Spinning words as disguise: Shady services for ethical research?

Ethical researchers who want to quote public user-generated content without further exposing these sources have little guidance as to how to disguise quotes. Reagle (2021b) showed that researchers’ attempts to disguise phrases on Reddit are often haphazard and ineffective. Are there tools that can help? Automated word spinners, used to generate reams of ad-laden content, seem suited to the task. We select 10 quotations from fictional posts on r/AmItheButtface and “spin” them using Spin Rewriter and WordAi. We review the usability of the services and then (1) search for their spins on Google; and, (2) ask human subjects (N=19) to judge them for fidelity. Participants also disguise three of those phrases and these are assessed for efficacy and the tactics employed. We recommend that researchers disguise their prose by substituting novel words (i.e., swapping infrequently occurring words, such as “toxic” with “radioactive”) and rearranging elements of sentence structure. The practice of testing spins, however, remains essential even when using good tactics; a Python script is provided to facilitate such testing.

Applying a Python script to predict the geotechnical properties of the Nasiriyah soil

Soil is a natural material that suffers from intrinsic spatial variability resulting from natural factors and their influence on the soil. It became controversial and debated how to estimate the characteristic value of soils to obtain a reliable geotechnical design with low cost and less effort. Usually, foundations are not built on the same site as the screening; investigations are carried out to excavate a little at essential sites. In this paper (423), test wells were collected in the study area to be obtained and tabulated in Excel. The kriging statistics is applied using a python script to predict the values of geotechnical site properties and reliability of the method in estimating spatially varying soil properties values based on measurement data and prior knowledge. The program implements probabilistic kriging statistics and predicts the desired value by entering the coordinates of the locations whose properties you want to know and based on the previously prepared Excel file of known points, coordinates, and property values. The program will be used in two soil sites in the city of Nasiriyah to predict its properties. These points were selected from the examination of soil investigation reports to determine the reliability and accuracy of the program in predicting values. To get more reliable probability values using the kriging method and python scripts. A huge database of prepared and analyzed engineering soil properties has been created based on field investigation reports for projects in Nasiriyah.

linus: Conveniently explore, share, and present large-scale biological trajectory data in a web browser

In biology, we are often confronted with information-rich, large-scale trajectory data, but exploring and communicating patterns in such data can be a cumbersome task. Ideally, the data should be wrapped with an interactive visualisation in one concise packet that makes it straightforward to create and test hypotheses collaboratively. To address these challenges, we have developed a tool, linus, which makes the process of exploring and sharing 3D trajectories as easy as browsing a website. We provide a python script that reads trajectory data, enriches them with additional features such as edge bundling or custom axes, and generates an interactive web-based visualisation that can be shared online. linus facilitates the collaborative discovery of patterns in complex trajectory data.

A Python Script for Geometric Interval Classification in QGIS: A Useful Tool for Archaeologists

Graduated colour maps, created through the mathematical classification of quantitative variables, are frequently used in archaeology. A Python script for implementing a classification method based on geometric intervals in QGIS is presented here. This method is more suitable than the standard methods in case the quantitative attribute to be classified follows a right-skewed distribution, which is common among archaeological data. After an overview of the main classification methods, this paper focuses on the benefits of the geometric interval subdivision scheme, describes the technical features of the script and demonstrates how it works. A final thought on the advantages of using FLOSS is proposed.

PyTheis—A Python Tool for Analyzing Pump Test Data

The Theis equation is an important mathematical model used for analyzing drawdown data obtained from pumping tests to estimate aquifer parameters. Since the Theis model is a nonlinear equation, a complex graphical procedure is employed for fitting this equation to pump test data. This graphical method was originally proposed by Theis in the late 1930s, and since then, all the groundwater textbooks have included this fitting method. Over the past 90 years, every groundwater hydrologist has been trained to use this tedious procedure for estimating the values of aquifer transmissivity (T) and storage coefficient (S). Unfortunately, this mechanical procedure does not provide any intuition for understanding the inherent limitations in this manual fitting procedure. Furthermore, it does not provide an estimate for the parameter error. In this study, we employ the public domain coding platform Python to develop a script, namely, PyTheis, which can be used to simultaneously evaluate T and S values, and the error associated with these two parameters. We solve nine test problems to demonstrate the robustness of the Python script. The test problems include several published case studies that use real field data. Our tests show that the proposed Python script can efficiently solve a variety of pump test problems. The code can also be easily adapted to solve other hydrological problems that require nonlinear curve fitting routines.

A Compact Program for 3D Passive Seismic Source-Location Imaging

Microseismic source-location imaging is important for inferring the dynamic status of reservoirs during hydraulic fracturing. The accuracy and resolution of the located microseismic sources are closely related to the imaging technique. We present an open-source program for high-fidelity and high-resolution 3D microseismic source-location imaging. The presented code is compact in the sense that all required subroutines are included in one single C program, based on which seismic wavefields can be propagated either forward during a synthetic test or backward during a real time-reversal imaging process. The compact C program is accompanied by a Python script known as the SConstruct file in the Madagascar open-source platform to compile and run the C program. The velocity model and recorded microseismic data can be input using the Python script. This compact program is useful for educational purposes and for future algorithm development. We introduce the basics of the imaging method used in the presented package and present one representative synthetic example and a field data example. The results show that the presented program can be reliably used to locate source locations using a passive seismic dataset.

A Python script to merge Sanger sequences

Merging Sanger sequences is frequently needed during the gene cloning process. In this study, we provide a Python script that is able to assemble multiple overlapping Sanger sequences. The script utilizes the overlapping regions within the tandem Sanger sequences to merge the Sanger sequences. The results demonstrate that the script can produce the merged sequence from the input Sanger sequences in a single run. The script offers a simple and free method for merging Sanger sequences and is useful for gene cloning.

Automated Selection of Completion Interval Workflow Using Python Script for Probabilistic Range of EUR in a Greenfield Development

In the greenfield development process, one of the key questions that needs to be answered is, "What is the range of EUR for a particular development concept and the associated completion method based on the existing range of subsurface uncertainties?" The key challenge then is how can the team forecast a representative range of EUR efficiently to obtain a range of results that represent a probabilistic outcome. During the reservoir modelling process of this case study, a total of 405 static realizations had been run and then a STOIIP S-curve was generated. In the next step, 20 cases each of "High, Mid and Low" static models were selected based on the S-curve distribution for the next phase of dynamic simulation due to time and resources constraint. In terms of completion, the same development concept and completion method is assumed, where each dynamic case requires 8 horizontal producing wells with 200 metres of completion interval. Wells placement aside, each of the 60 dynamic models should not have the same fixed perforation depths and intervals due to the geological uncertainties with regards to facies distribution and they need to be selected based on the well effective k-h and hydrocarbon saturation along each well trajectory. Manual work could be used to analyse the best intervals for each of the planned wells, or in this case, this laborious process was replaced with an automated selection of the optimum completion interval workflow using Python script. This paper will show the workflow of how a scripted Python code is designed to provide an "automated moving window" to find the best intervals along a well trajectory. This workflow was executed in the pre-processor of the dynamic simulator which has a workflow window with Python-embedded capability. The Python code then generated the simulation keyword COMPDATMD, which contained the best perforation intervals for all the wells as an output. This automated workflow resulted in an optimization of the completion intervals in all the 60 dynamic model cases, while the ultimate recovery for this greenfield development in Peninsula Malaysia increased by 30% compared to EUR from previously "unoptimized runs". This approach is managed to cut down the run preparation time by at least two weeks compared to the manual solution. The improved range of EUR is also considered as a more representative outcome of the field development evaluation. Utilizing emerging technology breakthrough such as ability to customize specific features via a programming language is important towards a successful era of the Fourth Industrial Revolution (4IR). The results of this automated and customized workflow automation demonstrate a successful application of using machine learning for enhanced problem-solving in reservoir simulation.

CROP: a CRISPR/Cas9 guide selection program based on mapping guide variants

The off-target effect, in which DNA cleavage was conducted outside the targeted region, is a major problem which limits the applications of CRISPR/Cas9 genome editing system. CRISPR Off-target Predictor (CROP) is standalone program developed to address this problem by predicting off-target propensity of guide RNAs and thereby allowing the user to select the optimum guides. The approach used by CROP involves generating substitution, deletion and insertion combinations which are then mapped into the reference genome. Based on these mapped variants, scoring and alignment are conducted and then reported as a table comprising the off-target propensity of all guide RNAs from a given gene sequence. The Python script for this program is freely available from: https://github.com/vaprilyanto/crop.