Non-traditional data signals from social media and employment platforms for MTEM stock analysis

Dataset: data extraction spreadsheet and full workings for analysis accompanying a herbal medicine meta-review.

ESS-DIVE’s (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) dataset metadata reporting format is intended to compile information about a dataset (e.g., title, description, funding sources) that can enable reuse of data submitted to the ESS-DIVE data repository. The files contained in this dataset include instructions (dataset_metadata_guide.md and README.md) that can be used to understand the types of metadata ESS-DIVE collects. The data dictionary (dd.csv) follows ESS-DIVE’s file-level metadata reporting format and includes brief descriptions about each element of the dataset metadata reporting format. This dataset also includes a terminology crosswalk (dataset_metadata_crosswalk.csv) that shows how ESS-DIVE’s metadata reporting format maps onto other existing metadata standards and reporting formats. Data contributors to ESS-DIVE can provide this metadata by manual entry using a web form or programmatically via ESS-DIVE’s API (Application Programming Interface). A metadata template (dataset_metadata_template.docx or dataset_metadata_template.pdf) can be used to collaboratively compile metadata before providing it to ESS-DIVE. Since being incorporated into ESS-DIVE’s data submission user interface, ESS-DIVE’s dataset metadata reporting format, has enabled features like automated metadata quality checks, and dissemination of ESS-DIVE datasets onto other data platforms including Google Dataset Search and DataCite.

Establishing programmable and self-sustaining replication networks in pools of chemical reagents is a key challenge in systems chemistry. Self-replicating templates are formed from two constituent components with complementary recognition and reactive sites via a slow bimolecular pathway and a fast template-directed pathway. Here, we re-engineer one of the components of a synthetic replicator to encode an additional recognition function, permitting the assembly of a binary complex between the components that mediates replicator formation through a template-independent pathway, which achieves maximum rate acceleration at early time points in the replication process. The complementarity between recognition sites creates a key conformational equilibrium between the catalytically inert product, formed via the template-independent pathway, and the catalytically active replicator that mediates the template-directed pathway. Consequently, the rapid formation of the catalytically inert isomer kick-starts replication through the template-directed pathway. Through kinetic analyses, we demonstrate that the presence of the two recognition-mediated reactivity modes results in enhanced template formation in comparison to that of systems capable of exploiting only a single recognition-mediated pathway. Finally, kinetic simulations reveal that the conformational equilibrium and both the relative and absolute efficiencies of the recognition-mediated pathways affect the extent to which self-replicating systems can benefit from this additional template-independent reactivity mode. These results allow us to formulate the rules that govern the coupling of replication processes to alternative recognition-mediated reactivity modes. The interplay between template-directed and template-independent pathways for replicator formation has significant relevance to ongoing efforts to design programmable and adaptable replicator networks.

Impact Summary is a configurable app template which highlights an area and shows a summary of data related to its location. Use CasesPresents the impact of an event or a proposal on the local population.The data presented in the boxes, at the bottom of the map, can be any integer data allowing you to answer new questions about locations that you cannot answer with maps alone. For instance, in the web map you could enrich your polygon with demographics, landscape, infrastructure, and other variables using Enrich Layer or analyze your own data with Aggregate Points. Then you can use this template to show your analysis with a simple web mapping application.Configurable OptionsThe template can be configured using the following options:Map: Choose the web map used in your application.Mobile/Embed: A responsive side drawer is shown depending on the size available and can be toggled by the user.Navigation: Home and Geo-locate buttons add ease of map navigation.Content: Summarize integer data by choosing a Feature layer containing the fields. A layer containing multiple features and has a renderer defined allows for selecting the features by the renderer values. An interactive dashboard provides ability to categorize fields in up to 4 group panels and page through the fields.Summary: Briefly describe your application in the Area panel.Search: Enable Search for addresses and places.Share: Enable sharing using Twitter or FacebookData RequirementsThis application requires a feature layer with at least one numeric field. For more information, see the Layers help topic for more details.This app also includes the ability to geoenrich data as an alternative to providing your own numeric data. This option requires an ArcGIS Organization or ArcGIS developers Subscription and consumes credits.Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

General Accessibility Creative Commons All data products available from the data hub are provided on an 'as is' basis. The City of Sydney (City) makes no warranty, representation or guarantee of any type as to any errors and omissions, or as to the content, accuracy, timeliness, completeness or fitness for any particular purpose or use of any data product available from the data hub. If you find any information that you believe may be inaccurate, please email the City. In addition, please note that the data products available from the data hub are not intended to constitute advice and must not be used as a substitute for professional advice. The City may modify the data products available from the data hub and/or discontinue providing any or all of data products at any time and for any reason, without notice. Accordingly, the City recommends that you regularly check the data hub to ensure that the latest version of data products is used. The City recommends that when accessing data sets, you use APIs. We are committed to making our website as accessible and user-friendly as possible. Web Content Accessibility Guidelines (WCAG) cover a wide set of recommendations to make websites accessible. For more information on WCAG please visit https://www.w3.org/TR/WCAG21/ . This site is built using Esri's ArcGIS Hubs template, and their Accessibility status report is available online at https://hub.arcgis.com/pages/a11y. We create the maps and stories on this site using ArcGIS templates, each template having accessibility features. Examples include Instant Apps, Story maps, and Webapp builder. If you would like to request alternative formats for data products on this site please email the City. We encourage developers using our data to deliver maps and applications with consideration to accessibility for all. Design elements can include colour, contrast, symbol size and style, font size and style, basemap style, alternate text for images, and captions for video and audio. Alternative content such as static maps may sometimes be required. Unless otherwise stated, data products available from the data hub are published under Creative Commons licences. Creative Commons licences include terms and conditions about how licensed data products may be used, shared and/or adapted. Depending on the applicable licence, licensed data products may or may not be used for commercial purposes. The applicable Creative Commons licence for specific data is specified in the "Licence" section of the data description. By accessing, sharing and/or adapting licensed data products, you are deemed to have accepted the terms and conditions of the applicable Creative Common licence. For more information about Creative Commons licences, please visit https://creativecommons.org.au/ and https://creativecommons.org/faq/ If you believe that the applicable Creative Commons licence for the data product that you wish to use is overly restrictive for how you would like to use the data product, please email the City. Contact If you have a question, comments, or requests for interactive maps and data, we would love to hear from you. Council business For information on rates, development applications, strategies, reports and other council business, see the City of Sydney's main website.

Reaction of [RuCl2(p-cymene)]2 with 2-azidoethyl isocyanide (1a) or 2-azidophenyl isocyanide (1b) leads to the isocyanide complexes [RuCl2(p-cymene)(1a)] [2a] and [RuCl2(p-cymene)(1b)] [2b], respectively. Complex [2a] reacts with triphenylphosphine to yield the complex with a phosphinimine-substituted isocyanide ligand [3]. The attempted hydrolysis of the phosphinimine group in [3] with HCl·Et2O produced the complex with a protonated phosphinimine ligand [4]Cl instead of the expected complex with a 2-aminoethyl isocyanide ligand. An alternative method for the reduction of the azido group in [2a] and [2b] using FeCl3/NaI has been applied leading to complexes with the 2-amino-substituted isocyanide ligands, which undergo intramolecular ring closure to yield the complexes of type [RuI2(p-cymene)(NHC)], [5a] and [5b].

Rising population density and global mobility are among the reasons why pathogens such as SARS-CoV-2, the virus that causes COVID-19, spread so rapidly across the globe. The policy response to such pandemics will always have to include accurate monitoring of the spread, as this provides one of the few alternatives to total lockdown. However, COVID-19 diagnosis is currently performed almost exclusively by reverse transcription polymerase chain reaction (RT-PCR). Although this is efficient, automatable, and acceptably cheap, reliance on one type of technology comes with serious caveats, as illustrated by recurring reagent and test shortages. We therefore developed an alternative diagnostic test that detects proteolytically digested SARS-CoV-2 proteins using mass spectrometry (MS). We established the Cov-MS consortium, consisting of 15 academic laboratories and several industrial partners to increase applicability, accessibility, sensitivity, and robustness of this kind of SARS-CoV-2 detection. This, in turn, gave rise to the Cov-MS Digital Incubator that allows other laboratories to join the effort, navigate, and share their optimizations and translate the assay into their clinic. As this test relies on viral proteins instead of RNA, it provides an orthogonal and complementary approach to RT-PCR using other reagents that are relatively inexpensive and widely available, as well as orthogonally skilled personnel and different instruments. Data are available via ProteomeXchange with identifier PXD022550.

Feedstock readiness level evaluations are performed for a specific feedstock-conversion process combination and for a particular region. FSRL evaluations complement evaluations of Fuel Readiness Level (FRL) and environmental progress. The table in this dataset collates the results of the FSRL evaluations listed under the Farm2Fly Ag Data Commons datasets to enable users to quickly identify, review, and compare available evaluations. Evaluation scores are explained in the FSRL Checklist and Template available on the NAL Ag Data Commons - scores range from 1 to 9, with higher values indicating greater maturity of the feedstock in each area of assessment (production, market development, policy evaluation and compliance, and linkage to conversion efficiency). The overall score reflects the lowest maturity area within the four assessment areas. Summary data files of the compiled evaluations will be added to the repository on a quarterly basis, and are cumulative (the last quarter will contain the compiled evaluation results from the entire year). To access the newest evaluations that are not yet included in the most recent compilation, visit the Farm 2 Fly program page to view all datasets. The date of update/submission is indicated in the title of the file. Resources in this dataset:Resource Title: FSRL Evaluations Summary Table_Q2_2017. File Name: FSRL Evaluations Summary Table_Q2_2017.xlsxResource Description: As of June 2017: This document summarizes all available databases on the Farm2Fly repository at the Ag Data Commons to enable users to find, identify, and compare among evaluations. Contains graph and grid-enabled data for data visualization. Categories for comparison include Feedstock, Process, and Region, crossed with Ratings for Production, Market, Policy, Linkage, and Overall Score. Ratings are based on the Feedstock Readiness Level scale (1-9), with higher values indicating greater maturity of the process. The Overall Score reflects the least mature of the four assessment areas within a given evaluationResource Title: FSRL Summary Table Data Dictionary. File Name: Data Dictionary Summary Table FSRL 2.csvResource Description: Data dictionary describing the format and entry of data for the FSRL evaluations summary table.Resource Title: FSRL Evaluations Summary Table_Q2_2017.csv. File Name: FSRL Evaluations Summary Table_Q2_2017.csvResource Description: As of June 2017: This document summarizes all available databases on the Farm2Fly repository at the Ag Data Commons to enable users to find, identify, and compare among evaluations. Contains graph and grid-enabled data for data visualization. Categories for comparison include Feedstock, Process, and Region, crossed with Ratings for Production, Market, Policy, Linkage, and Overall Score. Ratings are based on the Feedstock Readiness Level scale (1-9), with higher values indicating greater maturity of the process. The Overall Score reflects the least mature of the four assessment areas within a given evaluation

Template-stripped substrates provide on-demand access to clean, ultraflat gold surfaces, avoiding the need for laborious cleaning procedures or the use of expensive single-crystal electrodes. While these gold/adhesion layer/support sandwich structures are most conveniently prepared through the application of epoxy or optical adhesives, such composites exhibit instabilities in organic solvents that limit their wider application. Here we demonstrate that substrates with solvent-impermeable metal films can be used in previously problematic chemical environments after integration into a protective, custom-built (electrochemical) flow cell. We apply our methodology to probe different self-assembled monolayers, observing reproducible alkanethiol reductive desorption features, an exemplary redox response using 6-(ferrocenyl)hexanethiol, and corroborate findings that cobalt(II) bis(terpyridine) assemblies exhibit a low coverage. This work significantly extends the utility of these substrates, relative to mechanically polished or freshly deposited alternatives, particularly for studies of systems involving adsorbed molecules whose properties are strongly influenced by the nanoscopic features of the metal-solution interface.

This layer is the example dataset provided in the original GitHub Repository for Action 2017.2 on INSPIRE Alternative Encodings from the INSPIRE JRC MIG-T Action 2017.2. It is provided herein as Alternative Encodings Draft GeoJSON imported into ArcGIS Online; this hosted Feature Layer was created from the GeoJSON at the time of import. This layer demonstrates the simplified/flattened address schema developed under MIG-T Action 2017.2 following the guidance provided for community implementations. The remainder of the ArcGIS INSPIRE Open Data streamlined fGDB templates in this collection follow the guidance and document templates laid out by Action 2017.2.Note: This Address point dataset contains only one point as provided through the GitHub Repository.

The X family of DNA polymerases (PolXs) includes the well-studied mammalian polymerases Polβ, Polλ, Polμ, and terminal deoxynucleotidyl transferase (TdT). The template-independent DNA polymerase activity of TdT has been harnessed for applications in enzymatic de novo DNA synthesis, offering a strategy to overcome the limitations of traditional phosphoramidite-based DNA synthesis methods. Close homologues of the mammalian PolXs are present in other vertebrates, while invertebrate PolXs remain unexplored. In this study, we characterize an invertebrate PolX from the extremotolerant tardigrade Ramazzottius varieornatus (RvPolX), and demonstrate that it possesses modest template-independent DNA polymerase activity, despite limited homology to mammalian PolXs (21% sequence identity with TdT). Through a combination of structural modeling, targeted mutagenesis of active site residues, and high-throughput screening under stringent high salt conditions, we identified a synergistic combination of two mutations (G513A and R522I) that led to a significant increase in activity for the incorporation of all four nucleotides, particularly dATP (∼35-fold), yielding a salt-tolerant polymerase with overall higher activity and substrate promiscuity. Under high salt conditions, the engineered RvPolX displays an activity comparable to TdT and a nucleotide selectivity complementary to TdT. As a template-independent polymerase with a low homology to TdT, RvPolX provides an alternative scaffold for further engineering in various biotechnological applications.

GFP loop10 AlphaFold3 structures predicted using the AlphaFold server (https://alphafoldserver.com/): GPF_loop10_AFserver_prediction.zip

Clashed and clash-free GFP loop10 PDB structures predicted using the AlphaFold3 server: GFP_loop10_AFserver_clashed_models.tgz and GFP_loop10_AFserver_clash-free_models.tgz

Calbindin Cal-E65Q1 AlphaFold3 clashed models obtained by standard AlphaFold3 prediction: Cal-E65Q1_clashed_sAF3_200_seeds_5_samples.tgz

Calbindin Cal-E65Q1 AlphaFold3 clash-free models obtained by standard AlphaFold3 prediction: Cal-E65Q1_clash-free_sAF3_200_seeds_5_samples.tgz

Calbindin Cal-E65Q2 AlphaFold3 clashed models obtained by standard AlphaFold3 prediction: Cal-E65Q2_clashed_sAF3_200_seeds_5_samples.tgz

Calbindin Cal-E65Q2 AlphaFold3 clash-free models obtained by standard AlphaFold3 prediction: Cal-E65Q2_clash-free_sAF3_200_seeds_5_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clashed models obtained by AlphaFold3 prediction with targeted column masking in the absence of His-tag (10 seeds, 100 samples per seed): GPF_clashed_his-tag-free_cmAF3_10_seeds_100_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clashed models obtained by AlphaFold3 prediction with targeted column masking in the absence of His-tagi (200 seeds, 5 samples per seed): GPF_clashed_his-tag-free_cmAF3_200_seeds_5_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clashed models obtained by standard AlphaFold3 prediction in the absence of His-tag (10 seeds, 100 samples per seed): GPF_clashed_his-tag-free_sAF3_10_seeds_100_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clashed models obtained by standard AlphaFold3 prediction in the absence of His-tag (200 seeds, 5 samples per seed): GPF_clashed_his-tag-free_sAF3_200_seeds_5_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clashed models obtained by AlphaFold3 prediction without templates in the absence of His-tag (10 seeds, 100 samples per seed): GPF_clashed_his-tag-free_tfAF3_10_seeds_100_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clashed models obtained by AlphaFold3 prediction without templates in the absence of His-tag (200 seeds, 5 samples per seed): GPF_clashed_his-tag-free_tfAF3_200_seeds_5_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clashed models obtained by AlphaFold3 prediction without templates and with targeted column masking in the absence of His-tag (10 seeds, 100 samples per seed): GPF_clashed_his-tag-free_tfcmAF3_10_seeds_100_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clashed models obtained by AlphaFold3 prediction without templates and with targeted column masking in the absence of His-tag (200 seeds, 5 samples per seed): GPF_clashed_his-tag-free_tfcmAF3_200_seeds_5_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clashed models obtained by AlphaFold3 prediction with targeted column masking in the presence of His-tag (10 seeds, 100 samples per seed): GPF_clashed_his-tagged_cmAF3_10_seeds_100_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clashed models obtained by AlphaFold3 prediction with targeted column masking in the presence of His-tag (200 seeds, 5 samples per seed): GPF_clashed_his-tagged_cmAF3_200_seeds_5_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clashed models obtained by standard AlphaFold3 prediction in the presence of His-tag (10 seeds, 100 samples per seed): GPF_clashed_his-tagged_sAF3_10_seeds_100_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clashed models obtained by standard AlphaFold3 prediction in the presence of His-tag (200 seeds, 5 samples per seed): GPF_clashed_his-tagged_sAF3_200_seeds_5_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clashed models obtained by AlphaFold3 prediction without templates in the presence of His-tag (10 seeds, 100 samples per seed): GPF_clashed_his-tagged_tfAF3_10_seeds_100_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clashed models obtained by AlphaFold3 prediction without templates in the presence of His-tag (200 seeds, 5 samples per seed): GPF_clashed_his-tagged_tfAF3_200_seeds_5_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clashed models obtained by AlphaFold3 prediction without templates and with targeted column masking in the presence of His-tag (10 seeds, 100 samples per seed): GPF_clashed_his-tagged_tfcmAF3_10_seeds_100_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clashed models obtained by AlphaFold3 prediction without templates and with targeted column masking in the presence of His-tag (200 seeds, 5 samples per seed): GPF_clashed_his-tagged_tfcmAF3_200_seeds_5_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clash-free models obtained by AlphaFold3 prediction with targeted column masking in the absence of His-tag (10 seeds, 100 samples per seed): GPF_clash_free_his-tag-free_cmAF3_10_seeds_100_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clash-free models obtained by AlphaFold3 prediction with targeted column masking in the absence of His-tag (200 seeds, 5 samples per seed): GPF_clash_free_his-tag-free_cmAF3_200_seeds_5_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clash-free models obtained by standard AlphaFold3 prediction in the absence of His-tag (10 seeds, 100 samples per seed): GPF_clash_free_his-tag-free_sAF3_10_seeds_100_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clash-free models obtained by standard AlphaFold3 prediction in the absence of His-tag (200 seeds, 5 samples per seed): GPF_clash_free_his-tag-free_sAF3_200_seeds_5_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clash-free models obtained by AlphaFold3 prediction without templates in the absence of His-tag (10 seeds, 100 samples per seed): GPF_clash_free_his-tag-free_tfAF3_10_seeds_100_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clash-free models obtained by AlphaFold3 prediction without templates in the absence of His-tag (200 seeds, 5 samples per seed): GPF_clash_free_his-tag-free_tfAF3_200_seeds_5_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clash-free models obtained by AlphaFold3 prediction without templates and with targeted column masking in the absence of His-tag (10 seeds, 100 samples per seed): GPF_clash_free_his-tag-free_tfcmAF3_10_seeds_100_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clash-free models obtained by AlphaFold3 prediction without templates and with targeted column masking in the absence of His-tag (200 seeds, 5 samples per seed): GPF_clash_free_his-tag-free_tfcmAF3_200_seeds_5_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clash-free models obtained by AlphaFold3 prediction with targeted column masking in the presence of His-tag (10 seeds, 100 samples per seed): GPF_clash_free_his-tagged_cmAF3_10_seeds_100_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clash-free models obtained by AlphaFold3 prediction with targeted column masking in the presence of His-tag (200 seeds, 5 samples per seed): GPF_clash_free_his-tagged_cmAF3_200_seeds_5_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clash-free models obtained by standard AlphaFold3 prediction in the presence of His-tag (10 seeds, 100 samples per seed): GPF_clash_free_his-tagged_sAF3_10_seeds_100_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clash-free models obtained by standard AlphaFold3 prediction in the presence of His-tag (200 seeds, 5 samples per seed): GPF_clash_free_his-tagged_sAF3_200_seeds_5_samples.tgz

GFPs loop0, loop1, loop2, loop4, loop6, loop10 and loop14 AlphaFold3 clash-free models obtained by AlphaFold3 prediction without templates in the presence of His-tag (10 seeds, 100 samples per seed): GPF_clash_free_his-tagged_tfAF3_10_seeds_100_samples.tgz

GFPs loop0, loop1,

Gamification is a strategy to stimulate social and human factors (SHF) that influence software development productivity. However, software development teams must improve their productivity to face the challenges of software development organizations. Traditionally, productivity analysis only includes technical factors. Literature shows the importance of SHFs in productivity. Furthermore, gamification elements can contribute to enhancing such factors to improve performance. Thus, to design strategies to enhance a specific SHF, it is essential to identify how gamification elements are related to these factors. The objective of this research is to determine the relationship between gamification elements and SHF that influence the productivity of software development teams. This research included the design of a scoring template to collect data from the experts. The importance was calculated using the Simple Additive Weighting (SAW) method as a tool framed in decision theory. Three criteria were considered: cumulative score, matches in inclusion, and values. The relationships of importance serve as a reference value in designing gamification strategies that promote improved productivity. It extends the path toward analyzing the effect of gamification on the productivity of software development. This relationship facilitates designing and implementing gamification strategies to improve productivity.

Example data sets, syntax files and macros for the tutorials in: Balloo, K., & Winstone, N. E. (2021). A primer on gathering and analysing multi-level quantitative evidence for differential student outcomes in higher education. Frontline Learning Research. https://doi.org/10.14786/flr.v9i2.675

The data for all examples are fictional, and have only been designed to simulate the possible behaviour of institutional data for the purposes of demonstrating the analytical approaches in the primer. No inferences or conclusions should be drawn from the findings of these examples, because the results are not real.

We anticipate that readers can use the example data sets as templates and substitute in their own data.

The functional MRI data were acquired as read out to understand targeted neuromodulation of the pain circuits in an MR guided focused ultrasound (MRgFUS) system. We provide two fMRI data types using different stimulus paradigms i.e., two and three-stimulus condition EPI data 1) Heat & Heat+FUS (Four subjects twenty-seven runs). FUS was applied to the VPL nuclei located in the Thalamus during simultaneous Heat+FUS and FUS only conditions where total acquisition time was 674 and 996 seconds for the two (sub-01-03) and three-condition data (sub-04/05). Alternate Heat and Heat+FUS was presented in the former data set for 16s followed by 30s rest. Stimulus on time for both conditions are Heat: 30 122 214 306 398 490 582sec and Heat+FUS: 76 168 260 352 444 536 628sec. However, 3-conditions data was acquired using a randomized stimulus paradigm where stimulus presentation timings are as follows. Heat: 30 168 398 490 628 812 858sec., Heat+FUS: 76 260 306 444 674 766 904sec. and FUS: 122 214 352 536 582 720 950sec. With TR = 2.0secs an ascending and interleaved slice timing acquisition protocol was followed to acquire EPI data. Uploaded datasets undergone basic pre-processing steps of motion and slice-timing correction as described below with registration steps for group level analysis.

Processing pipeline (Registration and normalization to NMT2.0 template): 1. Motion and slice time correction (mcflirt and slicetimer:) perform using FSL.

1. Register subject anatomic (High-resolution T1 weighted) image to median fMRI image and save registered anatomic image at native resolution.

2. Each functional run is then registered to anatomic image (flirt).

3. Anatomic image is then registered to template (non-rigid using fnirt in 2 steps affine transformation matrix estimated using flirt) and warping is applied to the 4D data sets. An ANTs alternative is also used for non-rigid registration.

This is a demonstration layer implementing streamlined INSPIRE data according to the INSPIRE rules for Alternative Encoding. It is provided as a courtesy and should not be used for any purpose other than demonstration.

This is a demonstration layer implementing streamlined INSPIRE data according to the INSPIRE rules for Alternative Encoding. It is provided as a courtesy and should not be used for any purpose other than demonstration.

A table showing the state of play of Memoranda of Understanding (MoUs) signed by EU national supervisors. ESMA had negotiated the template MoUs regarding the Alternative Investment Fund Directive (AIFMD) with non-EU regulators around the globe. The AIFMD MoUs allow the exchange of information between EU and non-EU supervisors thus enabling non-EU fund managers to market alternative funds within the European Union. The AIFMD covers hedge funds, private equity and real estate funds

This is a demonstration layer implementing streamlined INSPIRE data according to the INSPIRE rules for Alternative Encoding. It is provided as a courtesy and should not be used for any purpose other than demonstration.ArcGIS INSPIRE Open Data is a lightweight solution for European public sector organizations implementing the INSPIRE and PSI-2/Open Data Directives. See the Getting to know ArcGIS INSPIRE Open Data story map to learn more.ArcGIS INSPIRE Open Data on GitHub hosts file Geodatabase (GDB) templates and Alternative Encoding documentation per the Implementing Rules on interoperability of spatial data sets and services (Commission Regulation (EU) No 1089/2010). These resources are provided as-is and are freely available.