Research Challenges Research Articles

PLLA Porous Scaffold as a 3D Breast Cancer Model to Investigate Drug Resistance.

Multidrug resistance remains one of the major challenges in breast cancer research, often leading to treatment failure. To better understand this mechanism, sophisticated three-dimensional (3D) tumor models are necessary, as they offer several advantages over traditional bidimensional (2D) cultures. In this study, poly-l-lactic-acid porous scaffolds were produced using a thermally induced phase separation technique and employed as 3D models for breast cancer cell lines: MDA-MB-231, MCF-7, and its multidrug-resistant variant, MCF-7R. The MTS assay was used to compare growth inhibition following doxorubicin treatment in 2D and 3D. Remarkably, the IC50 values increased in 3D cultures compared to 2D: MDA-MB-231 (445 vs. 54.5 ng/mL), MCF-7 (7.45 vs. 0.75 μg/mL), and MCF-7R (165 vs. 39 μg/mL). MCF-7R, which usually shows greater resistance in 2D, demonstrated even higher resistance in 3D. In fact, IC50 was not reached within 3 days as with the other models, but only after 6 days. Cellular morphology also played a crucial role. When treated with concentrations higher than the IC50, MDA-MB-231 cells lost their physiological 3D clustered structure, while MCF-7 and its resistant variant exhibited disrupted layers. All cell lines in 3D showed higher chemoresistance, suggesting a more biomimetic spatial architecture. Our work bridges the gap between monolayer and animal models, highlighting the potential of polymeric 3D scaffolds in breast cancer research.

Research ethics and state power: access vs integrity in the study of armed actors

ABSTRACT In the study of armed actors, the state poses significant – yet often unrecognised – ethical challenges for researchers. This article provides a framework for researchers to pro-actively manage their relationships with the state. First, we show how the state affects the ethics of research on armed actors. Then we illustrate the danger of ignoring, discounting, or misreading the state, both at home and abroad. We question common assumptions about state unity, competence, and legitimacy, and we identify a persistent ethical dilemma between access, which is often mediated or controlled by the state, and integrity. We probe this dilemma via three dimensions of state power: the power of funding and partnership; the power of policy laundering; and the power of gatekeeping, surveillance, and coercion. Ultimately, we argue that ethically sound research on armed actors requires researchers to carefully and continuously re-examine their beliefs about the state. Along the way, we offer practical guidance on interacting with state-backed funders; mitigating risk and tempering the influence of the state during fieldwork; and thinking through the state’s complex and contradictory roles. We also raise uncomfortable questions about the red lines separating researchers’ interests from states’ interests.

Willingness to pay of Portuguese sparkling wine consumers: Econometric and machine learning approaches

Abstract Understanding consumer choices and their drivers of willingness to pay (WTP) for a bottle of wine has been a research challenge in wine economics, particularly in niche markets such as sparkling wine. This study investigates the determinants of WTP for sparkling wine based on data from Portuguese consumers. The results provided by two alternative methodologies are compared: a traditional econometric model, based on the estimation of an ordered probit model; and a modelling approach based on data-driven and using machine learning algorithms. Both approaches present similar results, highlighting the relevance of some determinants including income, Champagne brand, not being a protected designation of origin and being a red wine consumer as main predictors of WTP for sparkling wine in Portugal.

Multicolor Gold Clusterzyme-Enabled Construction of Ratiometric Fluorescent Sensor Array for Visual Biosensing.

The simultaneous detection of multiple bioanalytes with similar structures has been a long-standing challenge for biological research and disease diagnosis. Bioreceptor-inspired sensor arrays provide an attractive and competitive solution for addressing this challenge, but applying this technique to biosensing in practical application scenarios is complicated by many factors such as the lack of robust probes, interference from the biological matrix, and difficulty for on-site analysis. In this work, by taking advantage of the intrinsic fluorescence and enzyme-mimic properties of gold nanoclusters (AuNCs), we report the design of an innovative ratiometric sensor array toward enhanced fluorescent visual biosensing. With biologically important phosphates as an example, we show that the present fluorescent clusterzyme-based ratiometric sensor array could effectively discriminate and detect eight types of phosphates. In particular, AuNCs with three different emission colors (blue, green, and red) and good peroxidase-mimic properties were employed as the sensing units, and the presence of phosphates affected both the intrinsic fluorescence and the enzymatic activity of these AuNCs, yielding distinct optical responses in a ratiometric manner. Moreover, a portable sensor array was established by further integrating these fluorescent clusterzymes into a hydrogel matrix, which could visually identify different phosphates based on their distinct fluorescence color changes. Consequently, the point-of-care diagnosis of urinary tract infections at different levels was achieved by analyzing urinary microbial ATP via the present strategy. This study illustrates the great potential of clusterzyme-based fluorescent sensor arrays as a promising biosensing platform for disease diagnosis.

Blockchain-based energy consumption approaches in IoT.

Blockchain is a decentralized electronic account book that is secure, open, and impervious to manipulation, and records transactions across numerous computers. Internet of Things (IoT) deployed sensors are limited in bandwidth, processing power, and battery life. Energy efficiency is a vital factor and a decisive matter for energy-bound IoT-based networks. This paper provides an idea of the working principle of blockchain technology in IoT-enabled energy systems. It investigates the fundamentals of blockchain technology, clarifying its decentralized nature, cryptographic mechanisms, and consensus algorithms that ensure data immutability and transparency. It explores how blockchain technology can be integrated with various IoT fields for energy efficiency. The paper investigates the blockchain-based energy consumption approaches in IoT qualitatively, and quantitatively with future research directions and challenges. It is seen that the hybrid blockchain model is suitable for energy efficiency in IoT.

Detection of Fake Instagram Accounts via Machine Learning Techniques

This paper focuses on the detection of fake accounts on Instagram and proposes a novel solution that aims to address this problem. More specifically, a machine learning-based solution is introduced that can be employed by Instagram-based applications to combat this phenomenon. To accomplish this, publicly available data from Instagram users are collected and processed. After making the necessary feature additions to and removals from these data, they are fed into machine learning algorithms with the aim of detecting fake Instagram accounts. The interest of the results presented in this study lies in their comparability with the results of other similar studies, with the difference that fewer input features have been used. This addresses significant research challenges regarding the performance of and confidence in obtained results in this specific problem domain.

Exploring barriers and ethical challenges to medical data sharing: perspectives from Chinese researchers.

The impetus for policies promoting medical data sharing in China has gained significant traction. Nonetheless, the present legal and ethical framework governing the research use of medical data in China, is characterized by a more restrictive rather than permissive approach. The proportion of Chinese medical data being leveraged for scientific research still has room for improvement at present, indicating a significant untapped potential for advancing medical knowledge and improving healthcare outcomes. Building upon this research, we aim to delve deeper into the challenges researchers encounter in the sharing of medical data through focus group interviews. We conducted two focus group interviews study with researchers representing diverse disciplines to explore their perspectives on 21 June 2021 and 28 July 2021. A total of seventeen researchers willingly participated in this study, representing various professional backgrounds. Similar codes were merged. Research team discussions were also utilized to select interviewees' statements that were regarded as typical or representative. The respondents demonstrated a strong understanding that medical data should not be disseminated arbitrarily, recognizing the importance of sharing data in compliance with laws. Through the interview, we found that although respondents stressed the importance of careful consideration regarding if and when this information can be responsibly released, none of the respondents raised the issue of necessitating consent from data subjects for the research use of medical data. This observation sharply contrasts with the stringent separate consent provisions for secondary data use outlined in the PIPL. The findings from the focus group studies shed light on researchers' barriers and ethical challenges towards medical data sharing for scientific research, highlighting their deep concern for data security and cautious approach to sharing. The key objectives aimed at facilitating and enabling the reuse of medical data encompass enhancing interoperability, harmonizing data standards, improving data quality, safeguarding privacy, ensuring informed consent, incentivizing patients, and establishing explicit regulations pertaining to data access and utilization.

Smart Medicine: The Role of Artificial Intelligence and Machine Learning in Next-Generation Healthcare Innovation

The paper focuses on big data and healthcare pharmaceuticals, which pose both great promise and challenge as they can contribute to well-being and deliver innovation and knowledge to next-generation healthcare products. This paper provides an overview of the benefits that machine learning and big data technology contribute to healthcare systems. Realities such as patient and healthcare professional demands, the number of diseases, insufficient drugs, regulations, legal and ethical guideline restrictions, modus operandi, and technology development take place in the healthcare sector. In addition, big data opportunities and challenges for healthcare companies, research, and healthcare infrastructure are discussed. Finally, a vision of health innovation through personalized medicines as well as smart medicine is presented as a step closer to patients through regulatory, legal, and ethical guidelines, applications, and opportunities that need to be regulated and resolved.

An integrated approach for studying exposure, metabolism, and disposition of traditional Chinese medicine using PATBS and MDRB tools: a case study of semen Armeniacae Amarum.

Deciphering the in vivo processes of traditional Chinese medicine (TCM) is crucial for identifying new pharmacodynamic substances and new drugs. Due to the complexity and diversity of components, investigating the exposure, metabolism, and disposition remains a major challenge in TCM research. In recent years, a number of non-targeted smart mass-spectrometry (MS) techniques, such as precise-and-thorough background-subtraction (PATBS) and metabolomics, have realized the intelligent identification of in vivo components of TCM. However, the metabolites characterization still largely relies on manual identification in combination with online databases. We developed a scoring approach based on the structural similarity and minimal mass defect variations between metabolites and prototypes. The current method integrates three dimensions of mass spectral data including m/z, mass defect of MS1 and MS2, and the similarity of MS2 fragments, which was sequentially analyzed by a R-based mass dataset relevance bridging (MDRB) data post-processing technique. The MDRB technology constructed a component relationship network for TCM, significantly improving metabolite identification efficiency and facilitating the mapping of translational metabolic pathways. By combining MDRB with PATBS through this non-targeted identification technology, we developed a comprehensive strategy for identification, characterization and bridging analysis of TCM metabolite in vivo. As a proof of concept, we adopted the proposed strategy to investigate the process of exposure, metabolism, and disposition of Semen Armeniacae Amarum (CKXR) in mice. The currently proposed analytical approach is universally applicable and demonstrates its effectiveness in analyzing complex components of TCMs in vitro and in vivo. Furthermore, it enables the correlation of in vitro and in vivo data, providing insights into the metabolic transformationsamong components sharing the same parent nucleus structure. Finally, the developed MDRB platform is publicly available for ( https://github.com/933ZhangDD/MDRB ) for accelerating TCM research for the scientific community.

PH Dependence of Dihydroxyacetone Oxidation by Electrocatalytic Viologen Self-Assembled Monolayers on Gold Electrodes.

Carbohydrate fuel cells garner much research interest as the world's focus shifts from fossil fuels to renewable energy. Many catalyst options are available for carbohydrate fuel cell development, including enzymes and microbes, various metal-based catalysts, and natural or synthetic mediators. Research challenges include low power output, system fouling and poisoning, inefficient electron release, and complex mechanisms, with multiple pathways leading to low product selectivity. Here, we further investigate a novel approach to catalyze carbohydrate oxidation using Au electrodes with viologen self-assembled monolayers (SAMs). SAM-mediated fuel cells have the potential to address the challenges of other catalyst systems by protecting the electrode surface and controlling the local concentration and structure to increase current generation. The effects of increasing pH on dihydroxyacetone (DHA) oxidation by three viologen SAMs on Au electrodes are presented. Current and power generated during DHA oxidation at varying pH were measured and compared to those of bare Au performance. Two of the SAMs produced more current and power than bare Au at elevated pH. The SAM system produced more current and peak power per molecule than both dilute and concentrated homogeneous viologen systems in the same cell setup. These results demonstrate the benefits and limitations of electrodes modified with redox-active groups for the production of electricity from simple sugars and other carbohydrate sources. These results are encouraging for the development of new strategies for electrical power generation from renewable resources.

The next generation of physician-researchers: undergraduate medical students' and residents' attitudes, challenges, and approaches towards addressing them.

Undergraduate medical education and residency training are critical periods for conducting research. Medical diagnoses and therapies are direct results of successful research efforts that have advanced several scientific fields. This review highlights the importance of incorporating scientific research training into the curricula of undergraduate medical education and residency programs. We utilized key databases such as PubMed, Web of Science, and Google Scholar to conduct a narrative review of English-language articles published between 2002 and 2024. Ultimately, we selected 49 studies that examined the attitudes of undergraduate medical students and residents toward research, the common challenges they encounter while participating in research activities, and the strategies that support and encourage their involvement, all in alignment with our study objectives and keywords. In addition, we identified several relevant themes, including the value of research experience in shaping well-rounded medical professionals; the integrity of research practices in residency selection, which addresses concerns such as the arms race and misrepresentation, as well as the balance between quality and quantity; striking a balance between research and clinical training while promoting equity and inclusion; and effective programs and mentorship strategies to enhance research engagement. Translating the positive attitudes of undergraduate medical students and residents into improved knowledge and practice necessitates the development of structured mentoring programs and advanced training systems. Institutions must provide the necessary tools, guidance, and support to overcome research barriers. This will help establish an environment in undergraduate medical education and residency training that values research, facilitates access to it, and integrates it into the curriculum. To address the critical shortage of physician-researchers and improve evidence-based medical practice, it is crucial for medical schools to focus on research education and create avenues for student involvement. By tackling research challenges and implementing supportive strategies, these efforts empower the next generation of physician-researchers to embrace research, contribute to medical progress, and uphold the highest standards of patient care.

P63 affects distinct metabolic pathways during keratinocyte senescence, evaluated by metabolomic profile and gene expression analysis.

Unraveling the molecular nature of skin aging and keratinocyte senescence represents a challenging research project in epithelial biology. In this regard, depletion of p63, a p53 family transcription factor prominently expressed in human and mouse epidermis, accelerates both aging and the onset of senescence markers in vivo animal models as well as in ex vivo keratinocytes. Nonetheless, the biochemical link between p63 action and senescence phenotype remains largely unexplored. In the present study, through ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) and gas chromatography/mass spectrometry (GC/MS) metabolomic analysis, we uncover interesting pathways linking replicative senescence to metabolic alterations during p63 silencing in human keratinocytes. Integration of our metabolomic profiling data with targeted transcriptomic investigation empowered us to demonstrate that absence of p63 and senescence share similar modulation profiles of oxidative stress markers, pentose phosphate pathway metabolites and lyso-glycerophospholipids, the latter due to enhanced phospholipases gene expression profile often under p63 direct/indirect gene control. Additional biochemical features identified in deranged keratinocytes include a relevant increase in lipids production, glucose and pyruvate levels as confirmed by upregulation of gene expression of key lipid synthesis and glycolytic enzymes, which, together with improved vitamins uptake, characterize senescence phenotype. Silencing of p63 in keratinocytes instead, translates into a blunted flux of metabolites through both glycolysis and the Krebs cycle, likely due to a p63-dependent reduction of hexokinase 2 and citrate synthase gene expression. Our findings highlight the potential role of p63 in counteracting keratinocyte senescence also through fine regulation of metabolite levels and relevant biochemical pathways. We believe that our research might contribute significantly to the discovery of new implications of p63 in keratinocyte senescence and related diseases.

Health inequalities and indoor environments: research challenges and priorities

Health inequalities and indoor environments: research challenges and priorities

Toward revolutionizing PEMFC manufacturing for clean energy conversion: a review on the innovative contribution of 3D printing techniques

AbstractThree-dimensional printing (3DP) is a technology useful for fabricating both structural and energy devices. Of great concern to this review is promising nature of additive manufacturing (AM) for engineering fuel cells (FCs) for clean energy conversion. 3DP technique is useful for the fabrication of fuel cell components, and they offer waste minimization, low-cost, and complex geometric structures. In this review, significance of different 3DP techniques toward revolutionizing fuel cell fabrication is given. The aim is to unravel the importance and status of 3D-printed fuel cells and hence provides researchers and scientists with extensive opportunities of 3DP techniques for fuel cell engineering. After careful selection of state-of-the-art literatures, different kinds of 3DP techniques of relevance to electrolytes, electrodes, and other key components (e.g., gas diffusion layers (GDLs), bipolar plates (BPs), and membrane electrode assembly (MEA)) fabrication are explicitly discussed. Among the techniques, the best approaches are recommended for further studies. Advantages associated with these techniques are indicated for the benefit of those whose interests matter most on clean energy production. The challenges researchers are facing in the use of 3DP for fuel cell fabrications are identified. Possible solutions to the identified challenges are suggested as way forward to further development in this research area. It is expected that this review article will benefit engineers and scientists who have interest on clean energy conversion devices.

The Reliability and Validity of the ARIS Broader Impacts Rubric

More than 27 years have passed since the National Science Board identified Broader Impacts as one of two merit criteria for National Science Foundation proposals. Yet many researchers remain less certain of how to develop, implement, and assess a broader impact plan. This multi-method study of a Broader Impacts (BI) rubric analyzed expert panels that included BI professionals and researchers for both content validity and reliability. Focus groups with researchers explicate the challenges researchers face regarding BI plans and the potential value of the rubric as a tool for use. It revealed the challenges researchers have in weighing proven strategies versus innovative strategies, a bias documented by other scholars. Researchers stated concern with how to weigh the different facets of the rubric to arrive at a single score. Moreover, researchers reported that their disciplinary field influenced how they interpreted the audiences whose needs and interests may be met through BI plans. These distinctions represent a range of different types of community engaged scholarship (e.g., public information network, community-campus partnership, K-12 schools’ partnerships). Finally, researchers found the BI rubric useful in evaluating and developing their own BI plans as well as their role in panels to ultimately strengthen the field of funded BI work.

How Does Mangrove Restoration or Reforestation Change Trace Metal Pollution in Mangrove Ecosystems? A Review of Current Knowledge

In recent years, mangrove restoration has gained significant attention due to its carbon storage capacity, role as a pollution sink, and ecological and economic benefits. Moreover, the United Nations Sustainable Development Goals’ strategies include mangrove restoration. This review investigates the status of mangrove restoration research and the influence of restoration on trace metal accumulation, speciation, and associated risks in mangrove sediments. Our analysis reveals that research on mangrove restoration is increasing globally, with a notable surge in publications after 2017. However, fewer than 25 articles published between 2007 and 2024 address trace metals in restored mangroves, indicating limited focus from researchers on this topic. Research shows that mangrove restoration can quickly alter sediment properties, such as texture, salinity, and pH. As a result, restored sediments tend to accumulate higher organic carbon content than barren areas. Most studies also suggest that trace metal accumulation is higher in restored sites than in non-restored areas. However, metal speciation varies considerably between sites due to local environmental factors. Furthermore, many studies suggest that restoration may increase the risks posed by trace metals to aquatic biota. The challenges of mangrove restoration research include short study timeframes, low success rates, poorly defined targets, small-scale efforts, conflicts with local communities over resources and benefits, insufficient government funding, and a lack of historical data on community health and pollution status.

RJ-TinyViT: an efficient vision transformer for red jujube defect classification.

Compared to the surface defect detection of industrial products produced according to specified processes, the detection of surface defects in naturally grown red jujubes poses unique and significant challenges for researchers. The high diversity of surface defects, subtle distinctions from the background, low contrast, varying scales, and the presence of high levels of noise in images are among the factors that greatly amplify the complexity of defect detection tasks. Existing methods show some deficiencies in addressing these issues, mainly due to insufficient feature extraction capabilities and overly complex network structures, leading to limitations in model efficiency and practical application performance. To tackle the challenges associated with red jujube surface defect detection, this study proposes an optimized Tiny Vision Transformer (TinyViT) network structure, named RJ-TinyViT. This method refines the TinyViT-5m network structure to reduce network burden and introduces an improved Multi-Kernel Block (MK Block) and an improved Mobile Inverted Bottleneck Convolution Block (MBConv Block) to enhance feature extraction capabilities. Additionally, we have integrated the Coordinate Attention (CA) module to enhance the model's capacity for recognizing and focusing on features of surface defects on red jujubes. Experimental results show that RJ-TinyViT achieved a classification accuracy of 93.38%, marking an improvement of 1.84% over the original TinyViT network. At the same time, its Floating-point Operations (FLOPs) and Parameters (Params) were reduced to 58.97% and 39.84% of the original TinyViT network, respectively. These results not only demonstrate that RJ-TinyViT achieves model lightweighting while maintaining high accuracy but also highlight its value in practical industrial applications.

Development of surface-texturized black silicon through metal-assisted chemical etching and its application in the thermophotovoltaic field: a review and recommendation

Abstract The Shockley-Queisser limit poses a significant challenge in solar technology research, limiting the theoretical efficiency to around 30%. Thermophotovoltaic (TPV) systems have emerged as a solution by incorporating a thermal absorber in traditional solar cell setups to achieve total efficiency beyond the limits. The efficiency of the overall system heavily depends on the performance and quality of the thermal absorber, which absorbs photons from the heat source and transfers them to the TPV cell. However, complex and expensive fabrication processes have hindered widespread adoption of TPV technology. The well-established metal assisted chemical etching (MACE) method could be the best choice to mitigate these as it is a cost-effective, scalable, and mass-production-friendly process, which is widely used for surface texturization, creating nanostructures like nanopores, pyramids, and nanowires. MACE
technique is also suitable for producing highly efficient silicon-based thermal absorbers with over 90% absorption rate, which can contribute to increased total conversion efficiency. However, it does not come without challenges such as maintaining control over the etch rate in order to achieve uniformity. This paper comprehensively reviews the utilization of MACE for fabricating silicon-based thermal absorbers in TPV systems with the range of effective wavelengths of 600 – 2000 nm which corresponds to the energy level of 0.55 – 1.85 eV. The advantages and challenges of MACE, along with characterization techniques, are extensively discussed. By providing valuable insights, this paper aims to support researchers interested in advancing TPV technology.

Curcuma Longa Derived Heteroatom‐Self‐Doped Porous Carbon for Cost‐Effective Solid‐State Supercapacitors

The cost‐effective synthesis of supercapacitors is a significant challenge in energy storage research. This study introduces a sustainable and cost‐effective method for synthesizing biomass‐derived carbon for solid‐state supercapacitor fabrication. Turmeric (Curcuma longa) plant waste is carbonized at three distinct temperatures (500, 600, and 700 °C for 3 hours), and the resulting carbon is characterized to determine the optimal carbonization conditions. Physicochemical characterization revealed the presence of multiple heteroatoms, which may contribute to enhanced capacitance. Electrochemical studies showed that the carbonized material at 600 °C achieved the highest specific capacitance of 110.04 F/g at 0.1 A/g current density. After activation, the specific capacitance increased to 188 F/g at 0.1 A/g current density. A solid‐state supercapacitor was assembled using the synthesized activated carbon and PVA/H2SO4 gel‐type electrolyte. The resulting device exhibited an impressive specific capacitance of 92.33 F/g at 0.1 A/g, a power density of 4295.28 W/kg, and a cycling stability of 97.42%. This supercapacitor shows promising potential as an economical and sustainable energy storage solution for portable electronics.

Instagram data for validating Nothofagus pumilio distribution mapping in the Southern Andes: A novel ground truthing approach

The availability of valid, non-biased species occurrence data has always been a major challenge for biodiversity research and modelling studies. Data from open-source databases or remote sensing are promising approaches to increase the availability of species occurrence data. However, these data may contain spatial, temporal, and taxonomic biases or require ground truthing. In recent years, social media has received attention for its contribution to species occurrence data sampling and ground truthing approaches. The wide reach of social media platforms allows for rapid and large-scale analyses. Here we introduce a novel Instagram ground truthing approach to validate the occurrence mapping of Nothofagus pumilio across its entire distribution range. The treeline species of the southern Andes has been extensively studied in small-scale studies, but large-scale modelling approaches are largely missing due to limited accessibility to treeline sites resulting in a lack of occurrence data. The content posted on the social media platform Instagram consists of images and videos in which the species N. pumilio and its location can be identified. By searching for suitable posts using hashtags and location tags, we created 1238 Instagram ground truthing points. We compared the performance of our dataset with open-source data from the Global Biodiversity Information Facility (GBIF) through visual, quantitative, and bias analyses, acknowledging that both social media-based and Citizen Science data are subject to sampling and spatial biases due to collection in human-accessible areas. The Instagram ground truthing points were subsequently used to validate remote sensing occurrence data, generated using Sentinel-2 level 2A data and Supervised Classification. The combined approach – Instagram ground truthing and remote sensing – allows for the collection of occurrence data over the entire latitudinal range of N. pumilio, covering approximately 2000 km. The use of social media content provides potentially important contributions to species occurrence data sampling and ground truthing In our study we introduce a novel ground truthing approach for species occurrence data sampling based on Instagram data Instagram ground truthing points, combined with Supervised Classification generate species occurrence data of Nothofagus pumilio over its entire distribution range in the southern Andes The performance of the Instagram ground truthing points is evaluated by comparison with existing data from the GBIF database. Our Instagram ground truthing approach demonstrates a new way of sampling species occurrence data and can be applied to other suitable species and study areas.