PublicationPrimahadi Wijaya R., Gede. 2014. Visualisation of diachronic constructional change using Motion Chart. In Zane Goebel, J. Herudjati Purwoko, Suharno, M. Suryadi & Yusuf Al Aried (eds.). Proceedings: International Seminar on Language Maintenance and Shift IV (LAMAS IV), 267-270. Semarang: Universitas Diponegoro. doi: https://doi.org/10.4225/03/58f5c23dd8387Description of R codes and data files in the repositoryThis repository is imported from its GitHub repo. Versioning of this figshare repository is associated with the GitHub repo's Release. So, check the Releases page for updates (the next version is to include the unified version of the codes in the first release with the tidyverse).The raw input data consists of two files (i.e. will_INF.txt and go_INF.txt). They represent the co-occurrence frequency of top-200 infinitival collocates for will and be going to respectively across the twenty decades of Corpus of Historical American English (from the 1810s to the 2000s).These two input files are used in the R code file 1-script-create-input-data-raw.r. The codes preprocess and combine the two files into a long format data frame consisting of the following columns: (i) decade, (ii) coll (for "collocate"), (iii) BE going to (for frequency of the collocates with be going to) and (iv) will (for frequency of the collocates with will); it is available in the input_data_raw.txt. Then, the script 2-script-create-motion-chart-input-data.R processes the input_data_raw.txt for normalising the co-occurrence frequency of the collocates per million words (the COHA size and normalising base frequency are available in coha_size.txt). The output from the second script is input_data_futurate.txt.Next, input_data_futurate.txt contains the relevant input data for generating (i) the static motion chart as an image plot in the publication (using the script 3-script-create-motion-chart-plot.R), and (ii) the dynamic motion chart (using the script 4-script-motion-chart-dynamic.R).The repository adopts the project-oriented workflow in RStudio; double-click on the Future Constructions.Rproj file to open an RStudio session whose working directory is associated with the contents of this repository.

As of 2024, JavaScript and HTML/CSS were the most commonly used programming languages among software developers around the world, with more than 62 percent of respondents stating that they used JavaScript and just around 53 percent using HTML/CSS. Python, SQL, and TypeScript rounded out the top five most widely used programming languages around the world. Programming languages At a very basic level, programming languages serve as sets of instructions that direct computers on how to behave and carry out tasks. Thanks to the increased prevalence of, and reliance on, computers and electronic devices in today’s society, these languages play a crucial role in the everyday lives of people around the world. An increasing number of people are interested in furthering their understanding of these tools through courses and bootcamps, while current developers are constantly seeking new languages and resources to learn to add to their skills. Furthermore, programming knowledge is becoming an important skill to possess within various industries throughout the business world. Job seekers with skills in Python, R, and SQL will find their knowledge to be among the most highly desirable data science skills and likely assist in their search for employment.

This dataset is about stocks per day. It has 3,215 rows and is filtered where the stock is CIMBT-R.BK. It features 3 columns: stock, and highest price.

This dataset is about stocks per day. It has 3,902 rows and is filtered where the stock is EASON-R.BK. It features 3 columns: stock, and highest price.

This dataset include 514 scientific papers, which were used to review the litterature on impact modelling of agricultural landscapes. This study was published in Environmental Modelling and Software [Hossard, L., Chopin, P., in press. Modelling agricultural changes and impacts at landscape scale: A bibliometric review, Environmental Modelling and Software]. The literature search was conducted in April 2017 and involved entering keywords in the Clarivate Analytics’ Web of Science without a time frame limitation. The search was limited to the “Article” document type and to the “English” language. For “Topics”, the following search equation was used: “model* AND (agri* or agro* or crop* or farm*) AND (landscape* OR watershed* OR (water NEAR catchment*)) AND (scenar* OR alternative*)”. This initial search yielded 1,975 hits. We then excluded papers based on article abstracts when they did not match our selection criteria, which were: (1) use of a model, i.e. a simplified representation of the system, as a tool to design or assess future agricultural landscape(s), (2) a focus on agricultural systems (including farming practices and/or agricultural organisation, explaining why we chose not to use “land use*” as a key word), (3) resolution at landscape scale (i.e. beyond the farm level) and (4) with outcomes on alternative agricultural systems (thus excluding papers focusing only on the effects of climate change). We also manually excluded general papers lacking a case study application, e.g. reviews without case study (12 papers) (Figure 1). Our final dataset thus comprised 514 individual papers. The json file (EMS_public.jon) presented here, based on a Zotero extraction and excluding proprietary data (e.g., abstracts), includes: article ID, title, journal, page, volume, issue, country, authors and year of publication. Data can be viewed with Firefox, Zotero, or with the R software, using the package jsonlite (Ooms, 2014). The R code, run with R 3.3.3 (Mac), was used to identify the optimal number of topics for our 514 papers dataset, and perform the LDA analyses (number of groups, top words, distribution of topics for each paper, etc.). Note that the full database (including abstracts) is necessary to run this code. Only a partial database is included here, as the full one includes proprietary data. The excel file (all.art.final.topics.Threshold_0.15.xls) displays the results of our LDA analysis, as provided by the R code on the 514 papers database. It includes: the article number (corresponding to the ID of EMS_public.json), the article title, the number of occurences of words related to Topics 2, 1, 4, and 3, the Best topic (considering a dominancy threshold of 0.15), and the year of publication. Reference Ooms, J., 2014. The jsonlite Package: a practical and consistent mapping between JSON Data and R objects. arXiv:1403.2805 [stat.CO] URL https://arxiv.org/abs/1403.2805 (accessed, December 2017).

This dataset is about stocks per day. It has 141 rows and is filtered where the stock is EFORL-R.BK and the date is after the 3rd of October 2024. It features 3 columns: stock, and highest price.

We describe the procedure for the consolidation of the daily metabolic rate estimates along with summary weather, reach and water derived data that may be used for the interpretation of the processes happening along the reach of interest. Sections 1 and 2 of the R script define the working directory and read the file containing the time series of daily metabolic rate estimates for the study period (related variable: ´table1´). Section 3 converts to NAs the daily metabolic rates that are of bad quality. The considerations to do this are first, daily metabolic rate estimates for days in which the available number of DO or temperature data points were less than 65% of the maximum daily total (this criterion may be modified by the user) and second, daily outputs that reflect failure on model performance due to either reaeration rates much higher than metabolic processes or DO concentrations increasing during night time (these resulted in positive values of the estimated CR24 and/or negative values of GPP). Section 4 reads the daily solar radiation and reference PAR estimates and together with the filtered metabolic rates conform the final results table (related variable: ´main1´). Next, in section 5, the script reads the daily averages of ancillary water data (turbidity and chlorophyll for our case) and appends them to the final results table (related variable: ´main 4´). In section 6 the script creates figures 2, 3, and 4 of the publication. Figure 2 presents stacked panels of time series of mean daily discharge, water temperature, and DO (daily maximum, minimum and range); figure 3 presents stacked panels of the daily metabolic estimates (GPP, CR24 and NDM) in the context of the flow dynamics of the reach; figure 4 presents the ancillary data that is expected to support the analysis of the daily metabolic rates within the reach (turbidity, chlorophyll and reference PAR). Finally, section 7 prints the final results table that contains all the daily variables for the period of analysis. DOI: 10.6084/m9.figshare.3427892

Natural product in the COCONUT database with details of source organisms, geolocations and citations.

Natural product in the COCONUT database with details of source organisms, geolocations and citations.

Microfarms have been characteristed by Morel and Léger (2016) as commercial farms that meet the following four criteria: (1) soil-based market gardening is the main income generating activity (excludes roof-top gardening, although part of the cultivated acreage can be protected under cold tunnels); (2) a high level of cultivated biodiversity is grown organically, with 30 to 80 vegetables and herbs (excludes mushroom and fruit production); (3) the utilized agricultural area is less than 1.5 ha by full-time equivalent, which is the minimal size generally recommended by French official agricultural development agencies for diversified market 45 gardening ; (4) farmers sell their produce in short supply chains; (5) production is organic. The data and R script provided in this archive were collected and developped by Kevin Morel during his PhD (UMR SADAPT, INRAE, AgroParisTech, Université Paris-Saclay) about the viability of market gardening microfarms. These data have been used to build the simulation model MERLIN explosring the economic viability (incomes and labour) of microfarms strategic scenarios. Data were collected from 2014 to 2015 on 10 microfarms in Northern France chosen for their diversity in terms of practices. Collection of data involved semi-structured interviews but also collection of existing data and documents available on the farm. On two farms (C and F), more precise data (for yields and workload) have been collected because farmers were involved in data collection for other research projet or their own use. In this archive, we distinguish 3 folders: (1) RAW DATA where we prodive all data collected on the 10 farms (2) MODELLING DATA AND SCRIPTS where we provide aggregated data used in the MERLIN MODEL (3) R scripts of the MERLIN Model Full text of the PhD thesis is available here (chapters are in English): Morel, K., 2016. Viabilité des microfermes maraîchères biologiques. Une étude inductive combinant méthodes qualitatives et modélisation. INRA, AgroParisTech, Université Paris-Saclay, France. 352 p. Open access to thesis: https://hal.archives-ouvertes.fr/tel-02801554 Explanation about the context and diversity of microfarms can be found in: Kevin Morel, Francois Léger. A conceptual framework for alternative farmers' strategic choices: The case of French organic market gardening microfarms. Agroecology and Sustainable Food Systems, Philadelphia PA: Taylor & Francis, 2016, 40 (5), pp.466-492. Open access to post-print here: https://hal.inrae.fr/hal-02939297 Details about related data collection, treatment and modelling can be found in these two papers: Morel, K., San Cristobal, M., Léger, F., 2018. Simulating incomes of radical organic farms with MERLIN: A grounded modelling approach for French microfarms. Agricultural Systems, (161): 89-101. Open access to post-print here:https://hal.inrae.fr/hal-02939224 Morel, K., San Cristobal, M., Léger, F., 2017. Small can be beautiful for organic market gardens: An exploration of the economic viability of French microfarms using MERLIN. Agricultural Systems, (158): 39-49. Open access to post-print here:https://hal.inrae.fr/hal-01608929 All references cited in this archive refer to these 3 above publications. l (2016-12-31)

To overcome the frequently debated crisis of confidence, replicating studies is becoming increasingly more common. Multiple frequentist and Bayesian measures have been proposed to evaluate whether a replication is successful, but little is known about which method best captures replication success. This study is one of the first attempts to compare a number of quantitative measures of replication success with respect to their ability to draw the correct inference when the underlying truth is known, while taking publication bias into account. Our results show that Bayesian metrics seem to slightly outperform frequentist metrics across the board. Generally, meta-analytic approaches seem to slightly outperform metrics that evaluate single studies, except in the scenario of extreme publication bias, where this pattern reverses.

The consideration of combined effects of B and C drug classes heightened the significance of the results for miRNAs 219 and 29c (bold; see Table 3), while miRNA 30e-3p and 526b* (bold) acquired significance.BD =  bipolar disorder.

Summary data: R117H and WT subjects, top 12 glands; loss-corrected.

Top 3 models in descending order of Akaike weight given for each such response variable. Predictor variables: 1- arabkm1z (arable in 1 km x 1 km square around farm), 2- arabkm3z (arable in 3 km x 3 km square around farm), 3- calyear (calendar year, 2002,2003), 4- system (organic, conventional), 5- woodkm1z (woodland in 1 km x 1 km square around farm), 6- woodkm3z (woodland in 3 km x 3 km square around farm). Interaction terms: 7- arabkm1z:system, 8- arabkm3z:system, 9- system:woodkm1z, 10- system:woodkm3z. In the presence of system-landscape interaction, landscape models are given for each system separately. Model averaged parameter estimates given in S1 Table.