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NASA Picks 3 Companies to Help Astronauts Drive Around the Moon

The agency’s future moon buggies will reach speeds of 9.3 miles per hour and will be capable of self-driving.

By Kenneth Chang

NASA will be renting some cool wheels to drive around the moon.

Space agency officials announced on Wednesday that they have hired three companies to come up with preliminary designs for vehicles to take NASA astronauts around the lunar south polar region in the coming years. After the astronauts return to Earth, these vehicles would be able to self-drive around as robotic explorers, similar to NASA’s rovers on Mars.

The self-driving capability would also allow the vehicle to meet the next astronaut mission at a different location.

“Where it will go, there are no roads,” Jacob Bleacher, the chief exploration scientist at NASA, said at a news conference on Wednesday. “Its mobility will fundamentally change our view of the moon.”

The companies are Intuitive Machines of Houston, which in February successfully landed a robotic spacecraft on the moon ; Lunar Outpost of Golden, Colo.; and Venturi Astrolab of Hawthorne, Calif. Only one of the three will actually build a vehicle for NASA and send it to the moon.

NASA had asked for proposals of what it called the lunar terrain vehicle, or L.T.V., that could drive at speeds up to 9.3 miles per hour, travel a dozen miles on a single charge and allow astronauts to drive around for eight hours.

The agency will work with the three companies for a year to further develop their designs. Then NASA will choose one of them for the demonstration phase.

The L.T.V. will not be ready in time for the astronauts of Artemis III, the first landing in NASA’s return-to-the-moon program , which is currently scheduled for 2026 .

The plan is for the L.T.V. to be on the lunar surface ahead of Artemis V, the third astronaut landing that is expected in 2030, said Lara Kearney, manager of the extravehicular activity and human surface mobility program at the NASA Johnson Space Center.

“If they can get there earlier, we’ll take it earlier,” Ms. Kearney said.

The L.T.V. contract will be worth up to $4.6 billion over the next 15 years — five years of development and then a decade of operations on the moon, most of it going to the winner of this competition. But Ms. Kearney said the contracts allow NASA to later finance the development of additional rovers, or allow other companies to compete in the future.

The contract follows NASA’s recent strategy of purchasing services rather than hardware.

In the past, NASA paid aerospace companies to build vehicles that it then owned and operated. That included the Saturn V rocket, the space shuttles and the lunar roving vehicles — popularly known as moon buggies — that astronauts drove on the moon during the last three Apollo missions in 1971 and 1972.

The new approach has proved successful and less expensive for the transportation of cargo and astronauts to the International Space Station. NASA now pays companies, notably Elon Musk’s SpaceX, fixed fees for those services, more akin to plane tickets or FedEx shipments.

For the company chosen to build the L.T.V., the vehicle will remain its property, and that company will be able to rent it to other customers when it is not needed by NASA.

“It’s commercially available for us as a commercial business to sell capacity on that rover,” said Steve Altemus, the chief executive of Intuitive Machines, “and do that for international partners and for other commercial companies and space agencies around the world.”

The competition created alliances between small startups and larger, more established aerospace companies, as well as car companies. The Intuitive Machines team includes Boeing, Northrop Grumman and Michelin, the tire maker. Lunar Outpost added to its team Lockheed Martin, Goodyear and General Motors, which had helped design the Apollo moon buggies.

Astrolab is working with Axiom Space of Houston, which has sent private astronauts to the space station and is building a commercial module to the International Space Station. Astrolab announced last year that it had signed an agreement to send one of its rovers to the moon on a SpaceX Starship rocket as early as 2026. That mission is independent of whether it is selected by NASA, a company spokesman said.

While Lunar Outpost is competing with Intuitive Machines on this contract, it plans to work with the company separately, sending smaller robotic rovers to the moon on the company’s lunar landers.

Kenneth Chang , a science reporter at The Times, covers NASA and the solar system, and research closer to Earth. More about Kenneth Chang

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Characteristics of scientific articles on COVID-19 published during the initial 3 months of the pandemic

Nicola di girolamo.

1 Center for Veterinary Health Sciences, Oklahoma State University, 2065 W. Farm Road, Stillwater, OK 74078 USA

2 EBMVet, Via Sigismondo Trecchi 20, 26100 Cremona, CR Italy

Reint Meursinge Reynders

3 Department of Oral and Maxillofacial Surgery, Amsterdam University Medical Center, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands

4 Private Practice of Orthodontics, Via Matteo Bandello 15, 20123 Milan, Italy

Associated Data

The database including all data used will be available on Open Science Framework after a period of 6 months from publication during which the authors will still be working on other publications based on this database.

The COVID-19 pandemic has been characterized by an unprecedented amount of published scientific articles. The aim of this study is to assess the type of articles published during the first 3 months of the COVID-19 pandemic and to compare them with articles published during 2009 H1N1 swine influenza pandemic. Two operators independently extracted and assessed all articles on COVID-19 and on H1N1 swine influenza that had an abstract and were indexed in PubMed during the first 3 months of these pandemics. Of the 2482 articles retrieved on COVID-19, 1165 were included. Over half of them were secondary articles (590, 50.6%). Common primary articles were: human medical research (340, 59.1%), in silico studies (182, 31.7%) and in vitro studies (26, 4.5%). Of the human medical research, the vast majority were observational studies and cases series, followed by single case reports and one randomized controlled trial. Secondary articles were mainly reviews, viewpoints and editorials (373, 63.2%). Limitations were reported in 42 out of 1165 abstracts (3.6%), with 10 abstracts reporting actual methodological limitations. In a similar timeframe, there were 223 articles published on the H1N1 pandemic in 2009. During the COVID-19 pandemic there was a higher prevalence of reviews and guidance articles and a lower prevalence of in vitro and animal research studies compared with the H1N1 pandemic. In conclusions, compared to the H1N1 pandemic, the majority of early publications on COVID-19 does not provide new information, possibly diluting the original data published on this disease and consequently slowing down the development of a valid knowledge base on this disease. Also, only a negligible number of published articles reports limitations in the abstracts, hindering a rapid interpretation of their shortcomings. Researchers, peer reviewers, and editors should take action to flatten the curve of secondary articles.

Electronic supplementary material

The online version of this article (10.1007/s11192-020-03632-0) contains supplementary material, which is available to authorized users.

Introduction

The WHO was informed on December 31st 2019 that a number of patients were hospitalized for a pneumonia of unknown etiology in Wuhan City, China (WHO 2020 ). In the following week, molecular diagnostic techniques identified a novel coronavirus (SARS-CoV-2) as responsible of the pneumonia (WHO 2020 ). That was the first known outbreak of the disease that was lately renamed COVID-19. The SARS-CoV-2 has high transmissibility and an asymptomatic incubation period, during which transmission may occur (Huang et al. 2020 ; Rothe et al. 2020 ). Due to its characteristics, up to June 19th 2020, more than 200 countries have been affected by this disease (Centers for Disease Control and Prevention 2020 ), resulting in the most relevant pandemic in recent history.

Past coronavirus outbreaks have led to prolific publishing on these health issues (Kagan et al. 2020 ). Similar surges in publication numbers were seen with earlier outbreaks of viral diseases like SARS, MERS, Ebola, and Swine Flu, which then dropped drastically when these diseases were contained (Kagan et al. 2020 ). The production of a large bulk of literature in the early phases of such outbreaks can create a severe burden for policy makers who need to make rapid evidence-based decisions for controlling the pandemic. They have to scrutinize large quantities of scientific publications to assess what original research has been published on this topic and appraise the quality of this research. It is especially important to identify articles that report novel information to articles that summarize or comment on existing information, i.e. primary versus secondary articles.

In this research study we have replicated this process and report on the characteristics of articles published in the first trimester of the COVID-19 pandemic. Patients, health care professionals, policy makers, and the general public want to know what has been published on this health issue and what quality of research was available for decision making. Researchers, editors, peer reviewers, and publishing companies get an insight into the quantity and quality of articles that they contributed. The purpose of the present meta-epidemiological study is to identify the proportion of primary and secondary articles, to identify the proportion of studies that report limitations in their abstracts and to compare publishing patterns during COVID-19 and during the only other pandemic of the XXI century, the 2009 H1N1 swine influenza.

We performed a cross-sectional study of articles published during the initial period of the COVID-19 pandemic. We adopted the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement (von Elm et al. 2007 ) for reporting this study and included its checklist (Additional file 1). We implemented two changes compared with our original protocol. We did not assess whether studies originated as multi center research projects, because we realized that this information could not be extracted reliably from every article. To fulfill the request of one of the peer reviewers of this manuscript we included a new section: ‘Calculation of articles per population, per gross domestic product (GDP) and per declared COVID-19 cases’.

Eligibility criteria and search strategy

All articles retrieved on Medline through searching PubMed with the string “(COVID-19 OR COVID)” on April 2nd 2020 at 900 pm Central Standard Time, after application of the filter ‘Abstract’, were eligible for inclusion in the study. The full search strategy is given in Additional file 2A. Any type of article published on COVID-19 was eligible. This implies that a broad spectrum of articles ranging from letters to the editors to randomized controlled trials were eligible for inclusion. Articles were eligible if they included any terminology related to SARS-CoV-2 (including but not limited to: SARS-CoV-2, COVID, COVID-19, novel coronavirus 2019), in the title, abstract or full-text. For example, in vitro articles in which other viruses (e.g., MERS-CoV, SARS-CoV) were used as a proxy for SARS-CoV-2, were still eligible for inclusion in the study when the authors mentioned SARS-CoV-2 or synonyms in the manuscript. No eligibility criteria were applied to specific participants, interventions, comparators, outcomes, endpoints, language or settings of the articles. Articles that did not present an English abstract, as well as correspondence to previous research studies and errata were excluded.

Selection of articles and data extraction

We extracted from each article the following information: ‘title’, ‘abstract’, ‘DOI’’, ‘number of authors’, ‘journal’, ‘date of creation’, ‘first author’, ‘country of the first institution of the first author’, ‘article type’ (primary/secondary, defined below), ‘study design’ (defined below), ‘number of patients included’ (only for human medical research), ‘presence of objective in the abstract’, ‘presence of limitation in the abstract’, ‘main conclusion’ (Additional file 2B). Two operators (ND and RMR) conducted the selection of articles and data extraction procedures independently. These procedures were pilot tested on 40 articles to calibrate both operators and to fine-tune the data extraction forms. Disagreements during the selection of articles and data extraction procedures were resolved through discussions between both operators. Consultation with a third operator in the case of persisting disagreements was not necessary.

Classifications of the included articles

We used a multi-step approach in order to classify each article included in the study. The overarching final classification was whether an article was primary, i.e., adding original scientific information to the literature, or secondary. Primary articles refer to original research studies and secondary articles refer to perspectives and syntheses of the available knowledge on COVID-19 such as, viewpoints, commentaries, guidelines, reviews etc. (Table  1 ). Our classification of included articles was not exclusively based on the labels assigned to these articles, because study designs are often mislabeled by the authors themselves (Esene et al. 2014 ). We therefore first assessed the validity of such labeling by evaluating the study design in the full-text, before making our final classifications of a study. Primary articles were divided in five categories, i.e., human medical research, in silico, in vitro, animal research and human non-medical research, and then in subcategories (Table  1 ). Many published articles included multiple analytical steps and could therefore represent one or more of these categories. For example, in a study samples could be obtained from several patients—‘human medical research’—then transferred to a petri dish and cultured—‘in vitro research’—and the results of the growth could then be modelled using computer simulation—‘in silico research’. Since the purpose of the present meta-epidemiological study is to define the amount of information obtained that is actually relevant for healthcare policy makers and clinicians, the categorization of the articles was performed considering the theoretical order of evidence provided by different study settings, i.e., human medical research > animal research > in vitro research > in silico research. Therefore, if a study could fit in multiple categories, we assigned the highest category based on that order. In the example above, the study would have been categorized as ‘human medical research’. Similarly, a study including abundant in vitro (or in silico) research and a final part on an animal model would have been categorized as ‘animal research’.

Table 1

Criteria employed to classify the included articles

Calculation of articles per population, per gross domestic product (GDP) and per declared COVID-19 cases

Based on the suggestions of one of the reviewers during the peer-review process of the article, we have extracted population, GDP and number of declared COVID-19 cases for the ten countries that have published most articles. The population per country for the year 2018 was extracted from The World Bank website which makes data publicly available. The year 2018 was the most recent year available. The data ‘population, total’ was extracted. Country GDP for the year 2019 was extracted from the dataset World Economic Outlook, online available in the International Monetary Fund website. Number of declared cases on March 2nd 2020 (1 month prior our data extraction) was extracted from the data published by the European Centre for Disease Prevention and Control (ECDC), publicly available online. The number of articles published per million inhabitants was calculated dividing the total number of articles published by each country for the country population and multiplying it by 1.000.000. The number of articles published per GDP unit was calculated dividing the total number of articles published by each country for the country GDP. The number of articles published per 100 declared cases was calculated dividing the total number of articles published by each country for the number of cases and multiplying it by 100.

Abstract assessment

We screened all abstracts to assess whether the objectives and the limitations of the article were reported or not. An abstract was defined as any type of information reported in the area for abstracts in PubMed. Objectives were defined as ‘reported’ when the abstract reported any type of statement that explained the purpose of the article. Limitations were defined as ‘reported’ when the abstract reported any type of statement that explained limitation(s) of the article.

Limitations were further subdivided in ‘methodological limitation’ and ‘general limitation’; articles were classified as reporting a ‘methodological limitation’ when they stated in the abstract the presence of at least 1 limitation inherent to the article design (e.g., “due to the inclusion of a convenient sample this report is at risk of selection bias”); articles were classified as reporting a ‘general limitation’ when they stated in the abstract the presence of a limitation that was not inherent to the article’s design (e.g., “more evidence is needed”, “further research on the topic is warranted”).

Selection, extraction & classification of articles on H1N1 2009 pandemic

We performed a search & extraction in an analogous way for articles published during the early phases of the H1N1 2009 pandemic. We performed a search on Medline through PubMed with the string “H1N1”. We applied the text availability filter “Abstract” and ordered the articles by date of publication. Our search strategy is reported in Additional file 2C. We extracted all the articles retrieved through the “Save” function on a.csv file. We established which was the first published article on the H1N1 2009 pandemic (Centers for Disease Control and Prevention 2009) based on a CDC summary (Centers for Disease Control and Prevention 2010 ). We then included three full months of publications, i.e. from April 25th 2009 to July 25th 2009. Similar to articles related to COVID-19, articles were eligible for inclusion if they reported terminology related to “H1N1”, “swine flu” or “the current pandemic”, among others. From the articles included, we extracted country of origin, language of full-text, type of study and study design were extracted in a similar fashion as was done for the COVID-19 articles. The selection, extraction and classification of articles was performed independently by two operators (ND and RMR) and disagreements were resolved by consensus.

Outcomes and prioritization

The primary outcomes of this meta-epidemiological study were:

• The proportion of primary articles over the total number of articles with an abstract published during the first 3 months of the COVID-19 pandemic.
• The proportion of articles reporting limitations in their abstracts.
• The proportion of article types during COVID-19 and during the 2009 H1N1 swine flu pandemic.

The associations of any of these outcomes with other individual article characteristics were secondary outcomes.

Statistical analysis

Descriptive statistics are expressed as medians with interquartile ranges (IQR) and ranges or absolute counts and percentages. Multivariable logistic regression models were developed to explore the factors associated with the primary outcomes and provide odds ratio adjusted for confounders. Variables retained clinically significant were entered in the models regardless of their statistical significance. Goodness of fit was assessed with the Hosmer–Lemeshow test and Nagelkerke R squared was used as a measure of predictive power. The first multivariable logistic regression had primary vs secondary articles as the dependent variable and included the country of publication (limited to the 11 countries with more publications), the language of full-text (English/Other languages), the number of authors, and the number of days from the start of the pandemic as predictor variables. The initial model had a significant Hosmer–Lemeshow test ( P  = 0.004) and a low Nagelkerke R squared (0.27), due to non-linearities in the number of authors variable. The model was rebuilt after binning the variable (0 authors, 1–2 authors, 3–5 authors, 6–10 authors, > 11 authors). The new model had a non-significant Hosmer–Lemeshow test ( P  = 0.14) and higher Nagelkerke R squared (0.33) and was retained. A univariable logistic regression model was built including COVID-19 articles vs H1N1 articles as the dependent variable and including article type as predictor variable. A multiple linear regression model was built including number of articles per country as the dependent variable and country population, country GDP and country declared COVID-19 cases as predictor variables.

Data analyses and figures were performed using SPSS (version 24, IBM) and R 3.6.3 (R Core Team, 2020, www.R-project.org/ ). All P values were two tailed with nominal statistical significance claimed for P  < 0.05.

Results of the search

The results of our search are presented in a flow diagram (Fig.  1 ). Our search yielded 2482 articles. After exclusions of articles without an abstract, we retrieved 1215 articles. We excluded 50 articles based on the following rationale: duplicate articles ( n  = 13), articles that were not on COVID-19 (13), articles without an English abstract (6), letter to previous papers (8), erratum (2), local morbidity reports (7), and statement of the WHO (1). We included a total of 1165 articles on COVID-19 in the study.

Modified PRISMA flow diagram showing the article inclusion process

Characteristics of published articles

Four countries contributed to three quarters (871, 74.8%) of the included articles, i.e., approximately half of these articles, (588, 50.5%) came from China, 168 articles (14.4%) from the United States, 77 articles (6.6%) from Italy, 38 articles (3.3%) from the United Kingdom. The remaining 294 articles (25.2%) originated in decreasing numbers in Japan, Singapore, Korea, India, France, Germany, Taiwan, and other countries (Table  2 ). When considering publications per million population, Singapore (4.43), Italy (1.27) and Taiwan (0.68) were the most prolific countries. When considering publications per GDP point, Italy (256.7), China (96.4) and United States (73.0) were the most prolific countries. When considering publication per 100 declared COVID-19 cases, India (700.0), United States (188.8), and United Kingdom (105.5) were the most prolific countries (Table  2 ).

Table 2

Number of published articles per country, per country population, per country GDP and per country cases

Country population for the year 2018 was extracted from The World Bank website. Country GDP for the year 2019 was extracted from the dataset World Economic Outlook. Number of declared cases on March 2nd 2020 (1 month prior our data extraction) was extracted from the data published by the European Centre for Disease Prevention and Control (ECDC)

a Unavailable from The World Bank Data. Retrieved from Taiwan Government statistics website

b Unavailable from the European Centre for Disease Prevention and Control. Retrieved from Worldometer

Half of the included articles, (578, 49.6%) came from 49 individual journals (range of published articles per journal, 5–70). The full-text of 1000 of the 1165 articles (85.8%) was in English. Of the remaining articles, 152 full-texts (13.0%) were in Chinese, 6 (0.5%) in Spanish, 5 (0.4%) in German, and 2 (0.2%) in French. Articles included an average of 7.4 authors (SD: 6.98), ranging from 0 to 65 authors.

We identified 575 (49.4%) primary and 590 (50.6%) secondary articles. Of the primary articles, 340 were human medical research (59.1%), 182 were in silico studies (31.7%), 26 were in vitro studies (4.5%), 20 were human non-medical research (3.5%), and 7 were animal research (1.2%). Of the secondary articles, the majority were reviews, viewpoints and editorials (373, 63.2%). The second largest category was guidelines or guidance articles, including 193 articles (32.7%), of which 169 were indications for specific departments, patients or procedures. We included 23 systematic reviews (3.9%) and 1 protocol (0.2%).

Based on the multivariable logistic regression model, secondary articles were more likely to be published in a language different than English (aOR 3.02, 95% CI 1.99 to 4.58), to be published at a later stage of the pandemic (aOR 1.01, 95% CI 1.00 to 1.02), to include a lower number of authors (multiple aORs, Table  3 ), and to be published by authors from India, Italy, Singapore, Germany, and Taiwan (multiple aORs, reference: China; Table  3 ). Of the 20 journals that published more articles on COVID-19, there was a wide variation in the frequency of primary vs secondary articles (Fig.  2 ). Based on the multiple regression model [F(3,7) = 29.4, P  < 0.001, R 2  = 0.93], when adjusting for country population and GDP, the number of cases declared at the start of March significantly predicted the total number of articles published up to early April, with an increase of 6.7 articles (95% CI 4.1 to 9.4; P  < 0.001) for each 1000 case increase.

Table 3

Results of multivariable logistic regression analysis to determine factors associated with primary article publication in 1165 articles published in the early stages of COVID-19 pandemic

Continuous data are reported as Median ± IQR. Binary data are reported as number of observed events (percentage over the total). Hosmer–Lemeshow test: Chi square = 12.2; P  = 0.14. Nagelkerke R squared: 0.33. aOR adjusted odds ratios, CI Confidence intervals

Bar plot showing the percentage of primary articles (white boxes) and secondary articles (grey boxes) from the 20 journals that published more articles on COVID-19 in the first 3 months of the pandemic. Each bar represents all the articles published by each journal, with the number of articles showed in each box. The bar labelled as “Others” includes all remaining journals that had less than 10 publications each

Classification of primary articles

Human medical research consisted of 281 observational studies or case series (82.6%), 58 single case reports (17.1%), and 1 randomized controlled trial (0.3%). Human medical research included a median of 23 patients (IQR: 85), ranging from 1 to 72,314 patients. When only observational studies and case series were considered, the median number of patients included was 38 (IQR: 106). The only RCT included in the study enrolled 199 patients.

In silico research consisted of 109 studies on epidemiological modelling (59.9%), 64 studies on biochemistry, biology, bioinformatics or molecular modelling (35.2%), 5 studies evaluating or exploiting social media (2.7%), 3 studies on economical modelling (1.6%) and 1 description of an open database for viral trends (0.5%).

In vitro research consisted of 7 studies on the development or performance of diagnostic technology (26.9%), 7 studies on virus-host interactions (26.9%), 6 studies on gene expression or genomics (23.1%), 3 studies on pharmacological activity of compounds (11.5%), and 3 studies on viral isolation, transport or elimination (11.5%).

Animal research consisted of 4 studies that included mice (1 immunization with SARS-CoV S, 1 pharmacokinetic of a α-ketoamide inhibitor, 1 viral challenge with HCoV-OC43 and treatment with EK1C4, 1 hepatectomy and consequent gene expression)(57.1%), 1 study on hamsters challenged with SARS-CoV 2 (14.3%), 1 study on macaques challenged with MERS-CoV and treated with GS-5734 (14.3%), and 1 study on presence of SARS-CoV-2 related coronaviruses in Malayan pangolins (14.3%).

Human non-medical research consisted of 15 surveys, 8 on health professionals (40%), 7 on lay public (35.0%), 2 surveys of healthcare facilities (10.0%), 1 development of a psychological scale (5.0%), 1 RCT on medical professionals (5.0%), 1 simulation of an outbreak in a hospital (5.0%).

Reporting of limitations in the abstract

Limitations were reported in 42 out of 1165 abstracts (3.6%). Ten abstracts reported methodological limitations, i.e., limitations related to the study design and the remaining 32 abstracts reported general limitations, such as the current lack of evidence on COVID-19, or the need for further studies on COVID-19. Limitations were reported in 5 out of 23 systematic reviews (21.7%) and 2 out of 20 human non-medical researches (10.0%). All other manuscript types had a frequency of reporting limitations between 0% and 3.8%.

Comparison with early publications during the 2009 H1N1 swine influenza pandemic

Our search for articles on the 2009 H1N1 swine influenza during the first 3 months of that pandemic yielded 434 articles. After exclusions of articles without an abstract, we retrieved 239 articles. We excluded 16 articles that did not mention H1N1 or swine influenza in the full text. We included a total of 223 articles published at early stage of the 2009 H1N1 pandemic in the study. Eight countries contributed to three quarters (166, 74.4%) of the included articles, with approximately one-third of the articles coming from the United states (75, 33.6%) and one tenth of them coming from China (24, 10.8%). Almost all the articles (215, 96.4%) had an English full text. Based on our previous classification, there were 179 primary articles (80.3%) and 44 secondary articles (19.7%). The primary articles included 71 human medical researches (39.7%), 36 animal researches (20.1%), 33 in vitro studies (18.4%), 30 in silico studies (16.7%), and 9 human non-medical researches (5.0%). Of the human medical research, 66 were observational studies and case series (92.9%), 3 were RCTs (4.2%), and 2 were single case reports (2.8%). The secondary articles were mainly reviews, viewpoints and editorials (38, 86.4%), with a few guidelines or guidance articles (5, 11.4%) and 1 systematic review (2.3%) (Table  4 ).

Table 4

Type of studies published in the early stages of COVID-19 pandemic and of 2009 H1N1 pandemic

Results of univariable logistic regression analysis are reported. Binary data are reported as number of observed events (percentage over the total articles for each pandemic event; for human medical research subcategories, the percentage is calculated over the number of human medical researches). OR odds ratios, CI Confidence intervals

In the univariable logistic regression model, the odds of being published during COVID-19 were 8 times higher for guideline articles (OR 8.1, 95% CI 3.2 to 20.3), and 2 times higher for reviews (OR 2.0, 95% CI 1.3 to 3.1), while the odds of being published during H1N1 were 24 times higher for animal researches (OR 24.6, 10.5 to 57.6), and 6 times higher for in vitro research (OR 6.1, 3.4 to 10.8) (Table  4 ; Fig.  3 ).

Relationship between days from the start of the pandemics, number of articles published and type of articles during COVID-19 pandemic (red circles) and 2009 H1N1 swine flu pandemic (green circles). On the x axis the days from the start of COVID-19 pandemic (top axis) and the days from the first publication for each pandemic (bottom axis) are reported. On the y axis the total number of articles published for each pandemic is reported. Circle size has been arbitrarily classified in order to show different levels of clinical evidence: at increasing circle size, increase the value of the article (circle size: 1: secondary articles and human non-medical research; 7: in silico research; 8: in vitro research; 9: animal research; 11: case reports; 14: observational studies and case series; 17: randomized controlled trials; 20: systematic reviews). The values were jittered over the y axis to reduce superimposition of data

Principal findings of the study related to the COVID-19 pandemic

This meta-epidemiological study is novel in having assessed the characteristics of scientific articles published during the initial 3 months of the COVID-19 pandemic. Our study has five key findings. First, over half (50.6%) of all 1165 included articles were secondary articles. Perspectives and syntheses have an important role in scientific research, but one secondary article for each primary article could be redundant. Second, human medical research consisted of 29.2% (340/1165) of the included articles. This implies that a large body of articles are not relevant for health care policy makers. Identifying human medical research studies slows down the evidence-based decision making process, because a large bulk of literature has to be filtered out first. This selection process is particularly time consuming, because it can often not be done by reading titles and abstracts alone. Third, all except one (339/340) of the human medical research studies were observational studies or case reports. This implies that policy makers have to rely predominantly on studies that get a low-certainty (or quality) rating according to the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach (Schünemann et al. 2019 ). Fourth, only 3.6% (42/1165) of all included articles reported limitations in their abstracts. Reporting limitations is an important warning sign for end-users of research articles and is an obligatory item in the reporting of abstracts of systematic reviews (Beller et al. 2013 ). Fifth, about half of all included articles originated in China, i.e., 50.5% (588/1165). A high prevalence of articles from China was expected because the COVID-19 outbreak started in that country, but this statistic is disproportionate with the much higher COVID-19 infection and death rates in other countries. When evaluating the number of publications per inhabitant, per GDP and per COVID-19 cases, different countries were respectively more prolific. China was the second country with the highest number of articles per GDP unit, but was one of the countries with the lowest number of published articles per COVID-19 cases, with 0.73 articles published per 100 confirmed cases, and also its production was average when considering its number of inhabitants. Italy was the third overall most prolific country, the second in terms of articles per inhabitant, the first in terms of articles per GDP unit and the second to last in terms of articles per confirmed cases. The country that had most publications per number of cases, India, was also the country with the highest prevalence of secondary articles. Overall, we found an association between the number of cases declared in early March by a country and the number of articles published 1 month after, when adjusting for GDP and population. This association should be regarded carefully, considering that the analysis was performed as a deviation from the original protocol in light of a suggestion from a reviewer.

Comparison with the H1N1 pandemic

We observed several differences in the type of articles published during H1N1 and COVID-19 pandemics. The most obvious, is a striking difference in the proportion of secondary articles published during the two pandemics. Less than 20% of the articles were secondary in the early H1N1 pandemic, while during the COVID-19 pandemic over 50% of the articles were secondary. This difference was mostly related to the higher percentage of narrative reviews, editorials and guidelines. The amount of clinical reports was overall similar in the two pandemics, with a higher proportion of case reports during COVID-19 and a lower proportion of observational studies and randomized trials. Both in vitro and animal research were more prominent during the H1N1 pandemic. The larger proportion of animal research published during H1N1 could be related to the tight connection of the pandemic with farm animals, or with the increasing amount of regulations on laboratory animal research, such as the directive 2010/63/EU (2010).

Comparison with other studies

The exponential growth of publications identified in this paper during the first 3 months of the COVID-19 pandemic was also found in past viral outbreaks such as SARS, MERS, Ebola, and Swine Flu (Kagan et al. 2020 ). This high publication rate dropped dramatically upon containment of these diseases. Gori et al. ( 2020 ) identified a high proportion of secondary literature in the first 30 days of the COVID-19 pandemic. However, their findings cannot be directly compared with ours because they used different methods, had a much smaller sample size (234 papers versus 1165 in our sample), measured mostly different outcomes and at different time points (1 month versus 3 months in our sample).

Strengths and weaknesses

The strengths of this meta-epidemiological study are: (1) this is the first research study that assessed the characteristics of articles on COVID-19 listed in PubMed in the first 3 months since the outbreak of the coronavirus pandemic; (2) all study selection and data extraction procedures were conducted by two methodologists independently and all raw data were reported in additional files; (3) the manuscript was reported according to the STROBE checklist. The limitations of this study are: (1) having searched eligible articles exclusively in PubMed is a limitation, because this could have biased our outcomes (Lefebvre et al. 2008 ; Halladay et al. 2015 ). The total body of literature on COVID is expected to be larger; (2) lack of inclusion of articles without an abstract in English is another potential limitation, since it is possible that the proportion of primary/secondary articles was different when considering articles published in different languages without an English abstract; (3) lack of assessment of the discussion section for each article in order to retrieve limitations that were not reported in the abstracts.

Implications and future research

The exponential surge in scientific publishing was expected with the outbreak of a pandemic of an unknown virus. Finding mostly observational studies among the human medical research studies in this body of articles was also not surprising. However, having to filter out half of the literature, because it is not producing new research data is problematic, especially when almost 2500 new articles on COVID-19 were indexed in PubMed in the first 3 months of this pandemic. Researchers, peer reviewers, editors, and publishing companies are responsible for this large body of literature. They should aim at flattening the publication curve for example by tightening their acceptance criteria. This strategy could also help to improve the overall research quality (Sarewitz 2016 ). Labeling publications as ‘secondary article’ in the abstract could become an initial obligatory item for all publications that do not produce original research.

Undertaking future research studies on outbreaks of diseases should start with the consultation of a wide body of stakeholders to develop and prioritize research questions. Such research could explore (1) our statistics at later time points (2) quality assessments of the conduct and reporting of research studies on COVID-19 (3) factors that could be implemented to control the quantity and quality of publications (4) the impact of the development of a vaccine for COVID-19 on the publication curve and (5) how to rapidly synthesize literature in times of a pandemic. Further, high quality systematic reviews and guidelines for the prevention and management are necessary when COVID-19 is contained. This will be key to control new outbreaks of COVID-19 and other diseases.

Conclusions

We showed that as compared to the most recent pandemic (2009 H1N1), there is an overwhelming amount of information published on COVID-19. Due to the large body of non-original articles (about half) published in the early phases of the pandemic, the original information published has been diluted. This can slow down the development of a valid knowledge base on COVID-19 and the pertinent strategies to deal with this disease. Also, a negligible number of published articles reported limitations in the abstracts, potentially facilitating overemphasis of the article findings or recommendations. Researchers, peer reviewers, and editors should take action to flatten the publication curve and start labeling non-original research articles as secondary articles.

Below is the link to the electronic supplementary material.

Authors contribution

NDG: Conceptualization, Methodology, Investigation, Formal analysis, Writing-Original Draft, Writing-Review & Editing. RMR: Conceptualization, Methodology, Investigation, Writing-Original Draft, Writing-Review & Editing.

No funding received.

Availability of data and material (data transparency)

Compliance with ethical standards.

The authors declare no competing interests nor conflict of interests.

No codes have been generated during this research.

Our protocol was registered in Open Science Framework: https://osf.io/eanzr .

The protocol was registered in Open Science Framework as: ‘Characteristics of scientific articles on COVID-19 published during the initial 3 months of the pandemic: protocol for a meta-epidemiological study’.

Contributor Information

Nicola Di Girolamo, Email: moc.liamg@iggidalocin .

Reint Meursinge Reynders, Email: moc.liamg@liamsrednyer .

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The most anti-science belief you can hold is that science is a religion

A new theory wonders if all matter could be "conscious" — before scientists hand-wave it, they should hear it out, by rae hodge.

“Fringe”, “weird” and “unthinkable” are perfectly acceptable descriptors any science writer might use when rightfully denouncing some hare-brained professor’s paper that suggests, for instance, the North American sasquatch is the leading driver of climate change, or that Elvis Presley and Tupac Shakur are responsible for kidnapping Shelly Miscavige. Science journalism has a job to do — and that includes verbally smacking the pseudoscience out of academic hustlers to defend the dignity of both the reader and the science. We stan a scientific diss track in this shop, and I’d gladly lend my pen to such a cause.

But when science writers dismiss robustly-debated philosophical theories this way — like panpsychism , one well-known theory about the possible nature of subjective consciousness even in inanimate objects — they look less like erudite champions of empirical truth, and more like a Victorian drawing room full of phrenologists scoffing at William James’ notions of psychology while proclaiming that “there isn't a single head-bump of evidence to support this theory.”

At least, that’s what they looked like this past week when Popular Mechanics science writer Stav Dimitropoulos offered a fresh bit of nuanced reporting on the renewed popular interest around the philosophical theory of panpsychism. To grossly oversimplify, the theory posits that consciousness isn’t just the currently scientifically-inexplicable emergent property of a human brain as many consider it now, but a property of pretty much any self-organizing system of material things. Panpsychism’s principles stretch back to human’s earliest notions of classical philosophy but have also evolved right alongside the sciences (like, you know, theories within humanities disciplines do).

Panpsychism winks at us from our species’ inquisitive past and seems to ask, “Aren’t you the same hairless apes that once laughed at a guy for suggesting all matter was ultimately made of vibration?

Its core concepts have been advocated for by the likes of Nobel Prize winner Roger Penrose , as well as physicists like Author Eddington and David Bohm, and even William James himself. As a theory, panpsychism challenges us to consider whether we featherless bipeds might be thinking a bit too primitively when we assume objectively extant concepts we have no real way to quantify — like consciousness — can only be produced by neuronal sparks of the electrified hamburger meat between our ears.

Panpsychism winks at us from our species’ inquisitive past and seems to ask, “Aren’t you the same hairless apes that once laughed at a guy for suggesting all matter was ultimately made of vibration? Do you think your primitive little frontal cortex is equivalent to the skull of Zeus, and that the totality of all possible wisdom springs from it fully armored as Athena?” 

Want more health and science stories in your inbox? Subscribe to Salon's weekly newsletter Lab Notes .

When fuzzy terms like “ artificial superintelligence ” are getting tossed around to describe black-box processes of a computer network you can pay to be your girlfriend, I’d say panpsychism’s questions are worth more than an embarrassingly tone-deaf snicker from science writers. Similarly well-timed amid all the recent heady research into quantum mechanics, Dimitropoulos’ rather eloquent piece invites readers to examine the current limits of material physics’ theories and see what the brainiacs in humanities departments have to say about self-awareness and the mind’s role in the wider universe.

But judging by the frantic oinking of science writers who quickly piggybacked off her click-traffic, you’d have thought the article was a crayon-scrawled defense of flat-earthers. Seemingly affronted by the possibility that a philosophical theory might offer a uniquely interdisciplinary approach to a problem that physics was never asked nor meant to solve alone, a gaggle of presumably muttonchopped science writers eagerly charged into the latest skirmish of a decades-old fray between philosophers and physicists.

In overindulgent headlines and ill-advised body-copy, would be defenders of the faith of Scientism gleefully celebrated missing the entire point of panpsychism across some widely circulating and uninformed articles that I’d rather not further promote.

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It’s disappointing to see but not a surprise. A lack of curiosity about the possibilities of consciousness is the hallmark of anti-science attitudes, even among those appointed to herald the sober inquiry of an awe-striking world of which the human race is but one fleeting member. And to do this job right — hell, to even get beyond our own trembling ontological frailty long enough to learn something about this existence — we have to fight anti-science attitudes in every quarter, even our own. 

We should start with our own beliefs. To that end, the most anti-science belief you can hold isn’t that the earth is flat, that consciousness may be more than human thought, or that existence may be more than we can quantify at the moment — it’s that science is a religion. And when you treat science like a religion, like a framework for limiting the interpretation of the world’s possibilities, rather than like a framework for discovering those possibilities — you stop writing science journalism and you start writing Scientism apologia.

When your congregation zealously overestimates the epistemological functionality of empiricism in the work of logical positivism, you trap the conversation of science and consciousness in your lethally boring Vienna wagon-Circling. And in this way, yes, you insult the dignity of both the reader and the science. 

An earlier version of this article originally appeared in Salon's Lab Notes , a weekly newsletter from our Science & Health team.

about consciousness

• I code the body electric: We're putting AI brains in robot bodies now. What could go wrong?
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Rae Hodge is a science reporter for Salon. Her data-driven, investigative coverage spans more than a decade, including prior roles with CNET, the AP, NPR, the BBC and others. She can be found on Mastodon at @[email protected]. 

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• 27 March 2024
• Correction 27 March 2024

Memories are made by breaking DNA — and fixing it

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Neurons (shown here in a coloured scanning electron micrograph) mend broken DNA during memory formation. Credit: Ted Kinsman/Science Photo Library

When a long-term memory forms, some brain cells experience a rush of electrical activity so strong that it snaps their DNA. Then, an inflammatory response kicks in, repairing this damage and helping to cement the memory, a study in mice shows.

The findings, published on 27 March in Nature 1 , are “extremely exciting”, says Li-Huei Tsai, a neurobiologist at the Massachusetts Institute of Technology in Cambridge who was not involved in the work. They contribute to the picture that forming memories is a “risky business”, she says. Normally, breaks in both strands of the double helix DNA molecule are associated with diseases including cancer. But in this case, the DNA damage-and-repair cycle offers one explanation for how memories might form and last.

It also suggests a tantalizing possibility: this cycle might be faulty in people with neurodegenerative diseases such as Alzheimer’s, causing a build-up of errors in a neuron’s DNA, says study co-author Jelena Radulovic, a neuroscientist at the Albert Einstein College of Medicine in New York City.

Inflammatory response

This isn’t the first time that DNA damage has been associated with memory. In 2021, Tsai and her colleagues showed that double-stranded DNA breaks are widespread in the brain, and linked them with learning 2 .

To better understand the part these DNA breaks play in memory formation, Radulovic and her colleagues trained mice to associate a small electrical shock with a new environment, so that when the animals were once again put into that environment, they would ‘remember’ the experience and show signs of fear, such as freezing in place. Then the researchers examined gene activity in neurons in a brain area key to memory — the hippocampus. They found that some genes responsible for inflammation were active in a set of neurons four days after training. Three weeks after training, the same genes were much less active.

How to see a memory

The team pinpointed the cause of the inflammation: a protein called TLR9, which triggers an immune response to DNA fragments floating around the insides of cells. This inflammatory response is similar to one that immune cells use when they defend against genetic material from invading pathogens, Radulovic says. However, in this case, the nerve cells were responding not to invaders, but to their own DNA, the researchers found.

TLR9 was most active in a subset of hippocampal neurons in which DNA breaks resisted repair. In these cells, DNA repair machinery accumulated in an organelle called the centrosome, which is often associated with cell division and differentiation. However, mature neurons don’t divide, Radulovic says, so it is surprising to see centrosomes participating in DNA repair. She wonders whether memories form through a mechanism that is similar to how immune cells become attuned to foreign substances that they encounter. In other words, during damage-and-repair cycles, neurons might encode information about the memory-formation event that triggered the DNA breaks, she says.

When the researchers deleted the gene encoding the TLR9 protein from mice, the animals had trouble recalling long-term memories about their training: they froze much less often when placed into the environment where they had previously been shocked than did mice that had the gene intact. These findings suggest that “we are using our own DNA as a signalling system” to “retain information over a long time”, Radulovic says.

How the team’s findings fit with other discoveries about memory formation is still unclear. For instance, researchers have shown that a subset of hippocampal neurons known as an engram are key to memory formation 3 . These cells can be thought of as a physical trace of a single memory, and they express certain genes after a learning event. But the group of neurons in which Radulovic and her colleagues observed the memory-related inflammation are mostly different from the engram neurons, the authors say.

Flashes of light show how memories are made

Tomás Ryan, an engram neuroscientist at Trinity College Dublin, says the study provides “the best evidence so far that DNA repair is important for memory”. But he questions whether the neurons encode something distinct from the engram — instead, he says, the DNA damage and repair could be a consequence of engram creation. “Forming an engram is a high-impact event; you have to do a lot of housekeeping after,” he says.

Tsai hopes that future research will address how the double-stranded DNA breaks happen and whether they occur in other brain regions, too.

Clara Ortega de San Luis, a neuroscientist who works with Ryan at Trinity College Dublin, says that these results bring much-needed attention to mechanisms of memory formation and persistence inside cells. “We know a lot about connectivity” between neurons “and neural plasticity, but not nearly as much about what happens inside neurons”, she says.

doi: https://doi.org/10.1038/d41586-024-00930-y

Read the related News & Views: ‘ Innate immunity in neurons makes memories persist ’.

Updates & Corrections

Correction 27 March 2024 : An earlier version of this story indicated that broken DNA accumulated in the centrosome. It is DNA repair machinery that accumulates in that organelle.

Jovasevic, V. et al. Nature 628 , 145–153 (2024).

Article   Google Scholar  

Stott, R. T., Kritsky, O. & Tsai, L.-H. PLoS ONE 16 , e0249691 (2021).

Article   PubMed   Google Scholar  

Josselyn, S. A. & Tonegawa, S. Science 367 , eaaw4325 (2020).

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The Far Right Is Crawling With Eclipse Conspiracy Theories

By Ej Dickson

Solar eclipses, like the upcoming one on April 8, are a well-documented scientific phenomenon. As early as 763 BCE, ancient Assyrians were charting the process by which the path of the moon temporarily obstructs the sun, and astronomers have continued to do so for thousands of years since. Our knowledge of eclipses predates our knowledge of gravity, algebra, and toilet paper. We are well aware of their existence, and we are well aware of what causes them. (I mean, I personally am not, but other people ostensibly are.)

This latest onslaught of misinformation began, as it often does, with InfoWars host Alex Jones , who has spent the past few weeks ranting on X about the upcoming eclipse. Last week, he posted a clip with the caption: “Major Events Surrounding The April 8th Solar Eclipse [.] Masonic rituals planned worldwide to usher in New World Order.”

The post, which has more than 3,000 retweets and 1 million views, illustrates how the trajectory of the most recent solar eclipse viewable in the United States, as well as the trajectory of the upcoming eclipse, form an “Aleph” and “Tav,” which (as anyone who was forced to go to Hebrew school instead of staying home and using cheat codes to make your Sims woo-hoo naked knows) are the first and last letters in the Hebrew language, signaling the beginning and end times.

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It is, indeed, true that a number of counties, including Texas’ Travis County and Oklahoma’s McCurtain County , have issued disaster warnings prior to the eclipse. But it does not appear to be for the nefarious reasons suggested by Jones. Rather, it appears to be for the intended purpose of the government: to adopt public safety initiatives that protect people from harm. For instance, according to the Travis County declaration of local disaster, the county anticipates an influx of thousands of tourists to watch the eclipse, thereby leading to “extreme traffic congestion” and potential “telephone service disruptions.” Such conditions necessitate a declaration of a state of emergency to ensure “critical infrastructure protection” and “facilitating access to food, water, and shelter” in case the roads become overwhelmed, per the language of the declaration.

While this explanation sounds both highly rational and surprisingly efficient by county bureaucracy standards, it is not one that has been accepted by the slew of mini-Alex Joneses on TikTok, Instagram, and X.

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It’s not all that surprising or unusual that brainrotted, terminally online people are obsessed with the eclipse. “It all feels very similar to the same run of conspiracy theories we had with stuff like the four blood moons and the Sept. 23rd apocalypse and other pseudoscience nonsense,” says Mike Rothschild, author of The Storm Is Upon Us: How QAnon Became a Movement, Cult, and Conspiracy Theory of Everything , referencing a series of doomsday prophecies peddled by evangelicals in 2014 and 2017, respectively. “Basically, a common event is imbued with cosmic significance, then totally forgotten when nothing happens.”

It’s far from unusual, Rothschild says, for there to be “weird theories and superstitions around cosmic events like eclipses, because people don’t understand science and how anything works.”

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April 3, 2024

New Law Allowing Religion into Science Classrooms Is Dangerous for Everyone

It is imperative that we protect science education from “intelligent design” and other alternative “theories”

By Amanda L. Townley

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I grew up a creationist in the rural southeastern U.S. I am now a scientist, educator, wife, mother and person of faith. Regardless of whether you practice religion, you should fight to prohibit the teaching of nonscientific alternative ideas in science classrooms and use your vote and your voice to prevent the inclusion of religious beliefs in public education. A recently signed law in West Virginia illustrates why.

I often hear lamentations about the removal of God from public schools. These sentiments are based on a misinterpretation of the principle of the separation of church and state. In the U.S., religious beliefs and practices are protected and situated in their rightful place within people’s homes and communities so that individuals can choose what to teach their children regarding religion. Kids can still pray whenever they wish, gather with their peers, create faith-based groups or even nondisruptively practice their faith in school. Separating state and church means young people cannot be compelled to engage in religious actions by someone in a position of power, such as a teacher, administrator or lawmaker. Separation of church and state is as critical to people of faith as it is to those who do not practice faith traditions. The protection of personal religious freedoms was a vital component of the foundation of our nation.

On March 22 West Virginia governor Jim Justice signed a bill that purports to protect the ability of the state’s public school educators to teach scientific theories. There is no actual problem that the new law would solve, however; none of its supporters produced a teacher who plausibly claimed to have been oppressed. But the legislative history of the bill, known as Senate Bill 280, makes it clear that its real aim is to encourage educators to teach religiously motivated “alternatives” to evolution. As introduced, SB 280 would have expressly allowed the teaching of “ intelligent design ” in West Virginia’s public schools.

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The National Center for Science Education (NCSE), of which I am the executive director, monitors attempts to undermine the accurate and robust teaching of science education in K–12 public school classrooms. Most often, these attempts die in committee or fail to pass in state legislatures to become a law. This particular West Virginia bill appeared in a prior session and passed the state’s Senate in February 2023 before dying in the House Education Committee. This session, the Senate Education Committee adjusted the wording to remove the term “intelligent design” in favor of “scientific theories,” conspicuously failing to explain what that term does and does not include. During the floor discussion of Senate Bill 280, however, its sponsor, Amy Grady (Republican, District 4), declared that even as amended, the bill would protect the teaching of “intelligent design” in West Virginia’s public schools.

It’s been 19 years since a federal court in neighboring Pennsylvania took up the issue of whether “intelligent design,” like its predecessor “creation science,” can be constitutionally taught in public schools. Presiding over the case Kitzmiller v. Dover , Judge John E. Jones III, appointed to the bench by President George W. Bush, found that it cannot be. There was no appeal of his meticulous decision, and no court has ruled otherwise.

The policy makers in West Virginia would have done well to consult the decision in Kitzmiller . They would have learned about the legal perils awaiting any teacher or district unwise enough to invoke the protection of the newly enacted law in defense of teaching “intelligent design”; in Pennsylvania, the Dover Area School Board ended up paying more than $1 million of the plaintiffs’ legal fees. They might also have realized that their motivations rested on some common misconceptions.

The first misconception is that learning about evolution threatens students’ faith. Evolutionary biology, like any area of modern science, is simply a body of knowledge about the natural world and a set of methods and procedures for attaining, refining and testing that knowledge. Nothing in evolutionary biology denies the existence of God or places constraints on divine activity. Evolutionary biologists include people of many faiths and of none, and evolutionary biology is routinely taught in institutions of higher education, whether public or private, secular or sectarian, as the well-established area of modern science that it is.

A second misconception is that exposing students to “intelligent design” promotes religious freedom. (The proponents of “intelligent design” often claim their views have no religious motivation, but frame it otherwise when it suits their purposes.) On the contrary, because “intelligent design” reflects a narrow sectarian rejection of evolution, teaching it in school actually harms religious freedom.

The division of church and state is crucial for the religious freedom of everyone in the U.S. Yet some people hope for the undoing of this separation of religion and political power, mainly because they expect that those in power will share their particular religious beliefs. They should stop and think very carefully about the possible consequences of temporarily having their way.

In particular, with Senate Bill 280 now on the books, West Virginia educators are free to teach whatever “scientific theories” they please. With no definition of "scientific theories" in the law, a few misguided educators may present creationism—either old-fashioned “creation science” or newfangled and equally unscientific “intelligent design”—as a result. But the sky’s the limit. Why not geocentrism or flat-Earthery? Why not crystal healing? Why not racist views claiming that white people and Black people have separate ancestry? All of these notions, which stem from religious beliefs, not science, have been held up by their proponents as scientific theories, and West Virginia’s legislature and governor just opened the public classroom door to them.

West Virginia is only one state, but others—Mississippi, Louisiana and Tennessee—have similar laws on the books. As the nation continues to polarize along religious and political lines, more states may follow, compromising both science education and religious freedom.

For these reasons, people of all faiths and none should unite in fighting for religious freedom, including by ensuring that religiously motivated but unscientific “alternatives” to science are not allowed in public school classrooms. Failure to maintain the separation of church and state, and to instead favor a particular sectarian view, opens a door that, one day, people will wish could be closed.

This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American.