This file was created on 2026-06-04 by Annalie Dorph _____________________________ ---- GENERAL INFORMATION ---- _____________________________ Title of dataset: Evaluating study design and spatial mark-recapture analysis methods for feral cat population monitoring Principal Investigator: Dr Annalie Dorph, University of New England, adorph2@gmail.com Date of data collection: 2023-07 to 2023-09 Geographic location of data collection: Guy Fawkes River National Park, NSW, Australia ______________________________ ---- DATA & FILE OVERVIEW ---- ______________________________ Folder: "code/rt_cat_SMR/" contains all files associated with running the analysis for the Bayesian categorical random-thinning spatial-mark-resight analysis - "./models/" : Folder containing all Bayesian models tested in the analy - "main.R" : A file loads and sets up the data, then runs the analysis - "model_summaries.R" : A secondary file to extract the results of the analysis and summarise the data - "nimble_functions.R" : Custom nimbleFunctions to fit categorical Random Thinning Model. Edited from code provided in doi: 10.1002/eap.2790 - "nimble_restart_MCMC.R" : From code provided in doi: 10.1002/eap.2790 - "overdispersion_check.R" : Code to calculate rudimentary checks of overdisperion within datasets - "prepare_camera_data.R" : A function to take raw camera data extracts and create a list of outputs for the function "prepare_nimble_data.R". Requires the following: * Camera locations : A csv with the columns CameraCode, Longitude, Latitude * Camera operation data : A csv with columns Camera, Date, Status for every day cameras were deployed. Status column contains "working"/"not working". * Camera data extract : A csv with columns Camera, DateTimeOriginal, FinalID (a name for individuals, or NO_FINAL_ID), Bicolor (contains Y/N), sex (contains MALE/FEMALE), Coat (contains BLACK/WHITE_IR/GING_D/CLAS_TABBY/MACK_TABBY/TORT), Long_Hair (contains Y/N). - "prepare_nimble_data.R" : A function that takes the output from "prepare_camera_data.R" and creates a list of outputs to use in the Bayesian modelling. - "run_nimble_model.R" : A function to take the outputs from "prepare_camera_data.R", "prepare_nimble_data.R", and a model script from the "./models/" folder to run the Bayesian models. Folder: "data_edited/rt_cat_SMR/" contains all file outputs from "code/rt_cat_SMR/prepare_camera_data.R" and "./prepare_nimble_data.R" Folder: "code/secr_SMR/" contains all files associated with running the analysis for the maximum likelihood SMR analysis - "main.R" : A file loads and sets up the data, runs the analysis and extracts the estimates - "prepare_camera_data.R" : A function to take raw camera data extracts and create several outputs for the models. Requires the following: * Camera locations : A csv with the columns CameraCode, Longitude, Latitude * Camera operation data : A csv with columns Camera, Date, Status for every day cameras were deployed. Status column contains "working"/"not working". * Camera data extract : A csv with columns Camera, DateTimeOriginal, FinalID (a name for individuals, or NO_FINAL_ID), Bicolor (contains Y/N), sex (contains MALE/FEMALE), Coat (contains BLACK/WHITE_IR/GING_D/CLAS_TABBY/MACK_TABBY/TORT), Long_Hair (contains Y/N). - "run_secr_model.R" : A function that takes the output from "prepare_camera_data.R" and builds SMR models. Folder: "data_edited/secr_SMR/" contains all the file outputs from "code/secr_SMR/prepare_camera_data.R".

Background:

Epidemic COVID-19 in 2020 was one of the crises that confronted the Islamic Republic of Iran’s health system as a developing low-income country with many challenges. The purpose of this study is to explain the requirements of similar health systems in facing epidemics such as COVID-19 as well as sharing experiences of Iran.

Methods:

The present study was a qualitative study using content analysis of the Iranian Ministry of Health website (behdasht.- gov.ir) during the official onset of COVID-19 in Iran (February 20) to March 7, 2020. To Integrate data, interviews with experts were used. In this regard, 4 national health policymakers and 5 provincial health managers were interviewed. After implementation using 8-step content analysis method, data were analyzed using MAXQDA software by research team members who had no conflict of interest.

Results:

The findings show that the disease began on February 20, 2020, with two cases of death from COVID-19 in Qom province in central Iran and within two weeks it has widely affected 31 provinces, with 4747 approved cases and 124 deaths. According to the results, the Islamic Republic of Iran’s health system faced five major challenges to confronting with COVID-19: 1- Medical consumables supply 2- Hospital beds allocation and intensive care units (ICU) equipment 3-The lack of a comprehensive COVID-19 protocol in the country 4-the lack of a single command authority to deal with the crisis 5- Insufficient public health literacy and information as a result of social anxiety.

Conclusions:

Experiences of this case indicate that health systems require policy action to prevent and control diseases rather than other implementations. In this regard, training should be given to children and adolescents, at-risk persons and health system staff. In addition, to confront with COVID-19, an integrated policy implementation team should be established in collaboration with the health sector and other relevant bodies.

There are three sexual fluids from women: lubrication (e.g. transudation of fluid across the mucosa of the vagina, and mucus from the greater vestibular glands); female ejaculation (from paraurethral glands); and gushing. Orthodox western medicine and physiology does not yet have a standardized description or explanation for the third, gushing. The present paper proposes that the gushing fluid is a filtrate of plasma, produced by the mechanism known as transudation. This is an additional application for the transudation mechanism, after the well accepted roles in lubrication of the vagina, and in generating serous fluids. The present model proposes that the fluid released in a gush arises from the ventral wall of the vagina due to the presence there of increased surface area of mucosa, dilated arterioles, pressurized venous and lymphatic plexuses, and compression provided by muscle contraction during orgasm.

Ground-based readings of temperature and rainfall, satellite imagery, aerial photographs, ground verification data and Digital Elevation Model (DEM) were used in this study. Ground-based meteorological information was obtained from Bangladesh Meteorological Department (BMD) for the period 1977 to 2015 and was used to determine the trends of rainfall and temperature in this thesis. Satellite images obtained from the US Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) website (www.glovis.usgs.gov) in four time periods were analysed to assess the dynamics of mangrove population at species level. Remote sensing techniques, as a solution to lack of spatial data at a relevant scale and difficulty in accessing the mangroves for field survey and also as an alternative to the traditional methods were used in monitoring of the changes in mangrove species composition, . To identify mangrove forests, a number of satellite sensors have been used, including Landsat TM/ETM/OLI, SPOT, CBERS, SIR, ASTER, and IKONOS and Quick Bird. The use of conventional medium-resolution remote sensor data (e.g., Landsat TM, ASTER, SPOT) in the identification of different mangrove species remains a challenging task. In many developing countries, the high cost of acquiring high- resolution satellite imagery excludes its routine use. The free availability of archived images enables the development of useful techniques in its use and therefor Landsat imagery were used in this study for mangrove species classification. Satellite imagery used in this study includes: Landsat Multispectral Scanner (MSS) of 57 m resolution acquired on 1st February 1977, Landsat Thematic Mapper (TM) of 28.5 m resolution acquired on 5th February 1989, Landsat Enhanced Thematic Mapper (ETM+) of 28.5 m resolution acquired on 28th February 2000 and Landsat Operational Land Imager (OLI) of 30 m resolution acquired on 4th February 2015. To study tidal channel dynamics of the study area, aerial photographs from 1974 and 2011, and a satellite image from 2017 were used. Satellite images from 1974 with good spatial resolution of the area were not available, and therefore aerial photographs of comparatively high and fine resolution were considered adequate to obtain information on tidal channel dynamics. Although high-resolution satellite imagery was available for 2011, aerial photographs were used for this study due to their effectiveness in terms of cost and also ease of comparison with the 1974 photographs. The aerial photographs were sourced from the Survey of Bangladesh (SOB). The Sentinel-2 satellite image from 2017 was downloaded from the European Space Agency (ESA) website (https://scihub.copernicus.eu/). In this research, elevation data acts as the main parameter in the determination of the sea level rise (SLR) impacts on the spatial distribution of the future mangrove species of the Bangladesh Sundarbans. High resolution elevation data is essential for this kind of research where every centimeter counts due to the low-lying characteristics of the study area. The high resolution (less than 1m vertical error) DEM data used in this study was obtained from Water Resources Planning Organization (WRPO), Bangladesh. The elevation information used to construct the DEM was originally collected by a Finnish consulting firm known as FINNMAP in 1991 for the Bangladesh government.

Previous research shows that reinforcement of variable responding will facilitate sequence learning in rats (Neuringer, Deiss & Olson, 2000) but may interfere with sequence learning in humans (Maes & van der Goot, 2006). The present study aimed to replicate and extend previous research by assessing the role of behavioral variability in the learning of difficult target sequences across 3 species: humans (n = 60), hens (n = 18) and possums (n = 6). Participants were randomly allocated to one of three experimental conditions (Control, Variable, Any). In the Control conditions sequences were only reinforced if they were the target sequence, in the Variability conditions sequences were concurrently reinforced on a Variable Interval 60-s schedule if the just entered sequence met a variability criterion, and in the Any condition sequences were concurrently reinforced on a Variable Interval 60-s schedule for any sequence entered. The results support previous findings with animals and humans; hens and possums were more likely to learn the target sequence in the Variability condition, and human participants were more likely to learn the target sequence in the Control condition. Possible explanations for differences between the performance of humans and animals on this task will be discussed.

This dataset provides all the raw and analysed data for the thesis titled 'The drivers and consequences of change to the physical character of waterholes on an Australian dryland river'.
The data has been divided into four folders that are aligned with the data chapters for the thesis. These being: (Ch 2) waterhole mapping, (Ch 3) floodplain gullies, (Ch 4) sediment transport and (Ch 5) fish.
A README file is provided for each chapter which contains a description of the individual datasets and a list of files that make up each dataset.
The data in this archive is a combination of data obtained from desktop studies as well as field work on the Darling River (i.e., the fish data).
Further, fish data were collected on the Darling River between Bourke and Wilcannia. Waterhole mapping was undertaken on the Barwon-Darling between Walgett and Wilcannia. Gully mapping was undertaken on the Barwon-Darling River between Mungindi and Wilcannia. Sediment transport capacity was assessed at five sites between Collarenebri and Tilpa.