cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/7725svpmy4.1
title: Deconstructed beetles: green composite materials with potential for passive cooling due to high near-infrared reflectivity -  Data and Code

description: This data allowed us to describe the optical properties and underlying structures that produce very high near-infrared reflectance in three species of green scarab beetles Xylonichus eucalypti, Anoplognathus prasinus and Paraschizognathus olivaceus. Although the chiral structures in scarabs have been well described in previous literature, we have limited knowledge of the natural mechanisms to manipulate near-infrared light (NIR; 700 – 1700 nm). In addition, previous studies in light manipulation by the beetle elytra have been focused mainly on the cuticle. Thus, our aim was to explore if other elements in the elytra can contribute to their characteristic visible and NIR reflectance. 

This dataset contains different types of data including reflectance and transmittance spectra, morphological data of the shape and dimension of the nanostructures that reflect light in the beetle elytron,  and results of computational models simulating different scenarios to understand how these structures interact with light. Thus, we are attaching a readme file inside the folder "Data" explaining the data collection and usage for each file. 

Our Highlights: 
- The studied beetle species have similar high NIR reflectance and green appearance. 
- The elytra of these species contains bilayered composite materials, where a structural component reflects light and an overlaying thin layer filters only the reflectance in the visible spectrum. We showed that the transmittance of the outermost component of the composite materials is similar in the three species.
- We characterized the shape of the nanostructures that produce broadband reflectance in Xylonichus eucalypti.
- We compared different simulated scenarios where the nanostructures varied in separation and size and were illuminated with different polarizations and angle of incidence.
DOI: 10.17632/wpdygfg4zv.3
title: EBSD Data of Nanoindentation Maps around Chloride-induced Stress Corrosion Cracks in Austenitic Stainless Steel 304L Welds

description: This dataset presents SEM-EBSD data collected on nanoindentation maps around chloride-induced stress corrosion cracks (CISCC) in a gas tungsten arc welded (GTAW) austenitic stainless steel (AuSS) 304L.  The experimental setup involved a welded stainless steel 304L coupon of dimensions 105 mm × 18.5 mm × 3 mm, loaded in a 4-point bending fixture with a maximum tensile stress of 380 MPa. The fixtured specimen was immersed in boiling magnesium chloride (MgCl2) solution until a through-crack was observed on the specimen surface after 17 hours of boiling. The cross-section was subsequently polished, and 5 cracks of interest in the heat affected zone (HAZ) were selected for nanoindentation and SEM-EBSD characterization. 

Nanoindentation testing was carried out on the cross-section of the specimen with a KLA iMicro Nanoindenter under displacement-control mode. The nanoindenter utilized a diamond Berkovich tip with an elastic modulus of 1141 GPa. Nanoindentation grits of 1000 nm depth and 50 µm spacing were applied around 5 CISCC cracks (Crack #1 - #5) in the HAZ. Nanohardness and modulus were calculated based on the Oliver-Pharr method. Nanoblitz is performed on 3 additional crack zones (Crack #6 - #8). 

Scanning electron microscopy (SEM) based electron backscatter diffraction (EBSD) was used to map the grain structure around the nanoindentation maps. The obtained orientation imaging microscopy (OIM) datasets were post-processed using EDAX OIM V8 proprietary software to generate inverse pole figures (IPF), image quality (IQ) figures, detector signal (SEM) images. This dataset can be used to understand CISCC crack initiation, propagation, and termination behaviors, as has been reported in the accompanying original research article. This dataset provides the raw EBSD OIM datasets and processed images formatted for accessibility in future studies.
DOI: 10.17632/rjjjsv8krf.2
title: 2021_DT_Ae(Tptm)

description: Research data support for Group 2 Tptm work, including NMR, IR, CHN and XRD analyses.
DOI: 10.17632/377b4ff8p6.1
title: Auditory- and visual-evoked activity in newly implanted adult CI recipients

description: Data reported in Shader et al., (2022) Contralateral dominance to speech in the adult auditory cortex immediately after cochlear implantation, iScience
DOI: 10.17632/nvxy6bd4hm.1
title: Satellite-based phytoplankton diagnostic pigment maps for the northern Gulf of Mexico. 2003-2018

description: Satellite-derived daily maps, multi-day composites and climatologies of four diagnostic pigments that serve as biomarkers for different phytoplankton types (fucoxanthin, zeaxanthin, 19'-butanoyloxyfucoxanthin and 19'-hexanoyloxyfucoxanthin). The data contain proportions of total diagnostic pigments (according to Vidussi et al., 2001) for the northern Gulf of Mexico 2003-2018 at 4km spatial resolution, and were created using random forests with various satellite data products as inputs. Linear correlations between predicted and observed values obtained by spatial leave-one-out cross-validation, when averaged over separation distances between training and test observations from 0.1km to 200km,  were: 0.85 (fuco), 0.72 (zea), 0.74 (but-fuco), and 0.69 (hex-fuco). Mean absolute errors estimated in the same way were 0.13 (fuco), 0.11 (zea), 0.02 (but.fuco), and 0.07 (hex.fuco). All data are provided in GeoTIF format. Dates in the filename for 8-day composites refer to the first of the 8 covered days. The folder hex_pan includes geotifs for 19'-hexanoyloxyfucoxanthin created with an alternative, polynomial algorithm broadly following Pan et al. (2010, 2013) for comparison purposes.

Details are provided in a research article under review that will be linked to once published.
DOI: 10.17632/2vsj7wr7wz.1
title: Stability of all 3- and 4-node interaction networks

description: We invite you to browse the data here: https://oceanchaos.shinyapps.io/motif/
Protein-protein interaction networks (PPIs) are large and complex, yet self-organize to sustain all known life. To help understand the building blocks of these systems, we analyze small subgraphs (called "motifs" in this study) where all proteins can interact positively, negatively or not at all. There are 132 such 3-protein networks that are topologically unique (A->B->C is the same as B->C->A and C->B->A and B->A->C etc, so only such network is simulated), and 22,662 4-protein networks. Using the model d[A]/dt = (synthesis rate) - (degradation rate)*[A] + kBA*[B]*[A]^(Hill coefficient) + kCA*[C]*[A]^(Hill coefficient) + ..., we choose 1,000 biologically plausible values for the parameters, find all fixed points of the system, and determine the stability of those points. Previous research had suggested that a switch from a stable fixed-point equilibrium, to a limit cycle, to divergence to infinity was to be expected as the three possible behaviors. Instead, we determined that the behavior space of the typical motif included many different numbers of fixed points in complex combinations of stable and unstable (a median of 6 and 12 distinct fixed point combinations). We also discover that divergence to infinity is not at all uncommon at known biological values, despite such a result likely being fatal to the cell, suggesting a substantial degree of selection and regulation.

The zip file includes:
- Lists of which motifs are isomorphic to each other (3-motifs: isomorph_list.txt; 4-motifs: isomorph_list4.txt)
- Julia code for generating our results for 3- and 4-motifs (farm3_log.jl and farm4_log.jl respectively). These were run on a supercomputer cluster for ~25k hours of CPU time to get 1,000 results for one member of each isomorph group. The results are stored in:
- stab[n]_[motif id]_pars.txt files, storing the parameter values of all simulations for n-motifs
- stab[n]_[motif id]_spec.txt files, which provide the spectral radius of the system's Jacobian at each fixed point. (-999 means no fixed points were found at the parameter values)
- stab[n]_[motif id]_aux.txt files, in the format [time to solve the system] [n numbers representing log-protein concentrations at the first fixed point] [the second fixed point] ...
Note that while these results are informed by biochemical networks, they are very general and would apply to any network.
DOI: 10.25442/hku.20140934
title: Data from 'Seasonal patterns of macroalgal and sessile invertebrate communities'

description: Seasonal patterns of macroalgal and sessile invertebrate communities in eastern Hong Kong. <br> Data includes a full list of macroalgae and invertebrates, and seasonal changes in their abundances/biomass over multiple cites in eastern Hong Kong. <br> The data has been used to understand seasonal trends in subtidal algae distribution. <br> The data was collected by surveying subtidal sites at 1 m and 5 m depth using transect lines and quadrats through winter 2018, summer 2019 and winter 2019. The abstract of the study, now in press in Estuarine, Coastal and Shelf Science, is provided below: <br> influence seasonal abundance of organisms and their assemblage patterns. With climate change altering phenology and distribution of species globally, establishing baseline biological patterns allows us to understand trends and challenges local communities might face in adverse conditions. We surveyed the seasonal biomass and cover of subtidal macroalgal and sessile invertebrate communities at 12 rocky subtidal habitats in Hong Kong to establish the first baseline for these communities. Overall productivity and composition of shallow (∼1 m depth) assemblages were prone to dynamic influences from environmental drivers and exhibited seasonal patterns, while those of deeper (∼5 m) communities had less biomass but were stable across seasons. Abundance of fleshy macroalgae was greatest during winter, dominated by canopy forming Sargassum hemiphyllum, low-laying turfs, and encrusting algae. S. hemiphyllum had the greatest seasonal productivity on shallow substrate, reaching up to 33.8 kg m−2 in winter. In contrast, macroalgae were almost absent in the hot and rainy season, with encrusting algae defining benthic communities. Sessile invertebrate communities were generally dominated by oysters, barnacles, and live corals at both depths, characterized by a shift towards oyster-dominant during summer in the shallow. With the temperature of coastal waters increasing under climate change and winter algal growing seasons shortening, this study forms a baseline from which to assess any changes in algal productivity and community composition in the future. <br>
DOI: 10.25442/hku.20140934.v1
title: Data from 'Seasonal patterns of macroalgal and sessile invertebrate communities'

description: Seasonal patterns of macroalgal and sessile invertebrate communities in eastern Hong Kong. <br> Data includes a full list of macroalgae and invertebrates, and seasonal changes in their abundances/biomass over multiple cites in eastern Hong Kong. <br> The data has been used to understand seasonal trends in subtidal algae distribution. <br> The data was collected by surveying subtidal sites at 1 m and 5 m depth using transect lines and quadrats through winter 2018, summer 2019 and winter 2019. The abstract of the study, now in press in Estuarine, Coastal and Shelf Science, is provided below: <br> influence seasonal abundance of organisms and their assemblage patterns. With climate change altering phenology and distribution of species globally, establishing baseline biological patterns allows us to understand trends and challenges local communities might face in adverse conditions. We surveyed the seasonal biomass and cover of subtidal macroalgal and sessile invertebrate communities at 12 rocky subtidal habitats in Hong Kong to establish the first baseline for these communities. Overall productivity and composition of shallow (∼1 m depth) assemblages were prone to dynamic influences from environmental drivers and exhibited seasonal patterns, while those of deeper (∼5 m) communities had less biomass but were stable across seasons. Abundance of fleshy macroalgae was greatest during winter, dominated by canopy forming Sargassum hemiphyllum, low-laying turfs, and encrusting algae. S. hemiphyllum had the greatest seasonal productivity on shallow substrate, reaching up to 33.8 kg m−2 in winter. In contrast, macroalgae were almost absent in the hot and rainy season, with encrusting algae defining benthic communities. Sessile invertebrate communities were generally dominated by oysters, barnacles, and live corals at both depths, characterized by a shift towards oyster-dominant during summer in the shallow. With the temperature of coastal waters increasing under climate change and winter algal growing seasons shortening, this study forms a baseline from which to assess any changes in algal productivity and community composition in the future. <br>
DOI: 10.5526/erdr-00000153
title: Is all seagrass habitat equal? Seasonal, spatial, and interspecific variation in productivity dynamics within Mediterranean seagrass habitat (DATA)
DOI: 10.5281/zenodo.6693792
title: MineDojo Internet Knowledge Base (YouTube)

description: <strong>Project website:</strong> minedojo.org <strong>Paper:</strong> arxiv.org/abs/2206.08853 <strong>GitHub:</strong> github.com/MineDojo/MineDojo Minecraft is among the most streamed games on YouTube. Human players have demonstrated a stunning range of creative activities and sophisticated missions that take hours to complete. We collect 730K+ narrated Minecraft videos, which add up to <strong>33 years of duration and 2.2B words</strong> in English transcripts. The time-aligned transcripts enable the agent to ground free-form natural language in video pixels and learn the semantics of diverse activities without laborious human labeling. There are two files in our YouTube knowledge base. <strong>youtube_tutorial.json</strong> (tutorial videos): Minecraft tutorial videos include step-by-step demonstrations and sometimes detailed verbal explanations. They also serve as a rich source of creative missions that humans find interesting. We harvest thousands of tasks from these videos in our benchmarking suite. <strong>youtube_full.json</strong> (general gameplay videos): Unlike tutorials, general gameplay videos do not necessarily provide guidance on particular tasks. Instead, they capture the “in-the-wild” human experiences that are much larger in quantity, diverse in contents, and rich in learning signals. Data Structure <pre><code class="language-python">list[ { "id": str, # video id "title": str, # video title "link": str, # video link "view_count": int # number of times the video has been viewed "like_count": int # number of users who have indicated that they liked the video "duration": float # video duration in seconds "fps": float, # video FPS } ]</code></pre> Check out our paper! <pre><code class="language-markdown">@article{fan2022minedojo, title = {MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge}, author = {Linxi Fan and Guanzhi Wang and Yunfan Jiang and Ajay Mandlekar and Yuncong Yang and Haoyi Zhu and Andrew Tang and De-An Huang and Yuke Zhu and Anima Anandkumar}, year = {2022}, journal = {arXiv preprint arXiv: Arxiv-2206.08853} }</code></pre>
DOI: 10.5281/zenodo.6641142
title: MineDojo Internet Knowledge Base (YouTube)

description: <strong>Project website:</strong> minedojo.org <strong>Paper:</strong> arxiv.org/abs/2206.08853 <strong>GitHub:</strong> github.com/MineDojo/MineDojo Minecraft is among the most streamed games on YouTube. Human players have demonstrated a stunning range of creative activities and sophisticated missions that take hours to complete. We collect 730K+ narrated Minecraft videos, which add up to <strong>33 years of duration and 2.2B words</strong> in English transcripts. The time-aligned transcripts enable the agent to ground free-form natural language in video pixels and learn the semantics of diverse activities without laborious human labeling. There are two files in our YouTube knowledge base. <strong>youtube_tutorial.json</strong> (tutorial videos): Minecraft tutorial videos include step-by-step demonstrations and sometimes detailed verbal explanations. They also serve as a rich source of creative missions that humans find interesting. We harvest thousands of tasks from these videos in our benchmarking suite. <strong>youtube_full.json</strong> (general gameplay videos): Unlike tutorials, general gameplay videos do not necessarily provide guidance on particular tasks. Instead, they capture the “in-the-wild” human experiences that are much larger in quantity, diverse in contents, and rich in learning signals. Data Structure <pre><code class="language-python">list[ { "id": str, # video id "title": str, # video title "link": str, # video link "view_count": int # number of times the video has been viewed "like_count": int # number of users who have indicated that they liked the video "duration": float # video duration in seconds "fps": float, # video FPS } ]</code></pre> Check out our paper! <pre><code class="language-markdown">@article{fan2022minedojo, title = {MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge}, author = {Linxi Fan and Guanzhi Wang and Yunfan Jiang and Ajay Mandlekar and Yuncong Yang and Haoyi Zhu and Andrew Tang and De-An Huang and Yuke Zhu and Anima Anandkumar}, year = {2022}, journal = {arXiv preprint arXiv: Arxiv-2206.08853} }</code></pre>
DOI: 10.6084/m9.figshare.20141057
title: excell spreadshet SPSS Coding.xlsx

description: This is an excell spreadshet for SPSS file explanation.
DOI: 10.5281/zenodo.6693745
title: MineDojo Internet Knowledge Base (Wiki)

description: <strong>Project website:</strong> minedojo.org <strong>Paper:</strong> arxiv.org/abs/2206.08853 <strong>GitHub:</strong> github.com/MineDojo/MineDojo The Minecraft Wiki pages cover almost every aspect of the game mechanics, and supply a rich source of unstructured knowledge in multimodal tables, recipes, illustrations, and step-by-step tutorials. <strong>We scrape 6,735 pages that interleave text, images, tables, and diagrams.</strong> To preserve the layout information, we also save the screenshots of entire pages and extract bounding boxes of the visual elements. There are two files in our Wiki knowledge base. <strong>wiki_samples.zip:</strong> A sample version of the full knowledge base (10 pages). <strong>wiki_full.zip:</strong> The full knowledge base (6,735 pages). Check out our paper! <pre><code class="language-markdown">@article{fan2022minedojo, title = {MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge}, author = {Linxi Fan and Guanzhi Wang and Yunfan Jiang and Ajay Mandlekar and Yuncong Yang and Haoyi Zhu and Andrew Tang and De-An Huang and Yuke Zhu and Anima Anandkumar}, year = {2022}, journal = {arXiv preprint arXiv: Arxiv-2206.08853} }</code></pre>
DOI: 10.5281/zenodo.6640448
title: MineDojo Internet Knowledge Base (Wiki)

description: <strong>Project website:</strong> minedojo.org <strong>Paper:</strong> arxiv.org/abs/2206.08853 <strong>GitHub:</strong> github.com/MineDojo/MineDojo The Minecraft Wiki pages cover almost every aspect of the game mechanics, and supply a rich source of unstructured knowledge in multimodal tables, recipes, illustrations, and step-by-step tutorials. <strong>We scrape 6,735 pages that interleave text, images, tables, and diagrams.</strong> To preserve the layout information, we also save the screenshots of entire pages and extract bounding boxes of the visual elements. There are two files in our Wiki knowledge base. <strong>wiki_samples.zip:</strong> A sample version of the full knowledge base (10 pages). <strong>wiki_full.zip:</strong> The full knowledge base (6,735 pages). Check out our paper! <pre><code class="language-markdown">@article{fan2022minedojo, title = {MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge}, author = {Linxi Fan and Guanzhi Wang and Yunfan Jiang and Ajay Mandlekar and Yuncong Yang and Haoyi Zhu and Andrew Tang and De-An Huang and Yuke Zhu and Anima Anandkumar}, year = {2022}, journal = {arXiv preprint arXiv: Arxiv-2206.08853} }</code></pre>
DOI: 10.5281/zenodo.6693238
title: MineDojo Internet Knowledge Base (Reddit)

description: <strong>Project website:</strong> minedojo.org <strong>Paper:</strong> arxiv.org/abs/2206.08853 <strong>GitHub:</strong> github.com/MineDojo/MineDojo <strong>We collect 340K+ Reddit posts along with 6.6M comments under the “r/Minecraft” subreddit.</strong> These posts ask questions on how to solve certain tasks, showcase cool architectures and achievements in image/video snippets, and discuss general tips and tricks for players of all expertise levels. Large language models can be finetuned on our Reddit corpus to internalize Minecraft-specific concepts and develop sophisticated strategies. Check out our paper! <pre><code class="language-markdown">@article{fan2022minedojo, title = {MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge}, author = {Linxi Fan and Guanzhi Wang and Yunfan Jiang and Ajay Mandlekar and Yuncong Yang and Haoyi Zhu and Andrew Tang and De-An Huang and Yuke Zhu and Anima Anandkumar}, year = {2022}, journal = {arXiv preprint arXiv: Arxiv-2206.08853} }</code></pre>
DOI: 10.5281/zenodo.6641114
title: MineDojo Internet Knowledge Base (Reddit)

description: <strong>Project website:</strong> minedojo.org <strong>Paper:</strong> arxiv.org/abs/2206.08853 <strong>GitHub:</strong> github.com/MineDojo/MineDojo <strong>We collect 340K+ Reddit posts along with 6.6M comments under the “r/Minecraft” subreddit.</strong> These posts ask questions on how to solve certain tasks, showcase cool architectures and achievements in image/video snippets, and discuss general tips and tricks for players of all expertise levels. Large language models can be finetuned on our Reddit corpus to internalize Minecraft-specific concepts and develop sophisticated strategies. Check out our paper! <pre><code class="language-markdown">@article{fan2022minedojo, title = {MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge}, author = {Linxi Fan and Guanzhi Wang and Yunfan Jiang and Ajay Mandlekar and Yuncong Yang and Haoyi Zhu and Andrew Tang and De-An Huang and Yuke Zhu and Anima Anandkumar}, year = {2022}, journal = {arXiv preprint arXiv: Arxiv-2206.08853} }</code></pre>
DOI: 10.57680/asep.0534139
title: Anchored phylogenomics data used for phylogenomic analysis of Arvicanthini rodents

description: The files represent datasets used in the paper: Mikula et al.: Nuclear phylogenomics, but not mitogenomics, resolves the most successful Late Miocene radiation of African mammals (Rodentia: Muridae: Arvicanthini). Molecular Phylogenetics and Evolution, under review. Arvicanthini_40sp_377loci.nex = alignments of 377 nuclear loci obtained by anchored phylogenomic approach in 40 species of Arvicanthini rodents (partitioned nexus format) Arvicanthini_40sp_377loci_genetrees.nwk = Bayesian gene trees (for 377 genes) used as input for the ASTRAL analysis (newick format).
DOI: 10.5517/ccdc.csd.cc2c6knt
title: CCDC 2181056: Experimental Crystal Structure Determination

description: Related Article: Alexander Bechtold, Michael Bolte, Matthias Wagner|2022|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.6719473
title: Leaf miners and gallicolous aphids per tree per species

description: The source data on leaf miners and gallicolous aphids per tree per species.
DOI: 10.5281/zenodo.6719472
title: Leaf miners and gallicolous aphids per tree per species

description: The source data on leaf miners and gallicolous aphids per tree per species.