Find research datasets worth reusing

opencc-by-4.0Jun 2022View details →

DATASET: Replication-competent HIV-1 in human alveolar macrophages and monocytes despite nucleotide pools with elevated dUTP

Complete data set to support content of the study:  Cui, et al (2022)  Replication-competent HIV-1 in human alveolar macrophages and monocytes despite nucleotide pools with elevated dUTP

opencc-by-4.0Oct 2022View details →

Replication Data for "How Do Different Types of Testing Goals Affect Test Case Design?"

# Replication Data for "How Do Different Types of Testing Goals Affect Test Case Design?" ## Overview Background: Test cases are designed in service of one or more goals, e.g., assessing functional correctness or performance. We lack a clear understanding of how specific goal types influence test design. Aims: We explore the relationship between types of testing goals and test design, including identification and importance of goal types, quantitative relations between goal types and test cases, and personal, organizational, methodological, and technological factors that may influence this relationship. Method: We have conducted both qualitative and quantitative analysis of interviews and a survey with software developers in various domains and of varying experience. Results: We identify nine goal types, and focus on correctness, reliability, and quality. We observe that test design for correctness forms a "default" design process that is modified when pursuing other goals. For the examined goal types, test cases tend to be simple, with many tests targeting a single goal and each test focusing on 1-2 goals at a time. Testers often start by using past tests as templates. Testing practices, tools, and system types of interest vary between goal types. Test design can be influenced by organization, process, and team makeup. Conclusions: This study provides a foundation for future research on test case design and testing goals. The paper can be found at http://greg4cr.github.io/pdf/23goals.pdf  ## Data Contained in This Package - thematic_coding.pdf This is the theme map created from the interview data. We extracted important statements from the interviews (codes) and clustered them into themes and sub-themes. - survey_responses.pdf This file contains all survey responses. Both interview and survey data has been anonymized to protect the privacy of the participants.

opencc-by-4.0Feb 2023View details →

Replication code and data for "Comparing measured dietary variation within and between tropical hunter-gatherer groups to the Paleo Diet".

Replication code and data for the publication "Comparing measured dietary variation within and between tropical hunter-gatherer groups to the Paleo Diet" appearing in the American Journal of Clinical Nutritian. Files include: 1) R replication code (5 files that should be run sequentially in numerical order). 2) "raw_diet_data.csv": 1 file that is ingested by the R code and used for the main analyses. 3) "lipid_classes.csv" and "nutriants_by_source_raw.csv": 2 files that are ingested by the R code and used for the supplementary analyses. 4) "wc2.1_30s_bio_1.tif": 1 file of GeoTiff data on annual mean temperature from the WorldClim v. 2.1 dataset (http://www.worldclim.com/version2) that is ingested by the R code. 5) "table_1.csv": a cleaned version of the data used in analyses that is output by the R code. 6) "table_hg_diet_data.csv" and "table_seasonal_diet_data.csv": 2 files containing seasonal and HG diet data that were not used in analyses, which are output by the R code.

opencc-by-4.0Jul 2023View details →

GC Skew and origin of replication-related plots for all Archaea genomes.

We generated data and plots exhaustively for all Archaea complete genomes in the <a href="https://www.ncbi.nlm.nih.gov/datasets/genomes/?taxon=2157">NCBI dataset </a>in April 2022. Plots and data available: SkewI and GC Skew: <ul> <li>  SkewI and  GC content of every archaeon complete genome. </li> <li> Cumulative GC Skew plot of every complete genome, with an estimation of the origin and termination point in the sequence, obtained by analyzing the plot </li> <li> The same results are available sorted by taxonomy (Euryarchaeota, Crenarchaeota, Thaumarchaeota) and SkewI values. </li> </ul>   Dot Plots: <ul> <li> Dot plots comparing two by two each of the ten genomes having the highest SkewI value for any given taxonomy. An estimation of the origin of replication based on the cumulative GC Skew is represented. </li> <li> Dot plots comparing two by two a genome of high SkewI with a genome of low SkewI. </li> </ul>   Clinker: <ul> <li> Comparison of genes around origin/terminus of replication within a taxonomy. Results obtained by running Clinker on a portion of 20 000 nucleotides before and after the origin/terminus of replication of all genomes belonging to the taxonomy. </li> </ul>   Violin plots: <ul> <li> Visual representation of the repartition of SkewI and GC Skew in different taxonomies of archaea and selected bacteria. </li> <li> Similar plots for TA skew, cumulative skew per codon position and cumulative skew for non-coding regions. </li> </ul>   Please refer to the readme file for further information about the results and references.

opencc-by-4.0Jul 2023View details →

Data set for the article "Synchronous replication initiation of multiple origins"

This data set contains the data of the submitted article "Synchronous replication initiation of multiple origins". The data was generated using simulations in python that are linked below. Experiments indicate that E. coli initiates DNA replication at multiple origins synchronously in fast growth conditions. We study by mathematical modelling under what conditions replication is initiated synchronously.

openmit-licenseJul 2023View details →

Replication Package: An Expert Survey on the Use of Informal Models in the Automotive Industry

This repository contains the replication package for the paper An Expert Survey on the Use of Informal Models in the Automotive Industry by <a href="https://orcid.org/0000-0001-6410-6769">Dominik Fuchß</a>, <a href="https://orcid.org/0000-0001-7312-2891">Thomas Kühn</a>, <a href="https://orcid.org/0000-0002-8953-1064">Jérôme Pfeiffer</a>, <a href="https://orcid.org/0000-0003-3534-253X">Andreas Wortmann</a>, and <a href="https://orcid.org/0000-0002-1593-3394">Anne Koziolek</a>. The paper has been accepted at the <a href="https://www.iese.fraunhofer.de/en/twinarch.html">TwinArch 2023: The 2nd International Workshop on Digital Twin Architecture</a> co-located with <a href="https://conf.researchr.org/home/ecsa-2023">ECSA 2023</a>.

openJan 2023View details →

Replication Package for "A Catch-22--the Test-Retest Method of Reliability Estimation"

Replication package for the paper "A Catch-22--the Test-Retest Method of Reliability Estimation".Files included in the replication package and purpose of each file:[1]Datafiles containing variables used in the analysis: question  content, stability and reliability estimates (in SPSS and Stata format): 01_GSS_gammaV26_April2022_extract.sav 01_GSS_gammaV26_April2022_extract.dta 01_GSS_gammaV26_April2022_long_extract.dta[2]SPSS Syntax file for replicating Tables 2,3 and Appendix Table:  02_SPSS_syntax_Catch22.sps[3]Stata .do file containing the code for the regression models (Table 4): 03_Stata_code_Catch22.do[4]List of GSS variables, wordings, and responses for variables included in the analysis (Excel file) 04_GSS variable wordings.xlsx

opencc-by-4.0Dec 2022View details →

Replication Package for: Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud

<h2>Replication Package for: Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud</h2>This is our replication package for our study on Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud.All scalability experiments are performed with the scalability benchmarking framework <a href="https://www.theodolite.rocks/">Theodolite</a> at <a href="https://www.se.informatik.uni-kiel.de/en/research/software-performance-engineering-lab-spel">Kiel University's Software Performance Engineering Lab (SPEL)</a> or Google Cloud.With this replication package, we provide:<ul><li><a href="https://www.theodolite.rocks/concepts/benchmarks-and-executions.html">Benchmark execution files</a> in executions,</li><li>our benchmark (raw) results in results, and</li><li>analysis script for our results in analysis.</li></ul><h3>Repeating Benchmark Executions</h3>All our Theodolite executions are tailored to either the SPEL cluster or the Google Cloud.<h4>Kiel University's Software Performance Engineering Lab (SPEL)</h4>The SPEL cluster consists of 5 nodes, named kube1-1 to kube1-5 and labeled with env=dev. To run them in your local cluster, make sure to provide the same infrastructure or rename node selectors in the execution files accordingly.To install Theodolite, run:<blockquote>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f se-cluster-dev.yaml</blockquote>or for the vertical scalability experiment:<blockquote>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f se-cluster-dev.yaml -f se-cluster-dev-vertical.yaml</blockquote>See <a href="https://www.theodolite.rocks">Theodolite's documentation</a> for further usage instructions.<h4>Google Cloud</h4>In the public cloud baseline experiments, the cluster consists of 5 e2-standard-32 nodes.To install Theodolite, run:<blockquote>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f gcp-cluster-dev.yaml</blockquote>For the experiments testing higher load intensities, the cluster consists of 4 e2-standard-16 nodes labeled with type=infra and 4 or 8 e2-standard-16 nodes with label type=sut. To install Theodolite in this cluster, run:<blockquote>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f gcp-cluster-stress.yaml</blockquote>In both cases, change the maximum load generated per load generator instance:<blockquote># Generate max. 100000 rec/sec per load generator instance export MAX_RECORDS_PER_INSTANCE=100000 kubectl patch benchmarks uc1-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]"</blockquote>See <a href="https://www.theodolite.rocks">Theodolite's documentation</a> for further usage instructions.<h3>Repeating Results Analysis</h3>To inspect, repeat, or extend our results analysis, see results or run the corresponding notebooks in analysis.For analyzing and visualizing benchmark results, either Docker or a Jupyter installation with Python 3.7 or 3.8 is required (e.g., in a virtual environment). Moreover, we require some Python libraries, which can be installed by:<blockquote>python3.8 -m venv .venv # source .venv/bin/activate pip install -r analysis/requirements.txt</blockquote>

Replication Data for: "Marine latitudinal diversity gradients are generally absent in intertidal ecosystems"

Datasets used in the paper "Marine latitudinal diversity gradients are generally absent in intertidal ecosystems"

Replication Package of Pandemic Pedagogy: Evaluating Remote Education Strategies during COVID-19

The COVID-19 pandemic precipitated an abrupt shift in the educational landscape, compelling universities to transition from in-person to online instruction. This sudden shift left many university instructors grappling with the intricacies of remote teaching. Now, with the pandemic behind us, we present a retrospective study aimed at understanding and evaluating the remote teaching practices employed during that period. Drawing from a cross-sectional analysis of 300 computer science students who underwent a full year of online education during the lockdown, our findings indicate that while remote teaching practices moderately influenced students' learning outcomes, they had a pronounced positive impact on student satisfaction. Remarkably, these outcomes were consistent across various demographics, including country, gender, and educational level. As we reflect on the lessons from this global event, this research offers evidence-based recommendations that could inform educational strategies in unwelcoming future scenarios of a similar nature, ensuring both student satisfaction and effective learning outcomes in online settings.

Replication Package for "Improving the Readability of Generated Tests Using GPT-4 and ChatGPT Code Interpreter"

While automated test generation can decrease the human burden associated with testing, it does not eliminate this burden. Humans must still work with generated test cases to interpret testing results, debug the code, build and maintain a comprehensive test suite, and many other tasks. Therefore, a major challenge with automated test generation is understandability of generated test test cases.  Large language models (LLMs), machine learning models trained on massive corpora of textual data - including both natural language and programming languages - are an emerging technology with great potential for performing language-related predictive tasks such as translation, summarization, and decision support.  In this study, we are exploring the capabilities of LLMs with regard to improving test case understandability. This package contains the data produced during this exploration: <ul> <li>The examples directory contains the three case studies we tested our transformation process on: <ul> <li>queue_example: Tests of a basic queue data structure</li> <li>httpie_sessions: Tests of the sessions module from the httpie project. </li> <li>string_utils_validation: Tests of the validation module from the python-string-utils project.</li> <li>Each directory contains the modules-under-test, the original test cases generated by Pynguin, and the transformed test cases. </li> <li>Two trials were performed per case example of the transformation technique to assess the impact of different results from the LLM.</li> </ul> </li> <li>The survey directory contains the survey that was sent to assess the impact of the transformation on test readability. <ul> <li>survey.pdf contains the survey questions.</li> <li>responses.xlsx contains the survey results.</li> </ul> </li> </ul>

opencc-by-4.0Sep 2023View details →

Replication package for the paper: "Machine Learning for the Identification and Classification of Technical Debt Types on StackOverflow Discussions"

This is the replication package for the article "Machine Learning for the Identification and Classification of Technical Debt Types on StackOverflow Discussions". The article was published in the Research Track of the third Brazilian Workshop on Intelligent Software Engineering (ISE'23). The replication package consists of 8 files: 1) dataset.csv, 2) code_anayses.ipynb and 3) example_test_balanced.csv and the others are results of word cloud generation. In dataset.csv, we provide the data for future replications. In code_anayses.ipynb, we provide the code we use to arrive at the results. In example_test_balanced.csv, we provide an example input dataset for training the models. For future references in this article, please contact lead author Eliakim Gama, or one of the co-authors.

Replication Package for "Why Do Deep Learning Projects Differ in Compatible Framework Versions? An Exploratory Study"

This dataset contains scripts and data used to generate relevant results for this paper. Detailed information are described in README.md.  code This folder contains all the scripts used for the experiment. The upgrade.py and downgrade.py are used to perform upgrade and downgrade runs. The pairing.py is used to generate the DFVC pairs. The main.py is used to identify root causes of DFVC pairs. result This folder contains all the results of the experiments, including the runtime output (e.g., a_1.0.0.txt), the runtime environment (e.g., condalist_1.0.0.txt), and the project's runtime commands (e.g., pytorch-cifar.xlsx) of all tested 90 PyTorch and 50 TensorFlow projects. Distribution of dfvc pairs.xlsx This file includes 6,926 DFVC pairs and their root causes. Tested framework versions.xlsx This file includes the framework versions tested and the Python versions that the framework versions are compatible with. Tested projects.xlsx This file includes the tested 90 PyTorch projects and 50 TensorFlow projects. We provide the following main information: (a) project name, (b) stars, (c) link, (d) the starting version, (e) python version, (f) incompatible upgrade/downgrade version, and (g) compatible versions.

opencc-by-4.0Sep 2023View details →

Replication Data for "Exploring Genetic Improvement of the Carbon Footprint of Web Pages"

## Overview In this study, we explore automated reduction of the carbon footprint of web pages through genetic improvement, a process that produces alternative versions of a program by applying program transformations intended to optimize qualities of interest. We introduce a prototype tool that imposes transformations to HTML, CSS, and JavaScript code, as well as image resources, that minimize the quantity of data transferred and memory usage while also minimizing impact to the user experience (measured through loading time and number of changes imposed). In an evaluation,  our tool outperforms two baselines---the original page and randomized changes---in the average case on all projects for data transfer quantity, and 80% of projects for memory usage and load time, often with large effect size. Our results illustrate the applicability of genetic improvement to reduce the carbon footprint of web components, and offer lessons that can benefit the design of future tools. ## Data Contained in This Package - experiment_data/Subject Project-XX-X.xlsx Each spreadsheet contains data collected as part of our experiments, including the fitness scores of the final solutions.

opencc-by-4.0Apr 2023View details →

Replication data for: Effect of Regional Marine Cloud Brightening Interventions on Climate Tipping Points

Data for reproduction of Hirasawa, H., Hingmire, D., Singh, H., Rasch, P. J., & Mitra, P. (2023). Effect of regional marine cloud brightening interventions on climate tipping elements. Geophysical Research Letters, 50, e2023GL104314. https://doi.org/10.1029/2023GL104314 Includes: <ul> <li>Raw monthly 2m temperature (TREFHT) and precipitation (PRECT) data from CESM2 MCB simulations.</li> <li>Ensemble mean data from CESM2 Large Ensemble</li> <li>Tipping point metric timeseries from CESM2 LE Historical and SSP2-4.5 and CESM2 MCB simulations.</li> <li>Jupyter notebook displaying plotting script</li> <li>Scripts showing procedure for computing tipping point metrics</li> <li>CAM6 SourceMod changes to apply cloud droplet number concentration perturbations</li> <li>Top of atmosphere long and shortwave anomalies from fixed sea surface temperature simulations for computing effective radiative forcing</li> </ul>

Approaching the Unknown: Replication file and Dataset

Dataset with R replication file for the paper: Approaching the Unknown. COVID-19 pandemic, political parties and digital adaptations: party élites' perceptions in Italy and Spain

Replication data for: IT Governance as Drivers of Dynamic Capabilities to Gain Corporate Performance Under the Effects of Environmental Dynamism.

Este estudo, baseado em dados de 147 empresas brasileiras da base de dados artigo-científica, explora o papel vital da governança de TI na promoção da inovação e no aprimoramento de capacidades operacionais para melhorar a eficiência corporativa em ambientes de negócios voláteis. A análise estatística destacou a importância da estratégia de orientação das empresas na maximização do valor e impacto da governança de TI, dependendo do nível de dinamismo ambiental. Os resultados sublinham a relevância da governança de TI na promoção de capacidades dinâmicas e na geração de retornos econômicos.

Replication data for: The Effect of IS-Innovation Strategy Alignment on Corporate Performance: Investigating the Role of Environmental Uncertainty by Heterogeneity

A base de dados técnico-científica de 856 empresas brasileiras examinou o alinhamento entre sistemas de informação estratégicos (ISS), na abordagem da estratégia como prática,  e inovação de exploration e exploitation, e seu impacto no desempenho corporativo (CP) sobre incerteza ambiental. Os resultados mostram que todos os tipos de alinhamento entre ISS e inovação influenciam positivamente o CP. O alinhamento com inovação ambidestra teve um impacto 62% maior no CP do que o alinhamento com inovações incrementais. Além disso, inovações disruptivas tiveram efeitos positivos em ambientes hostis, enquanto inovações exploratórias e ambidestras tiveram impactos fortes em ambientes altamente dinâmicos.

Experiment replication - Gould 1684 (Johns Hopkins University, Baltimore, USA, 6/6/2022)

Replication in laboratory of an experiment reported by William Gould, “An Account of the increase of weight in oyl of vitriol expos’d to the air,” Philosophical Transactions 14(156): 496–506 (1684): “On the ninth of No[vember] 1683. Three drams of oyl of vitriol so far dephlegm’d as to burn or corrode a strong packthred assunder, was expos’d to the air in a marmalade glass of three inches diameter, and plac’t in a nice pair of scales, in a room where no fire nor sun came; its increase for 7 natural days divided by less portions of time was according to the following table [...] upon the view of the whole diary of almost two months; it appear’d, the increase was more in a moist, rainy, misty, and snowy, but less in a frofty, clear, and dry seafon, as also was more in a cold than in a warm air” (pp. 497 & segg.) “All these circumstances which relate to the quantity will also influence very much the time of the encrease, the last thing to be consider’d in the experiment; but I shall only mention that which makes the most peculiar and principal variation in this point, and tis the proportion of the surface to the the liquor. For I find the greater or less the surface is, the quicker or slower the encrease [...] I expos’d in the same room and to the same temper of the air (as near as I could guess) three drams of the same oyl of vitriol in an open flat glass one inch broad, being only 2/3 of the diameter of that glass us’d at first with the like quantity. The result was this; that whereas the other surface of three inches diameter gain’d (as in the table) near nineteen grains the first six hours, this less surface gained a very little perceivable more then two grains in the same space of time” (pp. 503-504). The (potential) significance of this experiment for the debate on mineral generation lies also in the fact that it is mentioned by J.F. Henckel, Pyritologia (1721, Engl. transl. 1757): “Dr. Gould, of Oxford, has observed, that oil of vitriol does, by means of the air, encrease in weight, having, for that purpose, exposed a highly dephlegmated oil in an open wide glass, and weighed it accurately every day. In the space of fifty seven days three drachms of oil of vitriol came to nine drachms thirdy grains. The first day the oil increased one drachm and eight grains, afterwards, from day to day, still less, nay, the last day, scarce half a grain. This succeeds in moist foggy weather better than in dry, also in a wide than narrow vessel” (Henckel 1757, p. 374). Date: June 6, 2022 Place: Johns Hopkins University, Baltimore (MD), US Project and Funding Source: Horizon 2020 – MGA MSCA-IF – Grant agreement No. 101019781 – SOUNDEPTH Work Package: 4

Experiment replication - De Beaumont 1676 (Johns Hopkins University, Baltimore, USA, 6/6/2022)

Replication in laboratory of an experiment reported by John de Beaumont, “Two letters... concerning rock-plants and their growth,” Philosophical Transactions 11: 724–742 (1676): “Those who endeavour to explicate those figurations mechanically, seem to have a harder task; for, if they say with Hippocrates, Spiritu distenta omnia progeneris affinitate distant; as though, when the mineral spirit had extended the matter, it fell into those figures upon a spontaneous recess according to its proper weight, which gives order and measure to things; as he mechanically shews by a bladder, into which if earth, sand, and filings of lead be put, and water be added to them, and we give them motion by blowing in the bladder through a reed, first they are mixt together with the water, but in a while continuing in a gentle motion they separate themselves and retire each to its like, the lead to the lead &c.” (p. 740) Date: June 6, 2022 Place: Johns Hopkins University, Baltimore (MD), US Project and Funding Source: Horizon 2020 – MGA MSCA-IF – Grant agreement No. 101019781 – SOUNDEPTH Work Package: 4