {\,:\,} \forall r. \forall s. A,\ b {\,:\,} \exists s. eg B \} )' G04Q60: the order of the ABox presented in question G02Q22 G02Q23: 'How difficult was it to decide whether the last ABox was inconsistent? 1 is very easy. 5 is very difficult.' G02Q07: '( \{ a {\,:\,} (A \sqcap B),\ a {\,:\,} eg B \} )' G04Q61: the order of the ABox presented in question G02Q07 G02Q14: '( \{ a {\,:\,} (\exists r. (\exists r. A \sqcap \exists r. B) \sqcap \exists r. B) \} )' G04Q62: the order of the ABox presented in question G02Q14 G02Q03: '( \{ a {\,:\,} A,\ a {\,:\,} B,\ a {\,:\,} eg A \} )' G04Q63: the order of the ABox presented in question G02Q03 G02Q11: '( \{ a {\,:\,} (A \sqcap ( eg A \sqcap (B \sqcap C))) \} )' G04Q64: the order of the ABox presented in question G02Q11 G02Q13: '( \{ a {\,:\,} (\exists r. A \sqcap \exists r. B) \} )' G04Q65: the order of the ABox presented in question G02Q13 G02Q25: 'How difficult was it to decide whether the last ABox was inconsistent? 1 is very easy. 5 is very difficult.' G02Q10: '( \{ a {\,:\,} eg A,\ a {\,:\,} eg B \} )' G04Q66: the order of the ABox presented in question G02Q10 G02Q21: '( \{ a {\,:\,} A,\ a {\,:\,} eg A,\ b {\,:\,} \forall r. A \} )' G04Q67: the order of the ABox presented in question G02Q21 G02Q15: '( \{ a {\,:\,} ( (\exists r. A \sqcap \exists r. B) \sqcap \forall r. (\exists r. A \sqcap \exists r. B)) \} )' G04Q68: the order of the ABox presented in question G02Q15 G02Q09: '( \{ a {\,:\,} A,\ a {\,:\,} B \} )' G04Q69: the order of the ABox presented in question G02Q09 G02Q05: '( \{ a {\,:\,} \exists r.A,\ a {\,:\,} \forall s. eg A,\ a {\,:\,} \exists r. B \} )' G04Q69Copy: the order of the ABox presented in question G02Q05 G02Q26: 'How difficult was it to decide whether the last ABox was...

To minimize ontogenetic and methodological variation, we only included trait data that met the following criteria: (a) plants were grown in natural ecosystems, excluding greenhouse and common garden experiments; (b) measurements were made on adult plants and not on seedlings; (c) hydraulic traits were measured on terminal stem or branch segments in the sapwood at the crown; and (d) trait data were calculated as the mean value for each species at the same site when data were from multiple sources.

Climate data were obtained either from the original reports or from WorldClim version 2 (http://worldclim.org/version2) if the original data were not available. The following variables were extracted from WorldClim: mean annual wind speed, mean annual precipitation, mean annual temperature, precipitation seasonality, temperature seasonality, precipitation of driest month, and minimum temperature of coldest month. The VPD data was extracted from the TerraClimate dataset (http://www.climatologylab.org/terraclimate.html). Annual PET (potential evapotranspiration) data were extracted from the CGIAR-CSI consortium (http://www.cgiar-csi.org/data). The moisture index (MI) is the ratio of precipitation to PET.

Simple linear regression was used to examine the relationships between two variables, utilizing the 'lm' function in R software. Partial regression analysis was conducted using the R package VISREG to investigate the relationships between wind speed and plant hydraulics while controlling for other variables. This analysis helped to illustrate the independent effect of wind on plant hydraulics. The Random Forest machine-learning algorithm (implemented using the R package randomForest) was utilized to assess the relative importance of environmental variables for each plant hydraulic trait. The Mean Decrease in Gini was calculated as the average of a variable's total decrease in node impurity, taking into account the proportion of samples that reach that node in each individual decision tree in the random forest. This provides a measure of a variable's importance in estimating the value of the target variable across all of the trees in the forest. A higher Mean Decrease in Gini value indicates greater importance of the variable. Multiple regression analyses were performed to develop predictive equations for plant hydraulic traits using environmental variables. To test for hydraulic traits-wind speed slope directions and differences among species groups in different climatic regions, we used standardized major axis (SMA) analyses. The R package SMATR was employed for these analyses. We considered p < 0.05 as the threshold for statistical significance in all models.

The Aboriginal Sites Decision Support Tool ASDST extends the Aboriginal Heritage Information Management System (AHIMS) by illustrating the potential distribution of site features recorded in AHIMS.\r ASDST was first developed in 2012 by the Office of Environment and Heritage (OEH) to support landscape planning of Aboriginal Heritage. The Tool produces a suite of raster GIS modelled outputs and is held in Esri GRID format. The first suite was published in 2016 as Version 7 at 100m resolution and in Lamberts Conic Conformal Projection (LCC). The current Version 7.5 was produced by the now Department of Planning, Industry and Environment (DPIE) in 2020 at 50m resolution in Geographic Coordinate System (GCS). Each layer covers the extent of NSW. \r \r The suite of layers includes separate predictive layers for different Aboriginal site feature types. The feature codes used in layer naming conventions are:\r \r * ALL = model for all feature types combined \r * AFT = predicted likelihood for stone artefacts \r * ART = predicted likelihood for rock art \r * BUR = predicted likelihood of burials \r * ETM = predicted likelihood of western mounds and shell \r * GDG = predicted likelihood of grinding grooves \r * HTH = predicted likelihood of hearths \r * SHL = predicted likelihood of coastal middens \r * STQ = predicted likelihood of stone quarries and \r * TRE = predicted likelihood of scarred trees. \r \r The feature models have been derived in two forms:\r \r * The first form (“p1750XXX” where XXX denotes three letter feature code) predicts likelihood of feature distribution prior to European colonisation of NSW. \r \r * The second form (“curr_XXX” where XXX denotes three letter feature code) predicts feature likelihood in the current landscape. \r \r For both sets of feature likelihood layers, cell values range from 0 – 1000, where 0 indicates low likelihood and 1000 is high likelihood. \r \r Please note the scale is likelihood and NOT probability. Likelihood is defined as a relative measure indicating the likelihood that a grid cell may contain the feature of interest relative to all other cells in the layer. \r \r Additionally, there are other derived products as part of the suite. These are: \r \r * drvd_imp = which is a model of accumulated impacts, derived by summing the difference between the pre colonisation and current version of all feature models. Cell values range from 0 – 1000, where 1000 is a high accumulated impact.\r \r * drvd_rel = which is a model of the reliability of predictions based on an environmental distance algorithm that looks at recorded site density across the variables used in the models.\r \r * drvd_srv = which is a survey priority map, which considers model reliability (data gap), current likelihood and accumulated impact. Cell values range from 0 – 1000 where 1000 indicates highest survey priority relative to the rest of the layer.\r \r For more details see the technical reference on the ASDST website.\r \r NB. Old layers with a suffix of “_v7” indicate they are part of ASDST Version 7 produced in 2016. The current models (Version 7.5) do not contain a version number in their name and will continue to be named generically in future versions for seamless access.\r \r Updates applied to ASDST version 7.5\r \r For all ASDST 7.5 data sets, the resolution was increased from a 100m cell to a 50m cell. All data sets were clipped and cleaned to a refined coastal mask. Cell gaps in the mask were filled using a Nibble algorithm. The pre-settlement data sets were derived by resampling the version 7 pre-settlement data sets to 50m cell size. The present-day data sets were derived from the version 7.5 pre-settlement layers and 2017-18 land-use mapping and applying the same version 7 parameters for estimating the preservation of each feature type on each land-use. For version 7.5, the model reliability data set was derived by resampling the version 7 data set to 50m cell size. Accumulated impact and survey priority version 7.5 data sets were derived by applying the version 7 processing algorithm but substituting the version 7.5 pre-settlement and present-day ASDST models.\r

SCOAPE_Pandora_Data is the column NO2 and ozone data collected by Pandora spectrometers during the Satellite Coastal and Oceanic Atmospheric Pollution Experiment (SCOAPE). Pandora instruments were located on the University of Southern Mississippi’s Research Vessel (R/V) Point Sur and at the Louisiana Universities Marine Consortium (LUMCON; Cocodrie, LA). Data collection for this product is complete.

The Outer Continental Shelf Lands Act (OCSLA) requires the US Department of Interior Bureau of Ocean Energy Management (BOEM) to ensure compliance with the US National Ambient Air Quality Standard (NAAQS) so that Outer Continental Shelf (OCS) oil and natural gas (ONG) exploration, development, and production do not significantly impact the air quality of any US state. In 2017, BOEM and NASA entered into an interagency agreement to begin a study to scope out the feasibility of BOEM personnel using a suite of NASA and non-NASA resources to assess how pollutants from ONG exploration, development, and production activities affect air quality. An important activity of this interagency agreement was SCOAPE, a field deployment that took place in May 2019, that aimed to assess the capability of satellite observations for monitoring offshore air quality. The outcomes of the study are documented in two BOEM reports (Duncan, 2020; Thompson, 2020).

To address BOEM’s goals, the SCOAPE science team conducted surface-based remote sensing and in-situ measurements, which enabled a systematic assessment of the application of satellite observations, primarily NO2, for monitoring air quality. The SCOAPE field measurements consisted of onshore ground sites, including in the vicinity of LUMCON, as well as those from University of Southern Mississippi’s R/V Point Sur, which cruised in the Gulf of Mexico from 10-18 May 2019. Based on the 2014 and 2017 BOEM emissions inventories as well as daily air quality and meteorological forecasts, the cruise track was designed to sample both areas with large oil drilling platforms and areas with dense small natural gas facilities. The R/V Point Sur was instrumented to carry out both remote sensing and in-situ measurements of NO2 and O3 along with in-situ CH4, CO2, CO, and VOC tracers which allowed detailed characterization of airmass type and emissions. In addition, there were also measurements of multi-wavelength AOD and black carbon as well as planetary boundary layer structure and meteorological variables, including surface temperature, humidity, and winds. A ship-based spectrometer instrument provided remotely-sensed total column amounts of NO2 and O3 for direct comparison with satellite measurements. Ozonesondes and radiosondes were also launched 1-3 times daily from the R/V Point Sur to provide O3 and meteorological vertical profile observations. The ground-based observations, primarily at LUMCON, included spectrometer-measured column NO2 and O3, in-situ NO2, VOCs, and planetary boundary layer structure. A NO2sonde was also mounted on a vehicle with the goal to detect pollution onshore from offshore ONG activities during onshore flow; data were collected along coastal Louisiana from Burns Point Park to Grand Isle to the tip of the Mississippi River delta. The in-situ measurements were reported in ICARTT files or Excel files. The remote sensing data are in either HDF or netCDF files.

Writing process data of 90 healthy elderly (50 - 90 years) were obtained. Each of them completed a typed sentence production task that consisted of 40 trials. Time on task, production time, and pause times before sentences, between words and within words were logged with a keystroke logging tool (ScriptLog). The data were used to examine the influences of normal ageing and verb transitivity on sentence production. The underlying aim was to provide a foundation for further research on sentence production in Alzheimer's disease (AD).

This dataset contains the data that remained after eliminating the first trial of each participant and the trials during which participants did not use the correct target sentence structure (intransitive, monotransitive, ditransitive) or made revisions. The R script that was used for the analyses is also available.

The paper is currently under review. The data will be available as soon as it is accepted for publication.