Find research datasets worth reusing

Fig. 4 in Pollen characters and DNA sequence data converge on a monophyletic genus Iresine (Amaranthaceae, Caryophyllales) and help to elucidate its species diversity

Fig. 4. Scanning electron photomicrographs of pollen grains from the Iresine clade (= Iresinoids). A, Iresine cassiniiformis (Borsch & al. 3792); B, Iresine type XXXIV (Borsch & al. 5412); C, Iresine rzedowskii (Borsch & al. 3793); D, Iresine ajuscana (Borsch & al. 5404); E, Iresine orientalis (Borsch & al. 5404); F, Iresine discolor (Purpus 3453); G, Magnification of aperture with details of mesoporium of pollen from the same plant; H, Iresine hartmanii (Tenorino 1864); I, Iresine type XXXIV (Borsch & al. 5390). — Scale = 10 µm apart from G where it is 4 µm.

Fig. 7. Scanning electron photomicrographs from the alternantheroid and gomphrenoid clades. A in Pollen characters and DNA sequence data converge on a monophyletic genus Iresine (Amaranthaceae, Caryophyllales) and help to elucidate its species diversity

Fig. 7. Scanning electron photomicrographs from the alternantheroid and gomphrenoid clades. A, Pedersenia cardenasii (Borsch & Ortuño 3504); B, Pedersenia sp. (Borsch & Ibisch 3532); C, Magnification of aperture and details of mesoporia of pollen from the same plant; D, Hebanthe occidentalis (Borsch & Ortuño 3512); E, Pfaffia dunaliana (Borsch & Ortuño 3756); F, Magnification of aperture and details of mesoporia of pollen from the same plant. — Scale = 10 µm apart from C where it is 4 µm.

Fig. 5. Scanning electron photomicrographs from the Iresine clade. A in Pollen characters and DNA sequence data converge on a monophyletic genus Iresine (Amaranthaceae, Caryophyllales) and help to elucidate its species diversity

Fig. 5. Scanning electron photomicrographs from the Iresine clade. A, Iresine hebanthoides (Borsch & al. 5415); B, Magnification of aperture and details of mesoporia of pollen from the same plant; C, Iresine sousae (Mendez Ton 7192, isotype B); D, Iresine nitens (Borsch & al. 3770); E, Magnification of aperture and details of mesoporia of pollen from the same plant; F, Iresine latifolia (Borsch & al. 3790); G, Magnification of aperture and details of mesoporia of pollen from the same plant; H, Iresine diffusa (Borsch & al. 3676); I, Irenella cysotricha (Asplund 16555). — Scale = 10 µm apart from B, E and G where it is 4 µm and I where it is 2 µm.

Fig. 6. Scanning electron photomicrographs from the Iresine clade. A in Pollen characters and DNA sequence data converge on a monophyletic genus Iresine (Amaranthaceae, Caryophyllales) and help to elucidate its species diversity

Fig. 6. Scanning electron photomicrographs from the Iresine clade. A, Iresine angustifolia (Zumaya & al. 81); B, Iresine nigra (Zumaya & al. 77); C, View from a different angle onto a pollen grain from the same plant; D, Iresine interrupta (Zumaya 62); E, Iresine borschii (Ventura 9443, paratype); F, Iresine arbuscula (Castillo s.n.). — Scale = 10 µm.

Fig. 1 in Pollen characters and DNA sequence data converge on a monophyletic genus Iresine (Amaranthaceae, Caryophyllales) and help to elucidate its species diversity

Fig. 1. Morphological diversity of Iresine. A, Synflorescence of I. interrupta (Borsch & al. 3789); B, Pistillate flowers at maturity and C, Staminate flowers of I. interrupta (Borsch & al. 3789); D, Upright woody stem of Iresine type XXXIV (Borsch & al. 5390); E, Inflorescence and F, Woody stem of I. nigra (S. Zumaya & al. 77); G, Part of synflorescence with staminate (Borsch & al. 5385) and H, Pistillate flowers of I. ajuscana (Borsch & al. 5367). — Photos: T. Borsch.

Supplementary material 2 from: Martín-Forés I, Casado MA, Castro I, del Pozo A, Molina-Montenegro MA, de Miguel JM, Acosta-Gallo B (2018) Variation in phenology and overall performance traits can help to explain the plant invasion process amongst Mediterranean ecosystems. NeoBiota 41: 67-89. https://doi.org/10.3897/neobiota.41.29965

Supplementary material 2 from: Martín-Forés I, Casado MA, Castro I, del Pozo A, Molina-Montenegro MA, de Miguel JM, Acosta-Gallo B (2018) Variation in phenology and overall performance traits can help to explain the plant invasion process amongst Mediterranean ecosystems. NeoBiota 41: 67-89. https://doi.org/10.3897/neobiota.41.29965

Supplementary material 4 from: Martín-Forés I, Casado MA, Castro I, del Pozo A, Molina-Montenegro MA, de Miguel JM, Acosta-Gallo B (2018) Variation in phenology and overall performance traits can help to explain the plant invasion process amongst Mediterranean ecosystems. NeoBiota 41: 67-89. https://doi.org/10.3897/neobiota.41.29965

Supplementary material 4 from: Martín-Forés I, Casado MA, Castro I, del Pozo A, Molina-Montenegro MA, de Miguel JM, Acosta-Gallo B (2018) Variation in phenology and overall performance traits can help to explain the plant invasion process amongst Mediterranean ecosystems. NeoBiota 41: 67-89. https://doi.org/10.3897/neobiota.41.29965

Supplementary material 3 from: Martín-Forés I, Casado MA, Castro I, del Pozo A, Molina-Montenegro MA, de Miguel JM, Acosta-Gallo B (2018) Variation in phenology and overall performance traits can help to explain the plant invasion process amongst Mediterranean ecosystems. NeoBiota 41: 67-89. https://doi.org/10.3897/neobiota.41.29965

Supplementary material 3 from: Martín-Forés I, Casado MA, Castro I, del Pozo A, Molina-Montenegro MA, de Miguel JM, Acosta-Gallo B (2018) Variation in phenology and overall performance traits can help to explain the plant invasion process amongst Mediterranean ecosystems. NeoBiota 41: 67-89. https://doi.org/10.3897/neobiota.41.29965

Supplementary material 1 from: Martín-Forés I, Casado MA, Castro I, del Pozo A, Molina-Montenegro MA, de Miguel JM, Acosta-Gallo B (2018) Variation in phenology and overall performance traits can help to explain the plant invasion process amongst Mediterranean ecosystems. NeoBiota 41: 67-89. https://doi.org/10.3897/neobiota.41.29965

Figure S1 : Explanation note: Distribution of Leontodonsaxatilis, Hypochaerisglabra and Trifoliumglomeratum in both the native (Spain) and the introduced (Chile) ranges.

opencc-by-4.0Apr 2019View details →

User Study Data from "HaptiGlow: Helping Users Position their Hands for Better Mid-Air Gestures and Ultrasound Haptic Feedback"

This dataset contains user study data from the experiment reported in our IEEE World Haptics Conference 2019 paper.

opennotspecifiedMar 2016View details →

Fig. 3 in Molecular data in conjunction with morphology help resolve the Hemidactylus brookii complex (Squamata: Gekkonidae)

Fig. 3 Principal component analysis using morphometric data of individuals from Hemidactylus brookii complex

opennotspecifiedMar 2016View details →

Fig. 2 in Molecular data in conjunction with morphology help resolve the Hemidactylus brookii complex (Squamata: Gekkonidae)

Fig. 2 Maximum Likelihood (ML) tree based on mitochondrial and nuclear data. The values on each node represent ML bootstrap value/ Bayesian posterior probability. Support values below 50/0.5 have been denoted as '-'. Voucher numbers of sequences obtained from NCBI are

opencc-by-4.0Oct 2024View details →

Do AI assistants help students write formal specifications? A study with ChatGPT and the B-Method

Replication package of the paper:  "Do AI assistants help students write formal specifications? A study with ChatGPT and the B-Method."  Submitted to the 37th edition of the IEEE Conference on Software Engineering Education and Training (CSEE&T), co-located with ICSE 2025.

opencc-by-4.0Oct 2024View details →

Datasets and scripts related to the paper: "Can Generative AI Help us in Open Coding of Software Engineering Data?"

This replication package contains datasets and scripts related to the paper: "Can Generative AI Help us in Open Coding of Software Engineering Data?" The replication package is organized into two directories: <ul> <li> <code>manual_analysis</code>: This directory contains all sheets used to perform the manual analysis for RQ1, RQ2, and RQ3. </li> <li> <code>stats</code>: This directory contains all datasets, scripts, and results metrics used for the quantitative analyses of RQ1 and RQ2. </li> </ul> In the following, we describe the content of each directory: <h2>manual_analysis</h2> <ul> <li> <code>manual_analysis_rq1</code>: This directory contains all sheets used to perform manual analysis for RQ1 (independent and incremental coding). <ul> <li> The sub-directory <code>incremental_coding</code> contains .csv files for all datasets (<code>DL_Faults_COMMIT_incremental.csv</code>, <code>DL_Faults_ISSUE_incremental.csv</code>, <code>DL_Fault_SO_incremental.csv</code>, <code>DRL_Challenges_incremental.csv</code> and <code>Functional_incremental.csv</code>). All these .csv files contain the following columns: <ul> <li>Link: The link to the instances</li> <li>Prompt: Prompt used as input to GPT-4-Turbo</li> <li>ID: Instance ID</li> <li>FinalTag: Tag assigned by the human in the original paper</li> <li>Chatgpt_output_memory: Output of GPT-4-Turbo with incremental coding</li> <li>Chatgpt_output_memory_clean: (only for the DL Faults datasets) output of GPT-4-Turbo considering only the label assigned, excluding the text</li> <li>Author1: Label assigned by the first author</li> <li>Author2: Label assigned by the second author</li> <li>FinalOutput: Label assigned after the resolution of the conflicts</li> </ul> </li> <li> The sub-directory <code>independent_coding</code> contains .csv files for all datasets (<code>DL_Faults_COMMIT_independent.csv</code>, <code>DL_Faults_ISSUE_ independent.csv</code>, <code>DL_Fault_SO_ independent.csv</code>, <code>DRL_Challenges_ independent.csv</code> and <code>Functional_ independent.csv</code>), containing the following columns: <ul> <li>Link: The link to the instances</li> <li>Prompt: Prompt used as input to GPT-4-Turbo</li> <li>ID: Specific ID for the instance</li> <li>FinalTag: Tag assigned by the human in the original paper</li> <li>Chatgpt_output: Output of GPT-4-Turbo with independent coding</li> <li>Chatgpt_output_clean: (only for DL Faults datasets) output of GPT-4-Turbo considering only the label assigned, excluding the text</li> <li>Author1: Label assigned by the first author</li> <li>Author2: Label assigned by the second author</li> <li>FinalOutput: Label assigned after the resolution of the conflicts.</li> </ul> </li> <li> Also, the sub-directory contains sheets with inconsistencies after resolving conflicts. The directory <code>inconsistency_incremental_coding</code> contains .csv files with the following columns: <ul> <li>Dataset: The dataset considered</li> <li>Human: The label assigned by the human in the original paper</li> <li>Machine: The label assigned by GPT-4-Turbo</li> <li>Classification: The final label assigned by the authors after resolving the conflicts. Multiple classifications for a single instance are separated by a comma “,”</li> <li>Final: final label assigned after the resolution of the incompatibilities</li> </ul> </li> <li> Similarly, the sub-directory <code>inconsistency_independent_coding</code> contains a .csv file with the same columns as before, but this is for the case of independent coding. </li> </ul> </li> <li> <code>manual_analysis_rq2</code>: This directory contains .csv files for all datasets (<code>DL_Faults_redundant_tag.csv</code>, <code>DRL_Challenges_redundant_tag.csv</code>, <code>Functional_redundant_tag.csv</code>) to perform manual analysis for RQ2. <ul> <li> The <code>DL_Faults_redundant_tag.csv</code> file contains the following columns: <ul> <li>Tags Redundant: tags identified as redundant by GPT-4-Turbo</li> <li>Matched: inspection by the authors to see if the tags are redundant matching or not</li> <li>FinalTag: final tag assigned by the authors after the resolution of the conflict</li> </ul> </li> <li> The <code>Functional_redundant_tag.csv</code> file contains the same columns as before </li> <li> The <code>DRL_Challenges_redundant_tag.csv</code> file is organized as follows: <ul> <li>Tags Suggested: The final tag suggested by GPT-4-Turbo</li> <li>Tags Redundant: tags identified as redundant by GPT-4-Turbo</li> <li>Matched: inspection by the authors to see if the tags redundant matching or not with the tags suggested</li> <li>FinalTag: final tag assigned by the authors after the resolution of the conflict</li> </ul> </li> <li> The sub-directory <code>code_consolidation_mapping_overview</code> contains .csv files (<code>DL_Faults_rq2_overview.csv</code>, <code>DRL_Challenges_rq2_overview.csv</code>, <code>Functional_rq2_overview.csv</code>) organized as follows: <ul> <li>Initial_Tags: list of the unique initial tags assigned by GPT-4-Turbo for each dataset</li> <li>Mapped_tags: list of tags mapped by GPT-4-Turbo</li> <li>Unmatched_tags: list of unmatched tags by GPT-4-Turbo</li> <li>Aggregating_tags: list of consolidated tags</li> <li>Final_tags: list of final tags after the consolidation task</li> </ul> </li> </ul> </li> <li> <code>prompt_for_each_rq</code>: This directory contains: - (i) the history of prompts used in each dataset (<code>prompts_history.txt</code>) -(ii) all final prompt used for the analysis of each dataset, prompt used for incremental coding, prompt used in rq2 to consolidate redundant codes, prompt used in rq3 to create taxonomy (<code>generic_prompt.txt</code>) -(iii) all .csv files in which there are indicate, for each dataset, the link and the prompt used (<code>prompt_DL_Faults_COMMIT.csv</code>, <code>prompt_DL_Faults_ISSUE.csv</code>, <code>prompt_DL_Faults_SO.csv</code>, <code>prompt_DRL_Challenges.csv</code>). For the Functional Dataset .csv file contains, instead, Question, Answer and Prompt used (<code>prompt_Functional.csv</code>) </li> <li> <code>rq3</code>: This directory contains the taxonomies obtained from GPT-4-Turbo for the DL Faults and for the DRL Challenges (<code>taxonomy_DL_Faults.txt</code>,<code>taxonomy_DRL_Challenges.txt</code>) </li> </ul> <h2>stats</h2> <ul> <li> <code>RQ1</code>: contains script and datasets used to perform metrics for RQ1. The analysis calculates all possible combinations between Matched, More Abstract, More Specific, and Unmatched. <ul> <li><code>RQ1_Stats.ipynb</code> is a Python Jupyter nooteook to compute the RQ1 metrics. To use it, as explained in the notebook, it is necessary to change the values of variables contained in the first code block.</li> <li><code>independent-prompting</code>: Contains the datasets related to the independent prompting. Each line contains the following fields: <ul> <li>Link: Link to the artifact being tagged</li> <li>Prompt: Prompt sent to GPT-4-Turbo</li> <li>FinalTag: Artifact coding from the replicated study</li> <li>chatgpt_output_text: GPT-4-Turbo output</li> <li>chatgpt_output: Codes parsed from the GPT-4-Turbo output</li> <li>Author1: Annotator 1 evaluation of the coding</li> <li>Author2: Annotator 2 evaluation of the coding</li> <li>FinalOutput: Consolidated evaluation</li> </ul> </li> <li><code>incremental-prompting</code>: Contains the datasets related to the incremental prompting (same format as independent prompting)</li> <li><code>results</code>: contains files for the RQ1 quantitative results. The files are named <code>RQ1\_<<Dataset>>\_<<Prompt method>>\_<<ExcludingNegative>>\_<<MetricAggregation>>.csv</code>, where Dataset is the dataset name, Prompt method indicates whether results are for independent or incremental prompting, Excluding Negatives (for datasets where this applies) whether results have been obtained by excluding negative instances, and MetricAggregation (where it applies) how metrics have been aggregated (macro or weighted average). The files report columns indicating the Dataset, the Matching type, the Accuracy, Precision, Recall, F1 Score, and Cohen's Kappa.</li> </ul> </li> <li> <code>RQ2</code>: contains the script used to perform metrics for RQ2, the datasets it uses, and its output. <ul> <li><code>RQ2_SetStats.ipynb</code> is the Python Jupyter notebook to perform the analyses. The scripts takes as input the following types of files, contained in the directory contains the script used to perform the metrics for RQ2. The script takes in input:</li> <li>RQ1 Data Files (<code>RQ1_DLFaults_Issues.csv</code>, <code>RQ1_DLFaults_Commits.csv</code>, and <code>RQ1_DLFaults_SO.csv</code>, joined in a single .csv <code>RQ1_DLFaults.csv</code>). These are the same files used in RQ1.</li> <li>Mapping Files (<code>RQ2_Mappings_DRL.csv</code>, <code>RQ2_Mappings_Functional.csv</code>, <code>RQ2_Mappings_DLFaults.csv</code>). These contain the mappings between human tags (HumanTags), GPT-4-Turbo tags (Final Tags), with indicated the type of matching (MatchType).</li> <li>Additional codes creating during the consolidation (<code>RQ2_newCodes_DRL.csv</code>, <code>RQ2_newCodes_Functional.csv</code>, <code>RQ2_newCodes_DLFaults.csv</code>), annotated with the matching: new code,old code,human code,match type</li> <li>Set files (<code>RQ2_Sets_DRL.csv</code>, <code>RQ2_Sets_Functional.csv</code>, <code>RQ2_Sets_DLFaults.csv</code>). Each file contains the following columns: <ul> <li>HumanTags: List of tags from the original dataset</li> <li>InitialTags: Set of tags from RQ1,</li> <li>ConsolidatedTags: Tags that have been consolidated,</li> <li>FinalTags: Final set of tags (results of RQ2, used in RQ3)</li> <li>NewTags: New tags created during consolidation</li> </ul> </li> <li><code>RQ2_Set_Metrics.csv</code>: Reports the RQ2 output metrics (Precision, Recall, F1-Score, Jaccard).</li> </ul> </li> </ul>

opencc-zeroDec 2010View details →

Data from: Only helpful when required: a longevity cost of harbouring defensive symbionts

Maternally transmitted symbionts can spread in host populations if they provide a fitness benefit to their hosts. Hamiltonella defensa, a bacterial endosymbiont of aphids, protects hosts against parasitoids but only occurs at moderate frequencies in most aphid populations. This suggests that harbouring this symbiont is also associated with costs, yet the nature of these costs has remained elusive. Here we demonstrate an important and clearly defined cost: reduced longevity. Experimental infections with six different isolates of H. defensa caused strongly reduced lifespans in two different clones of the black bean aphid, Aphis fabae, resulting in a significantly lower lifetime reproduction. However, the two aphid clones were unequally affected by the presence of H. defensa, and the magnitude of the longevity cost was further determined by genotype × genotype interactions between host and symbiont, which has important consequences for their coevolution.

opencc-zeroNov 2019View details →

Odors of non-predatory species help prey moderate their risk assessment

<ol> <li>Prey use contemporary information to update their risk estimation, and accordingly, optimize their anti-predator reactions. Conceptualization of this process is largely focused on information that reflects predator activity. We aimed to complement this unilateral view by testing whether prey also use cues of non-predatory species to update their risk perception. </li> <li>We focused our investigation on the desert isopod Hemilepistus reaumuri that reacts defensively to excavated soil mounds, even in the absence of direct predator cues. We located in the field 18 isopod burrows and surrounded each with six soil mounds. One mound remained odorless, and the other five were supplemented with odors of a major isopod predator, the golden scorpion, and four sympatric species that do not prey on isopods, but excavate soil. </li> <li>Isopods augmented their defensive responses toward mounds supplemented by scorpion odors and lessened their anti-predator reactions toward mounds with odors of herbivore rodents. Isopods' responses to the odors of the two insectivorous species that do not eat isopods were similar to the reaction towards the odorless control mounds. </li> <li>Our results suggest that prey use non-predatory species cues to moderate their risk estimation. Therefore, we need to consider this potentially important source of information in studies of predator-prey interactions. Our findings also indicate cues of non-predatory species may not be adequate as control treatments to predator cues.</li> </ol>

opencc-zeroDec 2016View details →

Data from: Market forces influence helping behaviour in cooperatively breeding paper wasps

Biological market theory is potentially useful for understanding helping behaviour in animal societies. It predicts that competition for trading partners will affect the value of commodities exchanged. It has gained empirical support in cooperative breeders, where subordinates help dominant breeders in exchange for group membership, but so far without considering one crucial aspect: outside options. We find support for the existence of a biological market in paper wasps, Polistes dominula. We first show that females have a choice of cooperative partners. Second, by manipulating entire subpopulations in the field, we increased the supply of outside options for subordinates, freeing up suitable nesting spots and providing additional nesting partners. We predicted that by intensifying competition for help, our manipulation would force dominants to accept a lower price for group membership. As expected, subordinates reduced their foraging effort following our treatments. We conclude that to accurately predict the amount of help provided, social units cannot be viewed in isolation as has traditionally been done: the surrounding market must also be considered.

opencc-zeroJul 2021View details →

Multicellularity and sex helped shape the Tree of Life

Across the Tree of Life, there are dramatic differences in species numbers among groups. However, the factors that explain the differences among the deepest branches have remained unknown. We tested whether multicellularity and sexual reproduction might explain these patterns, since the most species-rich groups share these traits. We found that groups with multicellularity and sexual reproduction have accelerated rates of species proliferation (diversification), and that multicellularity has a stronger effect than sexual reproduction. Patterns of species richness among clades are then strongly related to these differences in diversification rates. Taken together, these results help explain patterns of biodiversity among groups of organisms at the very broadest scales. They may also help explain the mysterious preponderance of sexual reproduction among species (the "paradox of sex") by showing that organisms with sexual reproduction proliferate more rapidly.

opencc-zeroOct 2021View details →

Data from: Weather and topography regulate the benefit of a conditionally helpful parasite

Heat-induced mass mortalities involving ecosystem engineers may have long-lasting detrimental effects at the community level, eliminating the ecosystem services they provide. Intertidal mussels are ecologically and economically valuable with some populations facing unprecedented heat-induced mass mortalities. Critically, mussels are also frequently infested by endolithic parasites that modify shell albedo, hence reducing overheating and mortality rates under heat stress. Using a biophysical model, we explored the topographical and meteorological conditions under which endolithically-driven thermal buffering becomes critical to survival. Based on meteorological data from a global climate analysis, we modelled body temperatures of infested and non-infested mussels over the last decade (2010-2020) at nine sites spread across ca. 20° of latitude. We show that thermal buffering is enhanced where and when heat stress is greatest, that is, on sun-exposed surfaces under high solar radiation and high air temperature. These results suggest that new co-evolutionary pathways are likely to open for these symbiotic organisms as climate continues to change, potentially tipping the balance of the relationship from a parasitic to a more mutualistic one. However, endolithically-driven reductions in body temperatures can also occur at or below optimal temperatures, thereby reducing the host's metabolic rates and making the interplay of positive and negative effects complex. In parallel, we hindcasted body temperatures using empirical data from nearby weather stations and found that predictions were very similar with those obtained from two global climate reanalyses (i.e., NCEP-DOE Reanalysis 2 and ECMWF Reanalysis v5). This result holds great promise for modelling the distribution of terrestrial ectotherms at ecologically relevant spatiotemporal scales, as it suggests we can reasonably bypass the practical issues associated with weather stations. For intertidal ectotherms, however, the challenge will be incorporating body temperatures over the full tidal cycle.