A Saturated Ecosystem

The explosion has been brutal. In the international journals I work with, submissions have surged by over 30% in recent years. Rejection rates now reach 70% in some disciplines. Even more striking: the geography of research has been radically transformed. Where the United States dominated fifteen years ago, China now represents over 40% of global submissions in many fields.

This quantitative explosion masks deeper transformations. The emergence of AI-assisted writing tools facilitates large-scale manuscript production. Some countries have implemented financial incentives that can reach $43,000 for a publication in Nature or Science - peaks observed in China between 2008 and 2016.

The result? Between 10% and 30% of scientific articles remain uncited several years after publication, depending on the discipline. This isn't always a quality issue: it's often a discoverability problem. In this saturated environment, visibility has become a scientific skill in its own right.

Before Submission: Preparing for Discoverability

Visibility work begins well before submission. A title of 8-15 words, precise without jargon, an explicit abstract, and keywords covering the terminological variants of your field significantly improve findability via Google Scholar. Search engines first index the metadata you provide them.

Journal selection deserves strategic reflection that's often overlooked. The question isn't "which journal is most prestigious?" but "where does my audience actually read?" Publishing in a highly ranked journal but in a field distant from yours may flatter the ego, but sometimes has less impact than a second-tier journal that's central to your niche.

Metrics like CiteScore (measuring average citations over 4 years) or the "Cites per document" indicator on SCImago help choose a journal actually read by your peers. Better to publish "in the conversation" than "beside the conversation."

Open Access: A Global Opportunity

The open access movement has created unprecedented opportunities for research visibility. Repository platforms like arXiv (multidisciplinary), medRxiv (health), or EarthArXiv (Earth sciences) accelerate the circulation of ideas. In many fields, a preprint signals scientific openness, generates early feedback, and can initiate citations before formal publication.

For researchers in Europe, national agreements often allow avoiding open access publication fees. Simply linking your institutional email and ORCID identifier can automatically provide these benefits.

This "green" strategy ensures worldwide free availability of your work. Concretely: even if your article is published in a journal with a $3,000 annual subscription, anyone in the world can freely access your repository version after the embargo period.

Writing to Be Discovered

A principle I now apply: one idea equals one article, adaptable according to disciplines. Multi-subject manuscripts get lost in the crowd. I prefer short formats for targeted results - they're often more read and cited than very dense articles.

It's about doing "academic SEO": aligning title, abstract, keywords, and subheadings with your audience's typical queries. Make your figures self-sufficient with explanatory captions and clear licenses (like Creative Commons) to encourage reuse.

Systematically deposit your datasets and scripts following FAIR principles (Findable, Accessible, Interoperable, Reusable). A Data Management Plan from the beginning of a project facilitates this approach. A GitHub repository with DOI via Zenodo increases reuse and mentions, making you discoverable by completely different audiences.

For licenses, favor Creative Commons (CC-BY for example) which allows reuse with attribution. Open access isn't limited to "Green" (repository deposit) or "Gold" (direct paid publication): the emerging "Diamond" model offers free access for everyone.

After Publication: Orchestrating Dissemination

Publication day isn't the end of the story, it's the beginning of your research's public life. I now apply a reproducible plan:

• Immediate: Deposit the accepted version in institutional repository with ORCID synchronization. This step takes 10 minutes and ensures permanent archiving.

• Professional: Targeted announcement on LinkedIn in 3-4 sentences explaining the question your article answers, a key result, and why it matters. This is where you'll reach decision-makers and industry leaders who can transform your results into action.

• General public: A short popularization post on Medium or a blog explaining the "why" and potential uses. Some research deserves to influence public debate.

• Academic: Sharing on ResearchGate and Academia.edu while respecting publisher policies.

• Necessary vigilance: Only massively disseminate quality work - otherwise we contribute to the information noise we denounce.

Structural Solutions Needed

Beyond individual strategies, reforms are necessary. Researchers excel in fundamental research (Nobel prizes and Fields medals attest to this). But they are increasingly absent from editorial boards of major applied journals, particularly in strategic fields. Structural solutions could include:

• Revising valorization policies by placing quality at the center, with minimum thresholds over 2 years rather than a quantity race

• Recruiting dedicated administrative staff to free researchers from management tasks

• Recognizing editorial functions in career evaluation

• Training in "publication literacy" from the PhD level

In a saturated landscape where we must navigate between global quantitative explosion and quality maintenance, visibility becomes an impact multiplier. An article in a top journal is good. An article that people find, read, use, and build upon for their own research is infinitely better.

Visibility isn't academic vanity: it's the necessary condition for years of research to find their social, economic, and scientific utility.

Practical Toolkit

Before Submission

• Informative title 8-15 words, abstract with audience keywords

• Journal strategy: scope > blind prestige

• Metadata preparation and data management

Upon Acceptance

• Immediate deposit of accepted version in repository + ORCID synchronization

• Preparation of dissemination versions

Post-Publication

• LinkedIn for decision-makers (3-4 sentences, why important)

• Medium/blog for popularization

• Academic networks for peers

Data and Code

• Data Management Plan from the beginning

• GitHub repository with Zenodo DOI for reuse

• Explicit licenses (Creative Commons CC-BY recommended)

• Respect FAIR principles: Findable, Accessible, Interoperable, Reusable

Preprint Servers

• arXiv (multidisciplinary), medRxiv (health), EarthArXiv (Earth sciences)

• Signals openness and scientific priority

• Generates early feedback and anticipated citations

Open Access

• Green: repository deposit after embargo period

• Gold: direct open access publication (with APC)

• Diamond: free access without fees (emerging model)

Additional Resources

• Complete guide to open access

• Academic visibility strategies

• Applied research positioning

• Editorial ecosystem and current challenges

• Data management best practices

Key References

Quan, W., Chen, B., & Shu, F. (2017). Publish or impoverish: An investigation of the monetary reward system of science in China (1999-2016). arXiv preprint arXiv:1707.01162.

Evans, J. A., & Reimer, J. (2009). Open access and global participation in science. Science, 323(5917), 1025.

Larivière, V., Haustein, S., & Mongeon, P. (2015). The oligopoly of academic publishers in the digital era. PLOS One, 10(6), e0127502.

Tennant, J. P., et al. (2016). The academic, economic and societal impacts of Open Access. F1000Research, 5, 632.

Forecasting future solar power plant production is essential to continue the development of photovoltaic energy and increase its share in the energy mix for a more sustainable future. Accurate solar radiation forecasting greatly improves the balance maintenance between energy supply and demand and grid management performance. This study assesses the influence of input selection on short-term global horizontal irradiance (GHI) forecasting across two contrasting Algerian climates: arid Ghardaïa and coastal Algiers. Eight feature selection methods (Pearson, Spearman, Mutual Information (MI), LASSO, SHAP (GB and RF), and RFE (GB and RF)) are evaluated using a Gradient Boosting model over horizons from one to six hours ahead. Input relevance depends on both the location and forecast horizon. At t+1, MI achieves the best results in Ghardaïa (nMAE = 6.44%), while LASSO performs best in Algiers (nMAE = 10.82%). At t+6, SHAP- and RFE-based methods yield the lowest errors in Ghardaïa (nMAE = 17.17%), and RFE-GB leads in Algiers (nMAE = 28.13%). Although performance gaps between methods remain moderate, relative improvements reach up to 30.28% in Ghardaïa and 12.86% in Algiers. These findings confirm that feature selection significantly enhances accuracy (especially at extended horizons) and suggest that simpler methods such as MI or LASSO can remain effective, depending on the climate context and forecast horizon.

Clearsky models are widely used in solar energy for many applications such as quality control, resource assessment, satellite-base irradiance estimation and forecasting. However, their use in forecasting and nowcasting is associated with a number of challenges. Synchronization errors, reliance on the Clearsky index (ratio of the global horizontal irradiance to its cloud-free counterpart) and high sensitivity of the clearsky model to errors in aerosol optical depth at low solar elevation limit their added value in real-time applications. This paper explores the feasibility of short-term forecasting without relying on a clearsky model. We propose a Clearsky-Free forecasting approach using Extreme Learning Machine (ELM) models. ELM learns daily periodicity and local variability directly from raw Global Horizontal Irradiance (GHI) data. It eliminates the need for Clearsky normalization, simplifying the forecasting process and improving scalability. Our approach is a non-linear adaptative statistical method that implicitly learns the irradiance in cloud-free conditions removing the need for an clear-sky model and the related operational issues. Deterministic and probabilistic results are compared to traditional benchmarks, including ARMA with McClear-generated Clearsky data and quantile regression for probabilistic forecasts. ELM matches or outperforms these methods, providing accurate predictions and robust uncertainty quantification. This approach offers a simple, efficient solution for real-time solar forecasting. By overcoming the stationarization process limitations based on usual multiplicative scheme Clearsky models, it provides a flexible and reliable framework for modern energy systems.

Accurate solar energy output prediction is fundamental to integrating renewable energy sources into electrical grids, maintaining system stability, and enabling effective energy management. However, conventional error metrics—such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Skill Scores (SS)—fail to capture the multidimensional complexity of solar irradiance forecasting. These metrics lack sensitivity to forecastability, rely on arbitrary baselines (e.g., clear-sky models), and are poorly adapted to operational needs. 

To address these limitations, this study introduces the NICE^k metrics (Normalized Informed Comparison of Errors, with k = 1, 2, 3, Σ), a novel evaluation framework offering a robust, interpretable, and multidimensional assessment of forecasting models. Each NICE^k score corresponds to a specific L^k norm: NICE^1 emphasizes average errors, NICE^2 highlights large deviations, NICE^3 focuses on outliers, and NICE^Σ combines all three dimensions. 

The methodology combines synthetic Monte Carlo simulations with real-world data from the Spanish SIAR network, encompassing 68 meteorological stations in diverse climatic regions. Forecasting models evaluated include autoregressive approaches, Extreme Learning Machines, and smart persistence. Results show that theoretical and empirical NICE^k values converge only when strong statistical assumptions are met (e.g., R² ≈ 1.0 for NICE^2). Most importantly, the composite metric NICE^Σ consistently outperforms conventional metrics in discriminating between models (e.g., p-values < 0.05 for NICE^Σ vs > 0.05 for nRMSE or nMAE). 

Across increasing forecast horizons, NICE^Σ yields consistently significant p-values (from 10⁻⁶ to 0.004), while nRMSE and nMAE often fail to reach statistical significance. Furthermore, traditional metrics (nRMSE, nMAE, nMBE, R²) cannot reliably distinguish between models in head-to-head comparisons. In contrast, the NICE^k family demonstrates superior statistical discrimination (p < 0.001), broader variance distributions, and better inter-study comparability. 

This study confirms the theoretical and empirical validity of the NICE^k framework and highlights its operational relevance. It establishes NICE^k as a robust, unified, and interpretable alternative to conventional metrics for evaluating deterministic solar forecasting models.