Common Pitfalls Research Articles

Single bundle hamstrings autograft preparation techniques for anterior cruciate ligament reconstruction: current concepts.

Anterior cruciate ligament reconstruction (ACLR) is one of the most common orthopedic procedures, and huge variation exists in the surgical technique. Single bundle hamstrings autograft reconstruction is a common method and has good clinical outcomes. A criticism of the hamstrings autograft is a small graft diameter, often less than 8-mm, which has been associated with increased re-rupture rates. Several graft preparation techniques for single bundle hamstrings autograft exist. Perioperative decisions include the number of tendons utilized, number of graft strands, graft configuration, and femoral and tibial fixation methods. Awareness of the minimum tendon and graft length required to produce each graft variation is necessary to avoid common pitfalls. However, intraoperative graft modification is possible to maximize graft diameter, and obtain proper fixation. The objective of this current concepts review is to describe the indications, surgical anatomy, technique, intraoperative tips, clinical outcomes, and complications for single bundle hamstrings autograft preparation techniques in ACLR.

OPTIMIZING MICROMOBILITY INFRASTRUCTURE: A COMPREHENSIVE STUDY OF DOCKING, LOCKING, AND CHARGING SOLUTIONS IN METRO CITIES

The surge in micro-mobility options, such as e-scooters and shared bicycles, has transformed urban transportation, providing a sustainable and efficient means for short-distance travel. Despite their benefits, the rapid expansion of these services has unveiled significant infrastructural challenges, particularly concerning docking, locking, and charging systems. This comprehensive study examines these challenges, evaluates existing practices, and proposes optimized solutions to improve the functionality and user experience of micro-mobility in metro cities. Through a detailed analysis of various technologies, their implementation in different cities, and their impact on urban mobility, this research aims to offer actionable insights. Key areas of focus include standardizing docking mechanisms to enhance interoperability, developing robust and user-friendly locking solutions to prevent theft and vandalism, and creating efficient, sustainable charging infrastructure. By studying successful strategies from cities like Paris, New York, and Singapore, the research identifies best practices and common pitfalls. Recommendations emphasize the importance of standardization, integration with public transportation, sustainability, user education, and supportive policies and regulations. The findings of this study aim to guide city planners and micro-mobility service providers in optimizing infrastructure, thereby enhancing the overall efficiency, security, and sustainability of urban transportation networks.

Debunking common myths in coastal circulation modeling

Despite tremendous progress in algorithm development, computational efficiency and transition into operations over the past two decades, coastal modeling still lacks scientific rigor due to proliferation of many ‘gray’ areas related to various modeling choices made by modelers. In this paper, we propose some guiding principles for the modeling community to improve performance, and we also debunk commonly held myths that make the coastal modeling lack rigor. Using our own experience in developing seamless cross-scale unstructured-grid based models for the past two decades, we describe in unprecedented detail the end-to-end modeling process (i.e., from digital elevation models (DEMs) to mesh generation to post analysis), and demonstrate that defensible modeling is within reach for any end user by following three guiding principles: (1) Bathymetry is a first order forcing in coastal domains and thus should be respected in all aspects of modeling; (2) Oceanographic processes are driven across multiple spatial scales and so models should enable appropriate resolution as needed; and (3) Model assessment should focus on physical processes. Through qualitative and quantitative model assessments, we demonstrate the fundamental role played by bathymetry/topography as embedded in DEMs in making the results defensible, which is unfortunately glossed over in many modeling studies. Focusing on process-based assessment simplifies the calibration process. A major conclusion of this work is that model developers and operators should maximize the scientific rigor for in silico oceanography by avoiding some common pitfalls that rely on error compensation at the expense of representation of physical system processes. We present some best practice procedures for defensive and trustworthy numerical modeling.

AI-driven drug discovery from natural products

The latest review published in Nature Reviews Drug Discovery by Michael W. Mullowney and co-authors focuses on the use of artificial intelligence techniques, specifically machine learning, in natural product drug discovery. The authors discussed various applications of AI in this field, such as genome and metabolome mining, structural characterization of natural products, and predicting targets and biological activities of these compounds. They also highlighted the challenges associated with creating and managing large datasets for training algorithms, as well as strategies to address these obstacles. Additionally, the authors examine common pitfalls in algorithm training and offer suggestions for avoiding them.

Intraoperative frozen section evaluation of ovarian sex cord-stromal tumours and their mimics: a study of 121 cases with emphasis on potential diagnostic pitfalls

Ovarian sex cord-stromal tumours (SCSTs) present diagnostic difficulties during frozen section (FS) consultations due to their diverse morphology. This study aimed to evaluate the accuracy of FS evaluation of SCSTs in our institution, as well as to examine the reasons leading to incorrect FS diagnosis. Cases mimicking SCSTs and diagnosed as such during FS were also highlighted. We analysed 121 ovarian SCST cases and their mimics which underwent FS consultations over a 10-year period, to evaluate FS accuracy, reasons for deferrals and discrepancies. FS diagnoses were concordant, deferred and discrepant compared to the final diagnosis in 50 (41.3%), 39 (32.2%) and 32 (26.5%) cases, respectively. Major discrepancies (9/121, 7.4%) were mostly related to the diagnosis of adult granulosa cell tumour (AGCT). A fibromatous AGCT was misinterpreted as fibroma on FS, while a cystic AGCT was called a benign cyst. Conversely, a mesonephric-like adenocarcinoma, a sertoliform endometrioid carcinoma and a thecoma were misinterpreted as AGCT on FS. Another discrepant case was a Krukenberg tumour with prominent fibromatous stroma in which malignant signet ring cells were overlooked and misinterpreted as fibroma. Minor discrepancies were primarily associated with fibroma (21/23, 91.3%), wherein minor but potentially impactful details such as cellular fibroma and mitotically active cellular fibroma were missed due to sampling issues and misinterpretation as leiomyoma. FS evaluation for ovarian SCSTs demonstrated an overall accuracy of 78.5%, 81.0% and 81.8% for benign, uncertain/low malignant potential and malignant categories, respectively. There was no FS-related adverse clinical impact in all cases with available follow-up information (120/121 cases). Intraoperative FS evaluation of ovarian SCSTs is challenging. A small number of cases were misinterpreted, with AGCTs being the primary group where errors occur. Awareness of common diagnostic pitfalls and difficulties, alongside application of a stepwise approach, including (1) obtaining comprehensive clinical information, (2) thorough macroscopic examination and directed sampling, (3) meticulous microscopic examination with consideration of pitfalls and mimics, (4) effective communication with surgeons in difficult cases, and (5) consultation of subspecialty colleagues in challenging cases, will enhance pathologists' reporting accuracy and management of such cases in the future.

Optimizing Quality Improvement Methods in Practice: A Case Study Approach.

Quality improvement (QI) initiatives help ensure patients are receiving high-quality care. Iterative Plan-Do-Study-Act (PDSA) cycles are used to test change. Data are evaluated over time, and tests of change can be modified or discarded as needed. Health care QI teams lack the flexibility to conduct PDSA cycles, often conducting pre/post quasi-experimental research studies instead. This article reviews a case study of a "personal improvement" initiative as an example of QI methods and data evaluation for an individual trying to lose weight. The purpose of this article is to provide guidance on best practices for conducting QI initiatives; common pitfalls that clinicians may face when leading their own QI initiatives are identified and recommendations to overcome these challenges are discussed. Concepts from this case study, along with supplemental resources provided, can help clinicians optimize QI methodologies in the health care setting.

Regressor cascading for time series forecasting

Time series forecasting is the process of predicting future values of a time series based on its historical data patterns. It is a critical task in many domains, including finance, supply chain management, the environment, and more as accurate forecasts can help businesses and organizations make better decisions and improve their metrics. Although there have been significant advances in time series forecasting systems, thanks to the development of new machine learning algorithms, hardware improvements, and the increasing availability of data, it remains a challenging task. Common pitfalls, especially of single-model approaches include susceptibility to noise and outliers and inability to handle non-stationary data, which can lead to inaccurate and non-robust forecasts. Model-combining approaches, such as averaging the results of multiple predictors to produce a final forecast, are commonly used to mitigate such issues. This work introduces a novel application of Cascade Generalization or Cascading for time series forecasting, where multiple predictors are used sequentially, with each predictor’s output serving as additional input for the next. This methodology aims to overcome the limitations of single-model forecasts and traditional ensembles by incorporating a progressive learning mechanism. We adapt Cascade Generalization specifically for time series data, detailing its implementation and potential for handling complex, dynamic datasets. Our approach was systematically evaluated against traditional two-model averaging ensembles across ten diverse datasets, employing the Root Mean Square Error (RMSE) metric for performance assessment. The results revealed that cascading tends to outperform voting ensembles in most cases. This consistent trend suggests that cascading can be considered a reliable alternative to voting ensembles, showcasing its potential as an effective strategy for improving time series forecasting across a wide range of scenarios.

SeismoNet: A proximal policy optimization-based earthquake early warning system using dilated convolution layers and online data augmentation

In seismic safety, Earthquake Early Warning (EEW) systems are indispensable for mitigating earthquake hazards. These systems strive to quickly evaluate earthquake magnitudes to determine which events warrant immediate alerts. Despite their importance, conventional approaches to rapid earthquake classification face significant hurdles, particularly with the skewed data distribution where instances requiring alerts are far less common. This study proposes an EEW method, called SeismoNet, that leverages a 7-second snapshot of three-dimensional seismic waveforms from the China Earthquake Network Center (CENC). SeismoNet incorporates a trio of dilated convolution layers to distill essential features for effective classification, addressing the critical challenge of data imbalance. Our novel integration of the Off-policy Proximal Policy Optimization (Off-policy PPO) algorithm enriches the model by rewarding accurate classifications, refining its sensitivity to less frequent yet critical seismic events. The training approach is conceptualized as a sequence of decision-making steps, with each seismic sample treated as a unique scenario. The model, acting as a decision-making agent, is rewarded or penalized based on its ability to differentiate between less and more common events. To further enhance classification accuracy, we employ a generative adversarial network (GAN) for dynamic data augmentation and a regularization technique to prevent common pitfalls like mode collapse and training instability. The efficacy of SeismoNet is underscored by its superior performance metrics, including an F-measure of 88.14% and a geometric mean of 90.66%, outperforming existing deep-learning solutions, traditional algorithms, and transfer learning methods. Our exhaustive evaluations, including ablation studies that remove key components like dilated convolutions and PPO, affirm the vital contribution of these elements to SeismoNet’s exceptional capability in EEW. SeismoNet can successfully issue timely warnings for significant seismic events, allowing for essential preventative measures and significantly reducing the potential impact on these communities.

Gene-environment interactions in human health.

Gene-environment interactions (G × E), the interplay of genetic variation with environmental factors, have a pivotal impact on human complex traits and diseases. Statistically, G × E can be assessed by determining the deviation from expectation of predictive models based solely on the phenotypic effects of genetics or environmental exposures. Despite the unprecedented, widespread and diverse use of G × E analytical frameworks, heterogeneity in their application and reporting hinders their applicability in public health. In this Review, we discuss study design considerations as well as G × E analytical frameworks to assess polygenic liability dependent on the environment, to identify specific genetic variants exhibiting G × E, and to characterize environmental context for these dynamics. We conclude with recommendations to address the most common challenges and pitfalls in the conceptualization, methodology and reporting of G × E studies, as well as future directions.

Delineation of chicken immune markers in the era of omics and multicolor flow cytometry

Multiparameter flow cytometry is a routine method in immunological studies incorporated in biomedical, veterinary, agricultural, and wildlife research and routinely used in veterinary clinical laboratories. Its use in the diagnostics of poultry diseases is still limited, but due to the continuous expansion of reagents and cost reductions, this may change in the near future. Although the structure and function of the avian immune system show commonalities with mammals, at the molecular level, there is often low homology across species. The cross-reactivity of mammalian immunological reagents is therefore low, but nevertheless, the list of reagents to study chicken immune cells is increasing. Recent improvement in multicolor antibody panels for chicken cells has resulted in more detailed analysis by flow cytometry and has allowed the discovery of novel leukocyte cell subpopulations. In this article, we present an overview of the reagents and guidance needed to perform multicolor flow cytometry using chicken samples and common pitfalls to avoid.

Operating in the context of evolving climate disclosure practices and frameworks

The standards for companies reporting on the environmental impacts of their operations have changed drastically over the last decade and are about to become even more complex. Initially, limited regulation or guidance available to companies in relation to climate disclosures led to widely diverging practices. This has resulted in many companies facing allegations of ‘greenwashing’ and in some cases litigation and shareholder activism. However, the Australian Government has recently announced it will implement mandatory climate reporting commencing from 1 July 2024 for many Australian companies. Additional climate disclosures – encouraged by pressure from stakeholders and required by the new reporting regulations – will make environmental risk management more complex. The additional disclosures will also provide further opportunities for interference with the approval process for new projects, if there are inconsistencies between a company’s climate disclosures generally and what they have said in their project approval documents. In this paper, we provide an overview of effective strategies and mitigation steps that can be taken to reduce this risk of disruption and disincentivise activist interference more generally, drawing from recent climate change proceedings and experience advising energy and resources clients in relation to climate change and Environmental Social and Governance (ESG) issues more broadly. In particular, we will cover the aspects of the new mandatory climate reporting regime that will be most difficult to comply with; common greenwashing pitfalls; stakeholder/activist engagement planning; and how Boards should approach these issues.

“Stumbling blocks” in the translation of technical documentation

The objective of this study is to clarify the notion of “technical documentation” and explore the challenges and strategies involved in translating this type of text. Technical documentation is crucial in helping customers understand how to use the product effectively that is why it should be well-structured, allowing users to find the information they need quickly and easily. Examples of technical writing include technical manuals, operating instructions, scientific books, monographs, academic papers and articles, and patent translations. However, several obstacles can impede the translation process, potentially resulting in inaccuracies or misunderstandings. Common translation difficulties include untranslatable words, multiple-word meanings, differences in grammatical and syntactic systems, technical and specialized terminology, and “false friends.” The process of translation of such texts is intricate, involving the transfer of information from one language to another while considering cultural, linguistic, and contextual factors. While translating technical texts, the translator may encounter various obstacles, such as linguistic challenges, source text issues, expertise gaps, and realia. Improving the effectiveness of translating technical documentation involves a combination of best translation practices. Effective communication and collaborative efforts are crucial elements for project success. It is also important for professional translators to be vigilant and use the right resources to avoid common pitfalls and deliver high-quality. By combining these strategies, you can enhance the efficiency and accuracy of translating technical documentation, ensuring that the information is effectively communicated to a global audience.

Roles and Regulations for Conducting a Surveys Research in Social Studies

The quality of a survey is of prime importance for accurate, reliable and valid results, Survey teams should implement systematic quality assurance procedures to prevent unacceptable practices and to minimize errors in data collection This paper provides a checklist of good practice in the conduct and reporting of survey research, many researchers seek to publish work that uses questionnaires and surveys. Complexities in the use of surveys and questionnaires and the legal and ethical principles that frame their use are often overlooked, resulting in disappointment for authors who seem unaware of the responsibilities editors and reviewers have in relation to these data collection instruments. meaning a standard at which the results will be regarded as credible. Survey research is sometimes regarded as an easy research approach. However as with any other research approach and method, it is easy to conduct a survey of poor quality rather than one of high quality and real value, its purpose is to assist the novice researcher to produce survey work to a high standard, meaning a standard at which the results will be regarded as credible, and how to calculate sample size and how to select sample. It is not intended to provide a manual of how to conduct a survey, but rather to identify common pitfalls and oversights to be avoided by researchers if their work is to be valid and credible.

How to describe a new species in zoology and avoid mistakes

Abstract Taxonomy is the science of discovering, naming, describing, diagnosing, identifying, and classifying different kinds of taxa, from species to families. It lays the foundation for all of the biological sciences. The rapid increase in both taxonomic descriptions and malpractice in recent decades indicates a need for consistency in the procedure and quality of taxonomic research publications dealing with recognition of new taxa, name changes, and nomenclatural acts. Indeed, there are numerous examples in the recent literature of taxonomic works that fall short of the basic procedures and minimum standards required for naming new species according to the mandatory provisions and recommendations of the International Code of Zoological Nomenclature, accepted standards of taxonomic best practice, and journal editorial policy. Here, we provide practical guidelines of the procedure and key elements required to name, describe, and publish a new animal species or revise the taxonomic status of a species. We then discuss some of the common pitfalls that should be avoided. Mistakes commonly made include failure to read the Code, to review the primary taxonomic literature, to examine type material, to construct the taxon name correctly, to explicitly establish the new taxon, to provide adequate typification, to clearly differentiate the new taxon, to register the publication of the new taxon name in ZooBank, or to publish the name of the new taxon in a manner that is compliant with the Code. We provide some examples of these mistakes, mainly from butterflies (Insecta: Lepidoptera: Papilionoidea) and, to some extent, reptiles because these taxon groups seem to have an unusually high level of poor taxonomic practice.

Chronic Chest Computed Tomography Findings Following COVID-19 Pneumonia

Respiratory symptoms are a frequent manifestation of patients with post-acute sequela of SARS-CoV-2 (PASC), also known as long-COVID. Many cohorts of predominantly hospitalized patients have shown that a significant subset may have persistent chest CT findings for more than 12 months after the acute infection. Proper understanding of the evolving long-term imaging findings and terminology is crucial for accurate imaging interpretation and patient care. The goal of this article is to review the chronic chest CT findings of patients with PASC and common pitfalls.

Practice Briefing: A comprehensive negotiation framework for real estate professionals (Part I)

PurposeThe purpose of this briefing is to highlight the critical importance of negotiation skills in the everyday lives of real estate professionals. It delves into how negotiators must improve their negotiations skills given the negotiation-intensive nature of real estate. It also helps to handle common pitfalls and challenges in negotiations, particularly in the increasingly volatile, uncertain, complex and ambiguous (VUCA) reality of the real estate industry. The briefing offers strategic insights for preparation and negotiation aimed at improving any real estate negotiator’s average performance.Design/methodology/approachThe expert opinion piece combines a literature review on negotiation strategies with practical insights. It addresses the observed under appreciation of negotiation theory and skill, reflecting on real-world real estate negotiations. The goal is to enhance the use and recognition of negotiation theory in the real estate industry. The approach merges theoretical analysis with practical application, offering actionable recommendations to improve negotiation outcomes.FindingsThe negotiation-intensive real estate industry and the transformative impact of VUCA challenges on real estate professionals’ ability to adapt and continuously negotiate successful deals clashes with many real estate’s professional or fixed mind-set over negotiation historically being an art or a talent and mostly being stuck with win-lose strategies. Instead, negotiation is a science that can be learned and deliberately improved to counter stress-induced or fear-based responses that lead negotiators toward suboptimal negotiation strategies, such as win-lose or naive win-win. However, these dynamics are preventable. Well-equipped and well-prepared value win-win negotiators can adopt a growth mind-set, study modern negotiation advice and frameworks to thrive in the negotiation-rich real estate industry and convert even VUCA challenges into an amazing source of value.Practical implicationsReal estate professionals can become more aware of which and how current obstacles and poor choices negatively contribute to their negotiation performance. It contrasts win-lose and win-win strategic frameworks to enable real estate professionals to become more sophisticated when choosing their negotiation strategies. The briefing also helps real estate professionals expand their negotiation repertoire towards improved strategic flexibility when managing the evolving real estate profession reality and challenges.Originality/valueThe originality and value of the briefing lie in its comprehensive approach to addressing the negotiation challenges faced by real estate professionals. It offers a holistic view of real estate negotiation, advocating for a paradigm shift from traditional win-lose tactics to a collaborative, value win-win approach. The briefing integrates modern negotiation theory and emphasises ethical practices, providing practical strategies and best practices for professionals to improve their skills and adapt to industry changes. By empowering real estate professionals with knowledge and tools to navigate negotiations effectively, the briefing contributes to the overall success and professionalism of the industry.

How to Use and Report on p-values.

The use of the p-value in quantitative research, particularly its threshold of "P < 0.05" for determining "statistical significance," has long been a cornerstone of statistical analysis in research. However, this standard has been increasingly scrutinized for its potential to mislead findings, especially when the practical significance, the number of comparisons, or the suitability of statistical tests are not properly considered. In response to controversy around use of p-values, the American Statistical Association published a statement in 2016 that challenged the research community to abandon the term "statistically significant". This stance has been echoed by leading scientific journals to urge a significant reduction or complete elimination in the reliance on p-values when reporting results. To provide guidance to researchers in health professions education, this paper provides a succinct overview of the ongoing debate regarding the use of p-values and the definition of p-values. It reflects on the controversy by highlighting the common pitfalls associated with p-value interpretation and usage, such as misinterpretation, overemphasis, and false dichotomization between "significant" and "non-significant" results. This paper also outlines specific recommendations for the effective use of p-values in statistical reporting including the importance of reporting effect sizes, confidence intervals, the null hypothesis, and conducting sensitivity analyses for appropriate interpretation. These considerations aim to guide researchers toward a more nuanced and informative use of p-values.

Predicting non-muscle invasive bladder cancer outcomes using artificial intelligence: a systematic review using APPRAISE-AI.

Accurate prediction of recurrence and progression in non-muscle invasive bladder cancer (NMIBC) is essential to inform management and eligibility for clinical trials. Despite substantial interest in developing artificial intelligence (AI) applications in NMIBC, their clinical readiness remains unclear. This systematic review aimed to critically appraise AI studies predicting NMIBC outcomes, and to identify common methodological and reporting pitfalls. MEDLINE, EMBASE, Web of Science, and Scopus were searched from inception to February 5th, 2024 for AI studies predicting NMIBC recurrence or progression. APPRAISE-AI was used to assess methodological and reporting quality of these studies. Performance between AI and non-AI approaches included within these studies were compared. A total of 15 studies (five on recurrence, four on progression, and six on both) were included. All studies were retrospective, with a median follow-up of 71 months (IQR 32-93) and median cohort size of 125 (IQR 93-309). Most studies were low quality, with only one classified as high quality. While AI models generally outperformed non-AI approaches with respect to accuracy, c-index, sensitivity, and specificity, this margin of benefit varied with study quality (median absolute performance difference was 10 for low, 22 for moderate, and 4 for high quality studies). Common pitfalls included dataset limitations, heterogeneous outcome definitions, methodological flaws, suboptimal model evaluation, and reproducibility issues. Recommendations to address these challenges are proposed. These findings emphasise the need for collaborative efforts between urological and AI communities paired with rigorous methodologies to develop higher quality models, enabling AI to reach its potential in enhancing NMIBC care.

Lessons learned: overcoming common challenges in reconstructing the SARS-CoV-2 genome from short-read sequencing data via CoVpipe2

Background Accurate genome sequences form the basis for genomic surveillance programs, the added value of which was impressively demonstrated during the COVID-19 pandemic by tracing transmission chains, discovering new viral lineages and mutations, and assessing them for infectiousness and resistance to available treatments. Amplicon strategies employing Illumina sequencing have become widely established for variant detection and reference-based reconstruction of SARS-CoV-2 genomes, and are routine bioinformatics tasks. Yet, specific challenges arise when analyzing amplicon data, for example, when crucial and even lineage-determining mutations occur near primer sites. Methods We present CoVpipe2, a bioinformatics workflow developed at the Public Health Institute of Germany to reconstruct SARS-CoV-2 genomes based on short-read sequencing data accurately. The decisive factor here is the reliable, accurate, and rapid reconstruction of genomes, considering the specifics of the used sequencing protocol. Besides fundamental tasks like quality control, mapping, variant calling, and consensus generation, we also implemented additional features to ease the detection of mixed samples and recombinants. Results We highlight common pitfalls in primer clipping, detecting heterozygote variants, and dealing with low-coverage regions and deletions. We introduce CoVpipe2 to address the above challenges and have compared and successfully validated the pipeline against selected publicly available benchmark datasets. CoVpipe2 features high usability, reproducibility, and a modular design that specifically addresses the characteristics of short-read amplicon protocols but can also be used for whole-genome short-read sequencing data. Conclusions CoVpipe2 has seen multiple improvement cycles and is continuously maintained alongside frequently updated primer schemes and new developments in the scientific community. Our pipeline is easy to set up and use and can serve as a blueprint for other pathogens in the future due to its flexibility and modularity, providing a long-term perspective for continuous support. CoVpipe2 is written in Nextflow and is freely accessible from \href{https://github.com/rki-mf1/CoVpipe2}{github.com/rki-mf1/CoVpipe2} under the GPL3 license.

Mix-method toolbox for monitoring greenhouse gas production and microbiome responses to soil amendments

In this study, we adopt an interdisciplinary approach, integrating agronomic field experiments with soil chemistry, molecular biology techniques, and statistics to investigate the impact of organic residue amendments, such as vinasse (a by-product of sugarcane ethanol production), on soil microbiome and greenhouse gas (GHG) production. The research investigates the effects of distinct disturbances, including organic residue application alone or combined with inorganic N fertilizer on the environment. The methods assess soil microbiome dynamics (composition and function), GHG emissions, and plant productivity. Detailed steps for field experimental setup, soil sampling, soil chemical analyses, determination of bacterial and fungal community diversity, quantification of genes related to nitrification and denitrification pathways, measurement and analysis of gas fluxes (N2O, CH4, and CO2), and determination of plant productivity are provided. The outcomes of the methods are detailed in our publications (Lourenço et al., 2018a; Lourenço et al., 2018b; Lourenço et al., 2019; Lourenço et al., 2020). Additionally, the statistical methods and scripts used for analyzing large datasets are outlined. The aim is to assist researchers by addressing common challenges in large-scale field experiments, offering practical recommendations to avoid common pitfalls, and proposing potential analyses, thereby encouraging collaboration among diverse research groups.•Interdisciplinary methods and scientific questions allow for exploring broader interconnected environmental problems.•The proposed method can serve as a model and protocol for evaluating the impact of soil amendments on soil microbiome, GHG emissions, and plant productivity, promoting more sustainable management practices.•Time-series data can offer detailed insights into specific ecosystems, particularly concerning soil microbiota (taxonomy and functions).