cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/cc104g4r
title: CCDC 927803: Experimental Crystal Structure Determination

description: Related Article: Tong Wu,Yue-Jian Lin,Guo-Xin Jin|2013|RSC Advances|3|11476|doi:10.1039/C3RA42075B
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: 10.5517/cc104g5s
title: CCDC 927804: Experimental Crystal Structure Determination

description: Related Article: Tong Wu,Yue-Jian Lin,Guo-Xin Jin|2013|RSC Advances|3|11476|doi:10.1039/C3RA42075B
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: 10.5517/cc104msk
title: CCDC 927978: Experimental Crystal Structure Determination

description: Related Article: Verena Lingen, Anna Lüning, Christian Strauß, Ingo Pantenburg, Glen B. Deacon, Axel Klein, Gerd Meyer|2013|Eur.J.Inorg.Chem.|2013|4450|doi:10.1002/ejic.201300403
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: 10.5517/cc104mqh
title: CCDC 927976: Experimental Crystal Structure Determination

description: Related Article: Verena Lingen, Anna Lüning, Christian Strauß, Ingo Pantenburg, Glen B. Deacon, Axel Klein, Gerd Meyer|2013|Eur.J.Inorg.Chem.|2013|4450|doi:10.1002/ejic.201300403
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: 10.5517/cc104mrj
title: CCDC 927977: Experimental Crystal Structure Determination

description: Related Article: Verena Lingen, Anna Lüning, Christian Strauß, Ingo Pantenburg, Glen B. Deacon, Axel Klein, Gerd Meyer|2013|Eur.J.Inorg.Chem.|2013|4450|doi:10.1002/ejic.201300403
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: 10.5517/cc104mpg
title: CCDC 927975: Experimental Crystal Structure Determination

description: Related Article: Verena Lingen, Anna Lüning, Christian Strauß, Ingo Pantenburg, Glen B. Deacon, Axel Klein, Gerd Meyer|2013|Eur.J.Inorg.Chem.|2013|4450|doi:10.1002/ejic.201300403
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: 10.5517/cc104mnf
title: CCDC 927974: Experimental Crystal Structure Determination

description: Related Article: Verena Lingen, Anna Lüning, Christian Strauß, Ingo Pantenburg, Glen B. Deacon, Axel Klein, Gerd Meyer|2013|Eur.J.Inorg.Chem.|2013|4450|doi:10.1002/ejic.201300403
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.17309576
title: Multi Transaction Oracle Price Manipulation Data

description: This dataset contains single- and multi-transaction price manipulation attacks in DeFi, along with the logs and traces. The multi-transaction hashes are collected from the incident reported as a price manipulation attack in (Rekt./News). The single transaction hash is from the dataset reported in DeFiScope.
DOI: 10.5281/zenodo.17309577
title: Multi Transaction Oracle Price Manipulation Data

description: This dataset contains single- and multi-transaction price manipulation attacks in DeFi, along with the logs and traces. The multi-transaction hashes are collected from the incident reported as a price manipulation attack in (Rekt./News). The single transaction hash is from the dataset reported in DeFiScope.
DOI: 10.5286/isis.e.rb2590245-1
title: Crystal structure determination of LiTa2-xNbxPO8
DOI: 10.21979/n9/9eib8x
title: Octopus: Embodied Vision-Language Programmer from Environmental Feedback

description: Large vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning. Furthermore, when seamlessly integrated into an embodied agent, it signifies a crucial stride towards the creation of autonomous and context-aware systems capable of formulating plans and executing commands with precision. In this paper, we introduce Octopus, an embodied VLM designed to 1) proficiently decipher an agent's visual and textual task objectives, 2) formulate intricate action sequences, and 3) generate executable code. Our design allows the agent to adeptly handle a wide spectrum of tasks, ranging from mundane daily chores in simulators to sophisticated interactions in complex video games. Octopus is trained by leveraging GPT-4 to control an explorative agent to generate training data, i.e., action blueprints and the corresponding executable code, within our experimental environment called OctoVerse. We also collect the feedback that allows the enhanced training scheme of Reinforcement Learning with Environmental Feedback (RLEF). Through a series of experiments, we illuminate Octopus's functionality and present compelling results, and the proposed RLEF turns out to refine the agent's decision-making. By open-sourcing our model architecture, simulator, and dataset, we aspire to ignite further innovation and foster collaborative applications within the broader embodied AI community.
DOI: 10.21979/n9/imvwt4
title: Replication Data for: MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations

description: Understanding documents with rich layouts and multi-modal components is a long-standing and practical task. Recent Large Vision-Language Models (LVLMs) have made remarkable strides in various tasks, particularly in single-page document understanding (DU). However, their abilities on long-context DU remain an open problem. This work presents MMLongBench-Doc, a long-context, multi-modal benchmark comprising 1,062 expert-annotated questions. Distinct from previous datasets, it is constructed upon 130 lengthy PDF-formatted documents with an average of 49.4 pages and 20,971 textual tokens. Towards comprehensive evaluation, answers to these questions rely on pieces of evidence from (1) different sources (text, image, chart, table, and layout structure) and (2) various locations (i.e. page number). Moreover, 33.2% of the questions are cross-page questions requiring evidence across multiple pages. 22.8% of the questions are designed to be unanswerable for detecting potential hallucinations. Experiments on 14 LVLMs demonstrate that long-context DU greatly challenges current models. Notably, the best-performing model, GPT-4o, achieves an F1 score of only 42.7%, while the second-best, GPT-4V, scores 31.4%. Furthermore, 12 LVLMs (all except GPT-4o and GPT-4V) even present worse performance than their LLM counterparts which are fed with lossy-parsed OCR documents. These results validate the necessity of future research toward more capable long-context LVLMs.
DOI: 10.6084/m9.figshare.30326559
title: Machine learning model predicts clotting risk during CRRT in ESKD patients: a SHAP-interpretable approach

description: Ensuring fluent extracorporeal circulation and preventing circuit clotting are important for end-stage kidney disease (ESKD) patients undergoing continuous renal replacement therapy (CRRT). This study aimed to develop a predictive model using machine learning (ML) algorithms to evaluate clotting risk after initiating CRRT, enhancing treatment safety and effectiveness. This study involved 636 ESKD patients who underwent CRRT. Feature selection was conducted <i>via</i> the least absolute shrinkage and selection operator (LASSO) algorithm. ML algorithms, including support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), gradient boosting machine (GBM), decision tree, and logistic regression (LR), were applied to construct models through tenfold cross-validation. Model performance was assessed <i>via</i> the area under the receiver operating characteristic curve (AUC) and additional metrics. The Shapley additive explanation (SHAP) values quantify each feature’s contribution. This study included 199 patients with blood clots during extracorporeal circulation, corresponding to an incidence rate of 31.3%. The AUC values were 0.864 (SVM), 0.815 (XGBoost), 0.806 (GBM), 0.778 (RF), 0.732 (Decision Tree), and 0.717 (LR). The SVM exhibited the best performance. The initial dose of low-molecular-weight heparin (LMWH) was identified as the most significant factor influencing coagulation. ML serves as a reliable tool for predicting the risk of extracorporeal circuit clotting in ESKD patients undergoing CRRT. The SHAP method elucidates key risk factors, providing a basis for early clinical intervention.
DOI: 10.6084/m9.figshare.30326559.v1
title: Machine learning model predicts clotting risk during CRRT in ESKD patients: a SHAP-interpretable approach

description: Ensuring fluent extracorporeal circulation and preventing circuit clotting are important for end-stage kidney disease (ESKD) patients undergoing continuous renal replacement therapy (CRRT). This study aimed to develop a predictive model using machine learning (ML) algorithms to evaluate clotting risk after initiating CRRT, enhancing treatment safety and effectiveness. This study involved 636 ESKD patients who underwent CRRT. Feature selection was conducted <i>via</i> the least absolute shrinkage and selection operator (LASSO) algorithm. ML algorithms, including support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), gradient boosting machine (GBM), decision tree, and logistic regression (LR), were applied to construct models through tenfold cross-validation. Model performance was assessed <i>via</i> the area under the receiver operating characteristic curve (AUC) and additional metrics. The Shapley additive explanation (SHAP) values quantify each feature’s contribution. This study included 199 patients with blood clots during extracorporeal circulation, corresponding to an incidence rate of 31.3%. The AUC values were 0.864 (SVM), 0.815 (XGBoost), 0.806 (GBM), 0.778 (RF), 0.732 (Decision Tree), and 0.717 (LR). The SVM exhibited the best performance. The initial dose of low-molecular-weight heparin (LMWH) was identified as the most significant factor influencing coagulation. ML serves as a reliable tool for predicting the risk of extracorporeal circuit clotting in ESKD patients undergoing CRRT. The SHAP method elucidates key risk factors, providing a basis for early clinical intervention.
DOI: 10.5281/zenodo.17309673
title: Data set for dissertation chapter 3
DOI: 10.5281/zenodo.17309674
title: Data set for dissertation chapter 3
DOI: 10.6084/m9.figshare.30326566
title: 564yhstrh

description: y54y5w4esyy
DOI: 10.21979/n9/edkwdc
title: Generalizable Implicit Motion Modeling for Video Frame Interpolation

description: Motion modeling is critical in flow-based Video Frame Interpolation (VFI). Existing paradigms either consider linear combinations of bidirectional flows or directly predict bilateral flows for given timestamps without exploring favorable motion priors, thus lacking the capability of effectively modeling spatiotemporal dynamics in real-world videos. To address this limitation, in this study, we introduce Generalizable Implicit Motion Modeling (GIMM), a novel and effective approach to motion modeling for VFI. Specifically, to enable GIMM as an effective motion modeling paradigm, we design a motion encoding pipeline to model spatiotemporal motion latent from bidirectional flows extracted from pre-trained flow estimators, effectively representing input-specific motion priors. Then, we implicitly predict arbitrary-timestep optical flows within two adjacent input frames via an adaptive coordinate-based neural network, with spatiotemporal coordinates and motion latent as inputs. Our GIMM can be smoothly integrated with existing flow-based VFI works without further modifications. We show that GIMM performs better than the current state of the art on the VFI benchmarks.
DOI: 10.6084/m9.figshare.28228949
title: <b>Single-cell analysis of the human retina reveals stage-linked microglial states and neural-immune circuit rewiring in diabetic retinopathy</b>

description: Diabetic retinopathy (DR) is a major cause of vision loss worldwide. Here, we conduct single-cell RNA sequencing of twenty human retina samples (from living and post-mortem donors) across non-diabetic, diabetic, and DR states to create a comprehensive transcriptomic atlas. We identify two stable microglial populations—homeostatic and inflammatory—that exist along a functional continuum, plus a neutrophil cluster within C1QA+ myeloid cells with dynamic transitions occurring throughout disease progression. Module-level analysis reveals divergent transcriptional trajectories: homeostatic microglia maintain energetic programs while selectively upregulating stress elements, whereas inflammatory microglia layer additional pro-inflammatory programs onto preserved biosynthetic foundations. Eleven co-expression modules organize into two major axes: an inflammatory-stress axis, and a regulatory/metabolic-motility axis, with a stable translation module persisting across disease stages. Cell communication analysis further highlights sophisticated neural-immune interactions, particularly between photoreceptors and microglia. Our findings provide insights into the complex cellular dynamics of DR progression and suggest potential therapeutic targets for early intervention.
DOI: 10.6084/m9.figshare.28228949.v2
title: <b>Single-cell analysis of the human retina reveals stage-linked microglial states and neural-immune circuit rewiring in diabetic retinopathy</b>

description: Diabetic retinopathy (DR) is a major cause of vision loss worldwide. Here, we conduct single-cell RNA sequencing of twenty human retina samples (from living and post-mortem donors) across non-diabetic, diabetic, and DR states to create a comprehensive transcriptomic atlas. We identify two stable microglial populations—homeostatic and inflammatory—that exist along a functional continuum, plus a neutrophil cluster within C1QA+ myeloid cells with dynamic transitions occurring throughout disease progression. Module-level analysis reveals divergent transcriptional trajectories: homeostatic microglia maintain energetic programs while selectively upregulating stress elements, whereas inflammatory microglia layer additional pro-inflammatory programs onto preserved biosynthetic foundations. Eleven co-expression modules organize into two major axes: an inflammatory-stress axis, and a regulatory/metabolic-motility axis, with a stable translation module persisting across disease stages. Cell communication analysis further highlights sophisticated neural-immune interactions, particularly between photoreceptors and microglia. Our findings provide insights into the complex cellular dynamics of DR progression and suggest potential therapeutic targets for early intervention.
DOI: 10.6084/m9.figshare.28228949.v1
title: <b>Single-Cell Transcriptomics Reveals Dynamic Microglial States and Neural-Immune Networks in Human Diabetic Retinopathy</b>

description: Diabetic retinopathy (DR) is a major cause of vision loss worldwide. Here, we perform single-cell RNA sequencing of thirteen human retina samples (from living and post-mortem donors) across non-diabetic, diabetic, and DR states to create a comprehensive transcriptomic atlas. We uncover three distinct microglial states—homeostatic, stress-response, and inflammatory—along a functional continuum, rather than discrete activation states, with dynamic transitions occurring throughout disease progression. Trajectory analysis indicates bifurcating paths starting from the homeostatic state branching toward stress-response and inflammatory states. Three major functional modules: ribosomal/translation, coordinated immune cell function, and inflammatory/transcriptional regulation, showing disease-specific activation patterns were identified. Cell communication analysis further highlights sophisticated neural-immune interactions, particularly between photoreceptors and microglia. Our findings provide insights into the complex cellular dynamics of DR progression and suggest potential therapeutic targets for early intervention.
DOI: 10.5517/ccdc.csd.ccvrb2z
title: CCDC 796981: Experimental Crystal Structure Determination

description: Related Article: Sumit Kumar Panja, Nitin Srivastava, Jyoti Srivastava, Namburi Eswara Prasad, Hemanth Noothalapati, Shinsuke Shigeto, Satyen Saha|2018|Spectrochim.Acta,Part A|194|117|doi:10.1016/j.saa.2017.12.048
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: 10.5517/ccs6z64
title: CCDC 721686: Experimental Crystal Structure Determination

description: Related Article: Elena V. Fedorenko, Anatolii G. Mirochnik, Anton Yu. Beloliptsev|2018|J.Lumin.|196|316|doi:10.1016/j.jlumin.2017.12.071
description: Related Article: B.V.Bukvetsky, E.V.Fedorenko, A.G.Mirochnik, A.Yu.Beloliptsev|2010|Zh.Strukt.Khim.|51|563|
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: 10.5517/ccspgfb
title: CCDC 735612: Experimental Crystal Structure Determination

description: Related Article: Elena V. Fedorenko, Anatolii G. Mirochnik, Anton Yu. Beloliptsev|2018|J.Lumin.|196|316|doi:10.1016/j.jlumin.2017.12.071
description: Related Article: B.V.Bukvetsky, E.V.Fedorenko, A.G.Mirochnik|2010|Zh.Strukt.Khim.|51|812|
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: 10.5517/cct788k
title: CCDC 751758: Experimental Crystal Structure Determination

description: Related Article: Elena V. Fedorenko, Anatolii G. Mirochnik, Anton Yu. Beloliptsev|2018|J.Lumin.|196|316|doi:10.1016/j.jlumin.2017.12.071
description: Related Article: B.V.Bukvetsky, E.V.Fedorenko, A.G.Mirochnik|2011|Zh.Strukt.Khim.|52|223|
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: 10.5517/ccdc.csd.cczlp6f
title: CCDC 911685: Experimental Crystal Structure Determination

description: Related Article: Yu-Ping Zhang, De-Yu Hu, Hu-Rong Ling, Lei Zhong, An-Xiang Huang, Kan-Kan Zhang, Bao-An Song|2014|J.Agric.Food Chem.|62|9066|doi:10.1021/jf5018803
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: 10.5517/ccdc.csd.cczv5ts
title: CCDC 918896: Experimental Crystal Structure Determination

description: Related Article: Chandan Singh, A. P. Prakasham, Manoj Kumar Gangwar, Raymond J. Butcher, and Prasenjit Ghosh|2018|ACS Omega|3|1740|doi:10.1021/acsomega.7b01974
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: 10.5517/ccdc.csd.cczxgjv
title: CCDC 921088: Experimental Crystal Structure Determination

description: Related Article: Yu-Ping Zhang, De-Yu Hu, Hu-Rong Ling, Lei Zhong, An-Xiang Huang, Kan-Kan Zhang, Bao-An Song|2014|J.Agric.Food Chem.|62|9066|doi:10.1021/jf5018803
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: 10.5517/cczyng0
title: CCDC 922233: Experimental Crystal Structure Determination

description: Related Article: Larissa Bergmann, Jana Friedrichs, Mathias Mydlak, Thomas Baumann, Martin Nieger, Stefan Bräse|2013|Chem.Commun.|49|6507|doi:10.1039/C3CC42280A
description: Related Article: Larissa Bergmann, Carolin Braun, Martin Nieger, Stefan Bräse|2018|Dalton Trans.|47|608|doi:10.1039/C7DT03682E
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: 10.5517/cczynfz
title: CCDC 922232: Experimental Crystal Structure Determination

description: Related Article: Larissa Bergmann, Jana Friedrichs, Mathias Mydlak, Thomas Baumann, Martin Nieger, Stefan Bräse|2013|Chem.Commun.|49|6507|doi:10.1039/C3CC42280A
description: Related Article: Larissa Bergmann, Carolin Braun, Martin Nieger, Stefan Bräse|2018|Dalton Trans.|47|608|doi:10.1039/C7DT03682E
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.