Find research datasets worth reusing

Fig 3 in Comparative study on the survival and growth performance of white shrimp Penaeus vannamei in ponds cultured with hatchery and nursery reared post larvae

Fig 3: Temperature of P.Vannamei culture at Yazali during the year 2018.

Fig 2 in Comparative study on the survival and growth performance of white shrimp Penaeus vannamei in ponds cultured with hatchery and nursery reared post larvae

Fig 2: pH of P.Vannamei culture in summer crop at Yazali during the year 2018.

Fig 1 in Comparative study on the survival and growth performance of white shrimp Penaeus vannamei in ponds cultured with hatchery and nursery reared post larvae

Fig 1: Salinity of P.Vannamei culture at Yazali during the year 2018.

opencc-by-4.0Jun 2024View details →

Percentage survival of wMel-infected Aedes aegypti in UAV trial II in Nausori, Fiji (2019)

Triplicates samples of approximately 100-200 mosquitoes were collected from the emergence cages (rearing control), after cold immobilization at 4°C for 30 minutes (immobilization control), and after the end of each flight. Survival % was compared to that of the rearing control and statistical analysis was performed using ordinary one-way ANOVA, unless otherwise specified in the data sheet. NS = Not statistically significant and ND = Not done

Fig 1 in Growth performance, nutrient utilization and survival rate of Clarias gariepinus fed varied inclusion of processed Moringa oleifera diets

Fig 1: Showing Research Location

Fig 3 in Survival, growth, and biomass of brine shrimp (Artemia franciscana) fed with spirulina powder and soybean flour

Fig 3: The biomass of Artemia fed with different feeds at 21 days of rearing

Fig 2 in The effect of sulphur goatfish, sesarma, and green mussel as feed on the growth and survival of mud crab (Scylla paramamosain) in recirculation systems

Fig 2: Histogram of Survival Growth Rate (SGR)

Fig 2 in Survival, growth, and biomass of brine shrimp (Artemia franciscana) fed with spirulina powder and soybean flour

Fig 2: The body length of Artemia fed with different feeds at 21 days of rearing

Fig 1 in The effect of sulphur goatfish, sesarma, and green mussel as feed on the growth and survival of mud crab (Scylla paramamosain) in recirculation systems

Fig 1: Histogram of Food Conversion Ratio (FCR) Calculation

Fig 1 in Survival, growth, and biomass of brine shrimp (Artemia franciscana) fed with spirulina powder and soybean flour

Fig 1: The survival rate of Artemia fed with different feeds at 21 days of rearing

Fig 3 in The effect of sulphur goatfish, sesarma, and green mussel as feed on the growth and survival of mud crab (Scylla paramamosain) in recirculation systems

Fig 3: Histogram of Survival Rate (SR)

opencc-zeroJun 2024View details →

A synthetic dataset for the exploration of survival and classification models: prediction of heart attack or stroke within a 10-year follow-up period

<div> <div></div> </div> <div> <div> <div> Machine learning methodologies are increasingly popular in health care research. This shift to integrated data science approaches necessitates professional development of the existing health care data analyst workforce. To enhance a smooth transition, educational resources need to be developed. Barriers to accessing real healthcare datasets, vital for health care data analyses methodologies training purposes, include financial, ethical and patient confidentiality concerns. Synthetic datasets mimicking real-world complexities offer a simpler solution. We present a synthetic dataset which mirrors routinely collected primary care data on heart attack and stroke among the adult population. The data incorporates much of the practical challenges encountered in routinely collected primary care systems such as missing data, informative censoring, interactions, variable irrelevance, and noise and can be used for training in methods which handle these difficulties. The intent is for the user to build models of heart/stroke risk using survival-based methodologies. By sharing this synthetic dataset openly, our goal is to contribute a transformative asset for professional training in health and social care data analysis. The dataset covers demographics, lifestyle variables, comorbidities, systolic blood pressure, hypertension treatment, family history of cardiovascular diseases, respiratory functioning, and experience of heart-attack and/or stroke. This initiative aims to bridge the gap in sophisticated healthcare datasets for training, fostering professional development of the health and social care research workforce. This study is funded by the National Institute for Health and Care Research ARC Wessex and the National Centre for Research Methods. The views expressed in this summary are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.   </div> </div> </div>

dryad36/100

Environmentally acquired gut-associated bacteria are not critical for growth and survival in a solitary bee, Megachile rotundata

Social bees have been extensively studied for their gut microbial functions, but the significance of the gut microbiota in solitary bees remain less explored. Solitary bee, Megachile rotundata females provision their offspring with pollen from various plant species, harboring a diverse microbial community that colonizes larvae guts. The Apilactobacillus is the most abundant microbe, but evidence concerning the effects of Apilactobacillus and other provision microbes on growth and survival are lacking. We hypothesized that the presence of Apilactobacillus in abundance would enhance larval and prepupal development, weight, and survival, while the absence of intact microbial communities was expected to have a negative impact on bee fitness. We reared larvae on pollen provisions with naturally collected microbial communities (Natural pollen) or devoid of microbial communities (Sterile pollen). We also assessed the impact of introducing Apilactobacillus micheneri by adding it to both types of pollen provisions. Feeding larvae with sterile pollen + A. micheneri led to the highest mortality rate, followed by natural pollen + A. micheneri, and sterile pollen. Larval development was significantly delayed in groups fed with sterile pollen. Interestingly, larval and prepupal weights did not significantly differ across treatments compared to natural pollen-fed larvae. 16S rRNA gene sequencing found a dominance of Sodalis, when A. micheneri was introduced to natural pollen. The presence of Sodalis with abundant A. micheneri suggests potential crosstalk between both, shaping bee nutrition and health. Hence, this study highlights that the reliance on non-host specific environmental bacteria may not impact fitness of M. rotundata.

opencc-zeroJun 2024View details →

opencc-by-4.0Jul 2024View details →

Adaptative Survival of Aspergillus fumigatus to Echinocandins Arises from Cell Wall Remodeling Beyond β-1,3-glucan Synthesis Inhibition

Unprocessed Solid-state NMR and Molecular Dynamics data sets for the manuscript titled "Adaptative Survival of Aspergillus fumigatus to Echinocandins Arises from Cell Wall Remodeling Beyond β-1,3-glucan Synthesis Inhibition"

dryad36/100

Measuring the benefit of a risk-induced trait response: vigilance and survival probability

Defensive traits are hypothesized to benefit prey by reducing predation risk from a focal predator but come at a cost to the fitness of the prey. Variation in the expression of defensive traits is seen among individuals within the same population, and in the same individual in response to changes in the environment (i.e., phenotypically plastic responses). It is the relative magnitude of the cost and benefit of the defensive trait which underlies the defensive trait expression and its consequences to the community. However, whereas the cost has received much attention in ecological research, the benefit is seldom examined. Even in a defensive trait as extensively studied as vigilance, there are few studies of the purported benefit of the behavior, namely that vigilance enhances survival. We examined if prey vigilance increased survival and quantified that benefit in a natural system, with white-tailed deer (Odocoileus virginianus) experiencing unmanipulated levels of predation risk from Florida panther (Puma concolor coryi). Deer that spent more time vigilant (as measured by head position using camera trap data) had a higher probability of survival. Indeed, an individual deer that were vigilant 75% of the time were more than three times as likely to be killed by panthers over the course of a year compared to a deer that were vigilant 95% of the time. Our results therefore show that within-population variation in the expression of a defensive trait has profound consequences to the benefit it confers. Our results provide empirical evidence supporting a long-held but seldom tested hypothesis, that vigilance is a behavior that reduces the probability of predation and quantified the benefit of this defensive trait. Our work furthers an understanding of the net effects of a trait on prey fitness and predator-prey interactions, within-population variation in traits, and predation risk effects.

opencc-zeroJul 2024View details →

opencc-by-4.0Mar 2015View details →

Fig. 3 in Evaluation of field dispersal and survival capacity of the genetic sexing strain Tapachula-7 of Anastrepha ludens (Diptera: Tephritidae)

Fig. 3. Survival of SMR and Tap-7 strains of Anastrepha ludens afer aerial release.

opencc-by-4.0Mar 2018View details →

Temperature-Dependent Development and Freezing Survival of Protostrongylid Nematodes of Arctic Ungulates: Implications for Transmission

Data used to derive the conclusion of the work. Spreadsheets contain raw data.

opengpl-2.0Jun 2018View details →

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

<pre>Replication pack, FSE2018 submission #164: ------------------------------------------ </pre> <pre>**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)</pre> <pre>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 `<DATASET_PATH>/common.cache`, where `<DATASET_PATH>` is the value of the variable `DATASET_PATH` in `settings.py` - extract **dataset_minimal_Jan_2018** to the newly created folder - rename files: mv backporting.csv monthly_data.pypi_backporting.csv mv cc_degree.csv monthly_data.pypi_cc_degree.csv mv commercial.csv monthly_data.pypi_commercial.csv mv commits.csv monthly_data.pypi_commits.csv mv contributors.csv monthly_data.pypi_contributors.csv mv dc_katz.csv monthly_data.pypi_dc_katz.csv mv downstreams.csv monthly_data.pypi_downstreams.csv mv d_upstreams.csv monthly_data.pypi_d_upstreams.csv mv github_user_info.csv user_info.pypi.csv mv issues.csv monthly_data.pypi_issues.csv mv non_dev_issues.csv monthly_data.pypi_non_dev_issues.csv mv non_dev_submitters.csv monthly_data.pypi_non_dev_submitters mv package_urls.csv package_urls.pypi.csv mv q90.csv monthly_data.pypi_q90.csv # raw_dependencies.csv is not required # raw_packages_info.csv is not required # Feel free to read README.md for more details about the data mv submitters.csv monthly_data.pypi_submitters.csv # In this scenario we'll generate a new survival_data.csv mv university.csv monthly_data.pypi_university.csv mv upstreams.csv monthly_data.pypi_upstreams.csv - edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` Then, use the Python code above to obtain `survival_data.csv`. ####Option 2.c, difficulty level: medium Use predownloaded raw data to build longitudinal feature values, and then the dataset. Despite most of the data is cached, some functions will pull up updates which might take anywhere from days to couple weeks to run. - Download **dataset_full_Jan_2018.tgz** from http://k.soberi.us/dataset_full_Jan_2018.tgz . This file is not included in this archive because of its size (5.4Gb compressed, 34Gb unpacked). - edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` - extract the content of this archive into `<DATASET_PATH>`. - clean up `<DATASET_PATH>/common.cache` (otherwise you'll get Step 2.a. You can reproduce Step 2.b by deleting only `survival_data.pypi_2008_2017-12_6.csv`) Run the Python code above to obtain `survival_data.csv`. ####Option 2.d, difficulty level: hard Build the dataset from scratch. Although most of the processing is parallelized, it will take at least couple months on a reasonably powerful server (32 cores, 512G of RAM, 2Tb+ of HDD space in our setup). - ensure the `<DATASET_PATH>` is empty - add more GitHub tokens (borrow from your coworkers) to `settings.py`. Run the Python code above to obtain `survival_data.csv`. Step 3 - run the regression --------------------------- install R libraries: install.packages(c("htmlTable", "OIsurv", "survival", "car", "survminer", "ggplot2", "sqldf", "pscl", "texreg", "xtable")) Use `build_model.r` (e.g. in RStudio) and produced `survival_data.csv` to build the regressions used in the paper. This process takes at least 16Gb of RAM and takes few hours to run due to the gigantic size of the dataset. </pre>

opencc-by-4.0Aug 2017View details →

A Search for a Surviving White Dwarf Companion in SN~1006 - photometric dataset

This is a photometry catalogue obtained by DECam to find hot surviving WD companions to the SN1006 supernova. Please acknowledge the science paper Kerzendorf et al. 2017 if you use this dataset.