Find research datasets worth reusing

Comprehensive Dataset for Detecting Road Anomalies in Diverse Real-World Situations

<p>In Smart Cities, technologies are playing an important role in efficiently managing the rapid growth of the world's industrialization today. The deployment of surveillance cameras has proliferated to improve public safety and security. Many Closed-Circuit Television (CCTV) cameras have been installed to monitor and safeguard public spaces efficiently within the cities. Despite advancements in technology, video and image processing still largely rely on manual observation. This manual analysis is time-consuming, prone to missing critical details, and costly in terms of labor and resources. Nevertheless, monitoring large video feeds for long periods indicates fatigue, demise of focus, and errors, particularly when video surveillance is a necessity. <br>Road anomaly detection is one of the prominent computer vision issues that researchers have investigated to guarantee public safety. Road anomaly identification is increasingly difficult and complex due to the variety and complexity of abnormalities. <br>Deep learning algorithms must be efficient but also need a large dataset to train to recognize road anomalies in different environments. We proposed a custom real-world data set containing road anomaly images and videos that are made available to the public and private surveillance systems. Primary data were collected from diverse sites in Pakistan, and the data were gathered by recording videos and capturing images by using mobile and surveillance cameras The dataset encompasses five major categories of road anomaly effects.: vehicle accidents, vehicle fire, fighting, snatching(gunpoint), and potholes that classification modeling while promoting improvement in both scientific research and realistic application. The dataset also encompasses annotations with You Only Look Once (YOLO) based bounding boxes and class label files in text format for every image.   <br>The researchers can utilize data to train and validate their anomaly detection algorithms and models, thus increasing public security and safety. This dataset focuses on natural environment scenes with a detailed examination of safe transportation and impacts on broader environmental knowledge. Data can give to the liable and ethical arrangement of Artificial Intelligence technologies in surveillance security system</p>

Data for: Exploring Transition States of Protein Conformational Changes via Out-of-Distribution Detection in the Hyperspherical Latent Space

<p>This contains all the TS-DART training results as well as the raw MD simulation data reported in the preprint "Exploring Transition States of Protein Conformational Changes via Out-of-Distribution Detection in the Hyperspherical Latent Space".</p>

DAS Data for the figure in the paper entitled "A Hybrid Earthquake Detection Method for Distributed Acoustic Sensing Array Data and Its Application to the 2022 Menyuan Earthquake Sequence"

<p>The DAS data can be loaded using numpy. The sampling rate is 100 Hz, and each row is a time series for that channel.</p>

Table 1 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA

<p><b>Table 1.</b> Number of field samples (including controls) for each qPCR assay at each site sampled for eDNA in 2018 and 2019 in western Lake Erie. DR = Detroit River, HP = Hot Ponds, MB = Maumee Bay. Note that samples are site-specific.</p><table><tbody><tr><th>Site</th><th>Year</th></tr><tr><th>2018</th><th>2019</th></tr><tr><th>Assay</th><th>Samples</th><th>Assay</th><th>Samples</th></tr></tbody><tbody><tr><th>DR</th><td>GCTM10 GCTM22 GCTM32</td><td>78 78 78</td><td>GCTM10 GCTM22 GCTM32</td><td>81 81 81</td></tr><tr><th>HP</th><td>GCTM10 GCTM22 GCTM32</td><td>77 77 77</td><td>GCTM10 GCTM22 GCTM32</td><td>82 82 82</td></tr><tr><th>MB</th><td>GCTM10 GCTM22 GCTM32</td><td>78 78 78</td><td>GCTM10 GCTM22 GCTM32</td><td>80 80 80</td></tr></tbody></table>

Table 4 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA

<p><b>Table 4.</b> Percentage of positive eDNA replicate detections in each month and site in 2018 and 2019 in western Lake Erie (based on at least one positive detection on at least one marker and one replicate). All markers (GCTM10, GCTM 22, GCTM32) were used to calculate these proportions. Samples were collected monthly from June to November for each site. The number of telemetered grass carp detected within 7 days before sampling for eDNA is denoted in parentheses. Acoustic telemetry receivers in MB in 2018 were not available. DR = Detroit River, HP = Hot Ponds, and MB = Maumee Bay. NA denotes when acoustic telemetry receivers were not in operation.</p><table><tbody><tr><th>Year</th></tr><tr><th>Site</th><th>2018</th><th>2019</th></tr><tr><th></th><th>June</th><th>July</th><th>Aug</th><th>Sept</th><th>Oct</th><th>Nov</th><th>May</th><th>June</th><th>July</th><th>August</th><th>Oct</th><th>Nov</th></tr></tbody><tbody><tr><th>DR</th><td>0.0%</td><td>2.3%</td><td>2.3%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>4.5%</td><td>10.6</td><td>14.1</td><td>17.4%</td><td>14.1%</td><td>2.2%</td></tr><tr><td>(1)</td><td>(1)</td><td>(2)</td><td>(1)</td><td>(2)</td><td>(0)</td><td>(1)</td><td>% (1)</td><td>% (1)</td><td>(2)</td><td>(2)</td><td>(0)</td></tr><tr><th>HP</th><td>0.0%</td><td>0.1%</td><td>4.1%</td><td>2.2%</td><td>15.8%</td><td>0.0%</td><td>9.0%</td><td>1.5%</td><td>34.8</td><td>1.5%</td><td>15.8%</td><td>22.7%</td></tr><tr><td>(1)</td><td>(1)</td><td>(2)</td><td>(2)</td><td>(3)</td><td>(NA)</td><td>(2)</td><td>(2)</td><td>% (2)</td><td>(3)</td><td>(2)</td><td>(2)</td></tr><tr><th>MB</th><td>12.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>0.0%</td><td>6.1%</td><td>37.1</td><td>8.3%</td><td>8.3%</td><td>0.0%</td></tr><tr><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(NA)</td><td>(0)</td><td>(0)</td><td>% (0)</td><td>(0)</td><td>(0)</td><td>(0)</td></tr></tbody></table>

Table 3 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA

<p><b>Table 3.</b> Candidate set of hierarchical occupancy models used to estimate probability of grass carp eDNA occurrence among sites (ψ), the conditional probability of grass carp eDNA occurrence at a sampling locality within a site given that grass carp were present at the site (Θ), and the conditional probability of eDNA detection on replicate filters collected at a sampling locality given that the species is present at the sampling locality <i>(p</i>) from three sites in western Lake Erie sampled in 2018 and 2019. Covariates included location (site), time (Month) and probe type (GCTM10, GCTM22, GCTM32). Model comparison was evaluated with the Widely Applicable Information Criterion (WAIC).</p><table><tbody><tr><th>Model</th><th>WAIC</th><th>Δ WAIC</th><th>Lack of fit</th><th>Predicted Variance</th></tr></tbody><tbody><tr><th>ψ(Site)Θ(Site)p(.)</th><td>309.44</td><td>-</td><td>298.70</td><td>18.63</td></tr><tr><th>ψ(.)Θ(Site)p(.)</th><td>309.50</td><td>0.06</td><td>298.99</td><td>10.73</td></tr><tr><th>ψ(.)Θ(Month)p(.)</th><td>317.61</td><td>8.18</td><td>299.05</td><td>18.56</td></tr><tr><th>ψ(Season)Θ(.)p(.)</th><td>317.67</td><td>8.24</td><td>299.01</td><td>18.65</td></tr><tr><th>ψ(Month)Θ(.)p(.)</th><td>317.72</td><td>8.28</td><td>299.04</td><td>18.67</td></tr><tr><th>ψ(Season)Θ(Season)p(.)</th><td>317.84</td><td>8.40</td><td>299.04</td><td>18.79</td></tr><tr><th>ψ(.)Θ(Season)p(.)</th><td>317.74</td><td>8.31</td><td>299.07</td><td>18.66</td></tr><tr><th>ψ(Site)Θ(.)p(.)</th><td>317.94</td><td>8.51</td><td>299.04</td><td>18.90</td></tr><tr><th>ψ(.)Θ(.)p(.)</th><td>325.00</td><td>15.57</td><td>305.50</td><td>20.21</td></tr><tr><th>ψ(Season)Θ(Site)p(.)</th><td>325.41</td><td>15.98</td><td>305.49</td><td>19.91</td></tr><tr><th>ψ(Site + Season)Θ(.)p(.)</th><td>325.59</td><td>16.16</td><td>305.44</td><td>20.15</td></tr><tr><th>ψ(Site)Θ(Season)p(.)</th><td>325.72</td><td>16.29</td><td>305.48</td><td>20.23</td></tr><tr><th>ψ(Site + Season)Θ(Site + Season)p(.)</th><td>325.92</td><td>16.49</td><td>305.49</td><td>20.43</td></tr><tr><th>ψ(.)Θ(Site + Season)p(.)</th><td>326.37</td><td>16.94</td><td>305.52</td><td>20.84</td></tr><tr><th>ψ(Site)Θ(Month)p(.)</th><td>329.57</td><td>20.14</td><td>308.65</td><td>20.92</td></tr><tr><th>ψ(Month)Θ(Month)p(.)</th><td>330.14</td><td>20.71</td><td>308.66</td><td>21.84</td></tr><tr><th>ψ(Site + Season)Θ(Site)p(Probe)</th><td>377.63</td><td>68.20</td><td>276.90</td><td>100.72</td></tr><tr><th>ψ(Site + Season)Θ(.)p(Probe)</th><td>378.35</td><td>68.92</td><td>277.00</td><td>101.34</td></tr><tr><th>ψ(Site + Season)Θ(Site + Season)p(Probe)</th><td>378.42</td><td>68.99</td><td>277.00</td><td>101.41</td></tr><tr><th>ψ(Site + Season)Θ(Season)p(Probe)</th><td>378.55</td><td>69.12</td><td>277.09</td><td>101.46</td></tr><tr><th>ψ(Season)Θ(Site + Season)p(Probe)</th><td>379.41</td><td>69.98</td><td>277.28</td><td>102.10</td></tr><tr><th>ψ(Site)Θ(Site + Season)p(Probe)</th><td>379.62</td><td>70.19</td><td>277.40</td><td>102.21</td></tr><tr><th>ψ(.)Θ(Site + Season)p(Probe)</th><td>379.88</td><td>70.45</td><td>277.31</td><td>102.57</td></tr><tr><th>ψ(Site + Month)Θ(.)p(.)</th><td>383.56</td><td>74.13</td><td>282.86</td><td>100.69</td></tr><tr><th>ψ(Site + Month)Θ(Site + Month)p(.)</th><td>385.47</td><td>76.04</td><td>283.38</td><td>102.09</td></tr><tr><th>ψ(Site + Month)Θ(Site + Month)p(Probe)</th><td>386.95</td><td>77.52</td><td>282.90</td><td>104.05</td></tr><tr><th>ψ(.)Θ(Site + Month)p(.)</th><td>396.44</td><td>87.01</td><td>292.14</td><td>104.29</td></tr><tr><th>ψ(.)Θ(.)p(Probe)</th><td>396.45</td><td>87.01</td><td>292.14</td><td>104.29</td></tr></tbody></table>

Table 2 in Assessing grass carp (Ctenopharyngodon idella) occupancy and detection probability within Lake Erie from environmental DNA

<p><b>Table 2.</b> Gene region, primer, and probe sequences used to amplify GCTM10,GCTM22, and GCTM32 for grass carp.</p><table><tbody><tr><th>Gene</th><th>Primers and Probes</th><th>Sequence</th></tr></tbody><tbody><tr><th>ND2</th><td>Forward</td><td>5′- CCYTACGTACTCGCAATTCTAC -3′</td></tr><tr><th>ND2</th><td>Reverse</td><td>5′- GTGGTGGTGTTGGGCTATTA -3′</td></tr><tr><th>ND2</th><td>Probe</td><td>5′- VIC- ACCCTAACCTTTGCTAGCTCCCAC -MGBNFQ-3′</td></tr><tr><th>COII</th><td>Forward</td><td>5′- CCGACTCCTAGAAACAGATCAC -3′</td></tr><tr><th>COII</th><td>Reverse</td><td>5′- GGGACAGCTCAGGAATGTAATA -3′</td></tr><tr><th>COII</th><td>Probe</td><td>5′- 56-FAM- CCAGTTCGT/ZEN/GTCCTAGTATCTGCCGA -3IABkFQ -3′</td></tr><tr><th>COIII</th><td>Forward</td><td>5′- CCACGGACTACACGTCATTATT -3′</td></tr><tr><th>COIII</th><td>Reverse</td><td>5′-GATGTTCGGATGTAAAGTGGTATTG -3′</td></tr><tr><th>COIII</th><td>Probe</td><td>5′-NED- TTCCTAGCTGTTTGCCTTCTCCGT -MGBNFQ-3′</td></tr></tbody></table>

IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection (part 5)

<p>This is part 5 of the IDNet dataset of our research paper "IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection". Here's a link to the paper: https://arxiv.org/pdf/2408.01690</p> <p>Citation:</p> <p>@article{guan2024idnet,<br>  title={IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection},<br>  author={Guan, Hong and Wang, Yancheng and Xie, Lulu and Nag, Soham and Goel, Rajeev and Swamy, Niranjan Erappa Narayana and Yang, Yingzhen and Xiao, Chaowei and Prisby, Jonathan and Maciejewski, Ross and Zou, Jia},<br>  journal={arXiv preprint arXiv:2408.01690},<br>  year={2024}<br>}</p>

IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection (part 1)

<p>This is part 1 of the IDNet dataset of our research paper "IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection". Here's a link to the paper: https://arxiv.org/pdf/2408.01690</p> <p>Citation:</p> <p>@article{guan2024idnet,<br>  title={IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection},<br>  author={Guan, Hong and Wang, Yancheng and Xie, Lulu and Nag, Soham and Goel, Rajeev and Swamy, Niranjan Erappa Narayana and Yang, Yingzhen and Xiao, Chaowei and Prisby, Jonathan and Maciejewski, Ross and Zou, Jia},<br>  journal={arXiv preprint arXiv:2408.01690},<br>  year={2024}<br>}</p>

IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection (part 4)

<p>This is part 4 of the IDNet dataset of our research paper "IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection". Here's a link to the paper: https://arxiv.org/pdf/2408.01690</p> <p>Citation:</p> <p>@article{guan2024idnet,<br>  title={IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection},<br>  author={Guan, Hong and Wang, Yancheng and Xie, Lulu and Nag, Soham and Goel, Rajeev and Swamy, Niranjan Erappa Narayana and Yang, Yingzhen and Xiao, Chaowei and Prisby, Jonathan and Maciejewski, Ross and Zou, Jia},<br>  journal={arXiv preprint arXiv:2408.01690},<br>  year={2024}<br>}</p>

IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection (part 3)

<p>This is part 3 of the IDNet dataset of our research paper "IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection". Here's a link to the paper: https://arxiv.org/pdf/2408.01690</p> <p>Citation:</p> <p>@article{guan2024idnet,<br>  title={IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection},<br>  author={Guan, Hong and Wang, Yancheng and Xie, Lulu and Nag, Soham and Goel, Rajeev and Swamy, Niranjan Erappa Narayana and Yang, Yingzhen and Xiao, Chaowei and Prisby, Jonathan and Maciejewski, Ross and Zou, Jia},<br>  journal={arXiv preprint arXiv:2408.01690},<br>  year={2024}<br>}</p>

IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection (part 2)

<p>This is part 2 of the IDNet dataset of our research paper "IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection". Here's a link to the paper: https://arxiv.org/pdf/2408.01690</p> <p>Citation:</p> <p>@article{guan2024idnet,<br>  title={IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection},<br>  author={Guan, Hong and Wang, Yancheng and Xie, Lulu and Nag, Soham and Goel, Rajeev and Swamy, Niranjan Erappa Narayana and Yang, Yingzhen and Xiao, Chaowei and Prisby, Jonathan and Maciejewski, Ross and Zou, Jia},<br>  journal={arXiv preprint arXiv:2408.01690},<br>  year={2024}<br>}</p>

IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection (part 7)

<p>This is part 7 of the IDNet dataset of our research paper "IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection". Here's a link to the paper: https://arxiv.org/pdf/2408.01690</p> <p>Citation:</p> <p>@article{guan2024idnet,<br>  title={IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection},<br>  author={Guan, Hong and Wang, Yancheng and Xie, Lulu and Nag, Soham and Goel, Rajeev and Swamy, Niranjan Erappa Narayana and Yang, Yingzhen and Xiao, Chaowei and Prisby, Jonathan and Maciejewski, Ross and Zou, Jia},<br>  journal={arXiv preprint arXiv:2408.01690},<br>  year={2024}<br>}</p>

IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection (part 6)

<p>This is part 6 of the IDNet dataset of our research paper "IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection". Here's a link to the paper: https://arxiv.org/pdf/2408.01690</p> <p>Citation:</p> <p>@article{guan2024idnet,<br>  title={IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection},<br>  author={Guan, Hong and Wang, Yancheng and Xie, Lulu and Nag, Soham and Goel, Rajeev and Swamy, Niranjan Erappa Narayana and Yang, Yingzhen and Xiao, Chaowei and Prisby, Jonathan and Maciejewski, Ross and Zou, Jia},<br>  journal={arXiv preprint arXiv:2408.01690},<br>  year={2024}<br>}</p>

IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection (part 8)

<p>This is part 8 of the IDNet dataset of our research paper "IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection". Here's a link to the paper: https://arxiv.org/pdf/2408.01690</p> <p>Citation:</p> <p>@article{guan2024idnet,<br>  title={IDNet: A Novel Dataset for Identity Document Analysis and Fraud Detection},<br>  author={Guan, Hong and Wang, Yancheng and Xie, Lulu and Nag, Soham and Goel, Rajeev and Swamy, Niranjan Erappa Narayana and Yang, Yingzhen and Xiao, Chaowei and Prisby, Jonathan and Maciejewski, Ross and Zou, Jia},<br>  journal={arXiv preprint arXiv:2408.01690},<br>  year={2024}<br>}</p>

Confirmation of four hot Jupiters detected by TESS using follow-up spectroscopy from MaHPS at Wendelstein together with NEID and TRES

<p>Supplementary material to the paper 'Confirmation of four hot Jupiters detected by TESS using follow-up spectroscopy from MaHPS at Wendelstein together with NEID and TRES':</p> <p>Fig. Z1 - Z4: Fit of the photometric lightcurves (Sec. 2.2)</p> <p>ZT1 -ZT4: Tables showing the parameters, the prior distribution and the posteriors for the four targets after performing a joint fit with the Python package 'juliet' (Sec. 5.1)</p> <p>Fig. Z5_6: S/N values for the observations taken with MaHPS (Sec. 3.2.1)</p> <p>Fig. Z7: O-C diagrams for the search of TTVs (Sec. 5.3.2)</p> <p> </p> <p> </p>

Dataset for Label-free and Sensitive Detection of Citrus Bark Cracking Viroid in Hop Using Ti3C2Tx MXene-modified Genosensor

<p>This is a dataset for a paper "Label-free and Sensitive Detection of Citrus Bark Cracking Viroid in Hop Using Ti3C2Tx MXene-modified Genosensor". All details about the data are included in the readme file.</p>

DETECTION _PROSODIQUE_Data_POST

Open the record for dataset details and reuse information.

A Deep-Learning Approach for Visual Detection of an AUV Docking Station - Dataset

<div>This dataset was used to train the models from the paper "A Deep-Learning Approach for Visual Detection of an AUV Docking Station" published by Ahmad et al. at Oceans 2024 Conference in Halifax.</div> <div> </div> <div># Dataset</div> <div> </div> <div>The dataset contains:</div> <div> </div> <div>1. images from Abisko Lake in Sweden[1]</div> <div>2. images recorded in the Maritime Hall basin at DFKI</div> <div> </div> <div># File contents</div> <div>Each of these datasets are put into seperate directories. The images were annotated using CVAT[2].</div> <div> </div> <div>The dataset has been exported into the following formats:</div> <div> </div> <div>1. YOLO</div> <div>2. PascalVOC</div> <div>3. COCO</div> <div> </div> <div>The exported datasets does not contain raw images, rather they are places into a seperate zip folder.</div> <div> </div> <div> </div> <div>           </div> <div># References</div> <div>[1]: <a href="https://zenodo.org/records/7035132">https://zenodo.org/record/7035132#.ZDfKE5FBzJU</a></div> <div>[2]: https://www.cvat.ai/</div>

Data from 'Sustainability in the laboratory: evaluating the reuseability of microtiter plates for PCR and fragment detection'

<p>This repository contains the data for the manuscript: Sustainability in the laboratory: evaluating the reuseability of microtiter plates for PCR and fragment detection.</p> <p>The script can be found here: https://github.com/AneLivB/SGP</p> <p><strong>Files: </strong></p> <p><strong>R15*_R*.csv</strong></p> <p>The processed data used for the script. All other files of the same name format contain the same elements!</p> <ul> <li> <p>Columns: Rack_location, ID, all loci</p> <ul> <li> <p>Rack_location: the location on the microwell plate the individual had in the wet lab</p> </li> <li> <p>ID: individual identification tag of the animal the DNA sample stems from</p> </li> <li> <p>_a and _b denotes the first and second allele of all loci</p> </li> </ul> </li> </ul> <p><strong>Mismatches</strong></p> <p>A dataframe created by the script and later used to calcualte per treatment single-locus genotype error rates.</p> <ul> <li> <p>Columns: Rack.1, Rack.2, Treatment, No..of.mistyped.alleles, No..of.mistyped.reactions, No..of.reactions, Allelic.error.rate, Genotype.error.rate</p> <ul> <li> <p>Rack.1: One of three racks (R154, R155, R156) from the standard protocol</p> </li> <li> <p>Rack.2: One of three racks (R154, R155, R156) from one of the other treatments (e.g. R154_R2, R154_R3, R154_R4)</p> </li> <li> <p>Treatment: One of three treatments (Internal control, Reused detection plate, Reused PCR plate)</p> </li> <li> <p>No..of.mistyped.alleles: Number of mismatched alleles within one treatment group, within a DNA plate</p> </li> <li> <p>No..of.mistyped.reactions: Number of mismatched single-locus genotypes within one treatment group, within a DNA plate</p> </li> <li> <p>No..of.reactions: Number of single-locus genotypes within one treatment group, within a DNA plate</p> </li> <li> <p>Allelic.error.rate: Error rate per allele</p> </li> <li> <p>Genotype.error.rate: Error rate per single-locus genotypes</p> </li> </ul> </li> </ul> <p><strong>model.mismatch.2.Rdata</strong></p> <p>RData file containing the model output.</p> <p> </p> <p><strong>Manuscript abstract: </strong></p> <p>Single-use plastics (SUPs) are indispensable in laboratory research, but their disposal contributes substantially to environmental pollution. Consequently, reusing common SUP items such as microtiter plates represents a promising strategy for improving laboratory sustainability. However, the key challenge lies in determining whether SUP reuse can be implemented without sacrificing data quality. To investigate this, we conducted a simple experiment to assess the impact of reusing microtiter plates on microsatellite genotyping accuracy. Plates previously used for PCR and fragment detection were cleaned using an environmentally friendly method and then reused. Our results indicate that, while reusing PCR plates significantly increases genotyping error rates due to residual DNA contamination, detection plates can be reused without compromising data quality. Our approach offers laboratories a practical and sustainable option for reducing SUP waste and costs while maintaining research integrity.</p>