cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2dpy57
title: CCDC 2225619: Experimental Crystal Structure Determination

description: Related Article: Xin-Wei Meng, Tao Ding, Bin Liu, Xue-Song Gong, Bo Liu, Li-Na Zheng|2023|CrystEngComm|25|2313|doi:10.1039/D3CE00068K
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.cc2ggvrk
title: CCDC 2278399: Experimental Crystal Structure Determination

description: Related Article: Shan Zhu, Qicheng Wang, Xiaomei Wang, Jiajun Pan, Tao Yang, Xinhui Zhou, Hongping Xiao, Yujian You|2023|Inorg.Chem.|62|16589|doi:10.1021/acs.inorgchem.3c02552
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.cc2gm80d
title: CCDC 2282623: Experimental Crystal Structure Determination

description: Related Article: Gurkiran Kaur, Nathaniel L. Dominique, Gaohe Hu, Phattananawee Nalaoh, Rebekah L. Thimes, Shelby L. Strausser, Lasse Jensen, Jon P. Camden, David M. Jenkins|2023|Inorg.Chem.Front.|10|6282|doi:10.1039/D3QI01541F
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.cc2gm82g
title: CCDC 2282625: Experimental Crystal Structure Determination

description: Related Article: Gurkiran Kaur, Nathaniel L. Dominique, Gaohe Hu, Phattananawee Nalaoh, Rebekah L. Thimes, Shelby L. Strausser, Lasse Jensen, Jon P. Camden, David M. Jenkins|2023|Inorg.Chem.Front.|10|6282|doi:10.1039/D3QI01541F
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.cc2gm8ds
title: CCDC 2282635: Experimental Crystal Structure Determination

description: Related Article: Zhifei Yan, Kristopher G. Reynolds, Rui Sun, Yongjin Shin, Agnes E. Thorarinsdottir, Miguel I. Gonzalez, Bryan Kudisch, Giulia Galli, Daniel G. Nocera|2023|J.Am.Chem.Soc.|145|22213|doi:10.1021/jacs.3c08144
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.cc2gn4mx
title: CCDC 2283479: Experimental Crystal Structure Determination

description: Related Article: Dmitry Uborsky, Mikhail Sharikov, Georgy Goryunov, Kristina Li, Anna Dall'Anese, Cristiano Zuccaccia, Antonio Vittoria, Teresa Iovine, Gianluigi Galasso, Christian Ehm, Alceo Macchioni, Vincenzo Busico, Alexander Voskoboynikov, Roberta Cipullo|2023|Inorg.Chem.Front.|10|6401|doi:10.1039/D3QI01537H
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.cc2gnzx1
title: CCDC 2284294: Experimental Crystal Structure Determination

description: Related Article: Ai-Ai Yang, Jian-Peng Cui, Yu Liu, Xiao-Sa Zhang, Ze-Bang Sun, Nan Luo, Wen-Ze Li, Jian Luan|2023|Dalton Trans.|52|14220|doi:10.1039/D3DT02749J
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.cc2gpp2y
title: CCDC 2284950: Experimental Crystal Structure Determination

description: Related Article: John Brown, Juan Forero-Saboya, Benoît Baptiste, Martin Karlsmo, Gwenaëlle Rousse, Alexis Grimaud|2023|Chem.Commun.|59|12266|doi:10.1039/D3CC03769J
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.cc2gsqnn
title: CCDC 2287882: Experimental Crystal Structure Determination

description: Related Article: Chong-Lin Zhu, Chong-Dao Lu|2023|Org.Chem.Front.|10|5490|doi:10.1039/D3QO01381B
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.cc2gsqpp
title: CCDC 2287883: Experimental Crystal Structure Determination

description: Related Article: Chong-Lin Zhu, Chong-Dao Lu|2023|Org.Chem.Front.|10|5490|doi:10.1039/D3QO01381B
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: 10.6084/m9.figshare.24173751
title: Cardiorenal care coordination: holistic patient care opportunities in the primary care setting for patients with chronic kidney disease and atherosclerotic cardiovascular disease

description: Chronic kidney disease (CKD) and atherosclerotic cardiovascular disease (ASCVD) are closely linked conditions, and the presence of each condition promotes incidence and progression of the other. In this study, we sought to better understand the medical journey of patients with CKD and ASCVD and to uncover patients’ and healthcare providers’ (HCPs) perceptions and attitudes toward CKD and ASCVD diagnosis, treatment, and care coordination. Cross-sectional, US-population-based online surveys were conducted between May 18, 2021, and June 17, 2021, among 239 HCPs (70 of whom were primary care physicians, or PCPs) and 195 patients with CKD and ASCVD. PCPs reported personally diagnosing CKD in 78% and ASVD in 64% of their patients, respectively. PCPs reported they are more likely to serve as the overall coordinator of their patient’s care (89%), while slightly more than half of PCPs self-identified as a patient’s coordinator of care specifically for CKD (54%) or ASCVD (59%). In contrast, patients viewed their PCP as their coordinator of care for CKD (25%) or ASCVD (9%). PCPs who personally treated patients with CKD and ASCVD most often recalled primarily prescribing or recommending pharmacologic treatments for CKD and ASCVD; however, patients reported that lifestyle modification was the most common treatment modality they had ever used to manage CKD and ASCVD. CKD and ASCVD are interrelated cardiometabolic conditions with underlying risk factors that can be managed in a primary care setting. However, few patients in our study considered their PCP to be the coordinator of their care for CKD or ASCVD. PCPs can and should take a more active role in educating patients and coordinating care for those with CKD and ASCVD. Chronic kidney disease (CKD) is a medical condition where the kidneys are damaged, and their function is reduced. CKD is often linked to other health problems. Atherosclerotic cardiovascular disease (ASCVD) is a condition where cholesterol builds up in the arteries, leading to reduced blood flow and heart issues. This study wanted to understand what patients and healthcare providers (HCPs) know about these two conditions and how they are managed. We sent questionnaires to 195 patients with CKD and ASCVD as well as 239 HCPs who treat patients with CKD and ASCVD. The results showed primary care physicians (PCPs) are the main healthcare providers for most patients, but specialists are often involved in managing CKD and ASCVD. PCPs play a crucial role in helping patients understand how other health care conditions can impact their risk for CKD and ASCVD. PCPs can also guide patients on making lifestyle changes to lower their risk of these diseases and can refer patients to specialists, while still providing guidance on management of these conditions.
DOI: 10.5066/p9a0f57k
title: Socioeconomic and water needs indicators along the Cambodia and Vietnam border, within the Mekong River Delta

description: This data release documents socioeconomic and water demand indicators for the Mekong River delta region. The Mekong River provides many valuable benefits to the 60 million Southeast Asian residents living in its Basin. Maintaining adequate supplies of water in the Mekong River Basin is vital for food security. However, the Mekong River Basin is increasingly stressed by infrastructure development, growing water demands, and land cover change, in addition to climate change. Freshwater demands and the capacity to respond to changing resource availability were estimated to assess vulnerability to changes in water availability in provinces of the Mekong River delta region along the Cambodia-Vietnam border. Water demands related to agriculture, livestock, and domestic uses were estimated to understand risk of water scarcity in relation to agricultural livelihoods. Following an existing adaptive capacity framework, estimates of asset base, knowledge and learning, flexibility, social organization, and agency were calculated at the province level.
DOI: 10.25739/t8yx-xm08
title: Carolyn_Lawrence_Dill_Maize_WiDiv_Association_Studies_Dataset_September_2023

description: This dataset was generated using spoken phenotypic data (DOI 10.25739/pvx4-5j31), genotypic data (DOI 10.6084/m9.figshare.19175888.v1; DOI 10.1093/gigascience/giac080), and Gene Ontology annotations (DOI 10.7946/P2M925). This analysis includes Genome-Wide Association Studies using the Wisconsin Diversity panel. The data include GWAS analysis code and visualizations performed on an HPC and locally.
DOI: 10.34740/kaggle/dsv/6786840
title: Harry Potter's Second Wizard War Dataset
DOI: 10.5281/zenodo.10037961
title: GRACE High-Resolution Trend Mascons - Greenland Ice Sheet (2007-2015)

description: High-resolution mascon trend solution, computed for the Greenland Ice Sheet over the time period from January 2007 and January 2015, where each mascon regression model (including the trend) has been directly estimated from the Gravity Recovery and Climate Experiment (GRACE) Level 1B data. The GAD product has not been restored, meaning the ocean mascons are consistent with the Level 2 GSM product information.
Description of columns in the dataset:

Latitude center (deg)
Longitude center (deg)
Mass change trend (cm w.e. / yr)
Mass change trend uncertainty (cm w.e. / yr)
Latitude minimum (deg)
Latitude maximum (deg)
Longitude minimum (deg)
Longitude maximum (deg)
Area of mascon (sq. km)
Label of mascon
When citing this dataset, please also include this citation:
Loomis, B. D., D. Felikson, T. J. Sabaka, and B. Medley (2021). High‐spatial‐resolution mass rates from GRACE and GRACE‐FO: Global and ice sheet analyses.   Journal of Geophysical Research: Solid Earth, https://doi.org/10.1029/2021JB023024
DOI: 10.5281/zenodo.10027472
title: GRACE High-Resolution Trend Mascons - Greenland Ice Sheet (2007-2015)

description: High-resolution mascon trend solution, computed for the Greenland Ice Sheet over the time period from January 2007 and January 2015, where each mascon regression model (including the trend) has been directly estimated from the Gravity Recovery and Climate Experiment (GRACE) Level 1B data. The GAD product has not been restored, meaning the ocean mascons are consistent with the Level 2 GSM product information.
Description of columns in the dataset:

Latitude center (deg)
Longitude center (deg)
Mass change trend (cm w.e. / yr)
Mass change trend uncertainty (cm w.e. / yr)
Latitude minimum (deg)
Latitude maximum (deg)
Longitude minimum (deg)
Longitude maximum (deg)
Area of mascon (sq. km)
Label of mascon
When citing this dataset, please also include this citation:
Loomis, B. D., D. Felikson, T. J. Sabaka, and B. Medley (2021). High‐spatial‐resolution mass rates from GRACE and GRACE‐FO: Global and ice sheet analyses.   Journal of Geophysical Research: Solid Earth, https://doi.org/10.1029/2021JB023024
DOI: 10.5281/zenodo.10027473
title: GRACE High-Resolution Trend Mascons - Greenland Ice Sheet (2007-2015)

description: High-resolution mascon trend solution, computed for the Greenland Ice Sheet over the time period from January 2007 and January 2015, where each mascon regression model (including the trend) has been directly estimated from the Gravity Recovery and Climate Experiment (GRACE) Level 1B data. The GAD product has not been restored, meaning the ocean mascons are consistent with the Level 2 GSM product information.
When citing this dataset, please also include this citation:
Loomis, B. D., D. Felikson, T. J. Sabaka, and B. Medley (2021). High‐spatial‐resolution mass rates from GRACE and GRACE‐FO: Global and ice sheet analyses.   Journal of Geophysical Research: Solid Earth, https://doi.org/10.1029/2021JB023024
DOI: 10.5281/zenodo.10037988
title: АНАЛИЗ СОВРЕМЕННОГО СОСТОЯНИЯ И ПЕРСПЕКТИВЫ РАЗВИТИЯ СОЛНЕЧНЫХ ЭНЕРГОСИСТЕМ
DOI: 10.5281/zenodo.10037989
title: АНАЛИЗ СОВРЕМЕННОГО СОСТОЯНИЯ И ПЕРСПЕКТИВЫ РАЗВИТИЯ СОЛНЕЧНЫХ ЭНЕРГОСИСТЕМ
DOI: 10.5281/zenodo.10038000
title: ФОРМИРОВАНИЕ ФИЗИЧЕСКОЙ ВОЕННО-ПРИКЛАДНОЙ ПОДГОТОВЛЕННОСТИ МОЛОДЕЖИ ДОПРИЗЫВНОГО ВОЗРАСТА
DOI: 10.5281/zenodo.10038001
title: ФОРМИРОВАНИЕ ФИЗИЧЕСКОЙ ВОЕННО-ПРИКЛАДНОЙ ПОДГОТОВЛЕННОСТИ МОЛОДЕЖИ ДОПРИЗЫВНОГО ВОЗРАСТА
DOI: 10.5281/zenodo.10038010
title: O'QUVCHILARGA INGLIZ TILINI O'QITISHDA QO'LLANILADIGAN USULLAR
DOI: 10.5281/zenodo.10038011
title: O'QUVCHILARGA INGLIZ TILINI O'QITISHDA QO'LLANILADIGAN USULLAR
DOI: 10.5281/zenodo.10037980
title: Adjusted OpenFF POPC

description: 200 ns simulation of an OpenFF parameterized, pure POPC bilayer.
DOI: 10.5281/zenodo.10037981
title: Adjusted OpenFF POPC

description: 200 ns simulation of an OpenFF parameterized, pure POPC bilayer.
DOI: 10.5281/zenodo.10038026
title: ИҚТИСОДИЁТДА ИННОВАЦИОН МЕНЕЖМЕНТ ТАМОЙИЛЛАРИ ВА ФУНКСИЯЛАРИ
DOI: 10.5281/zenodo.10038025
title: ИҚТИСОДИЁТДА ИННОВАЦИОН МЕНЕЖМЕНТ ТАМОЙИЛЛАРИ ВА ФУНКСИЯЛАРИ
DOI: 10.5281/zenodo.10038030
title: ПСИХОЛОГИЧЕСКИЕ OСОБЕННОСТИ ПОВЕДЕНИЯ ЛЮДЕЙ В ЧРЕЗВЫЧАЙНЫХ СИТУАЦИЯХ
DOI: 10.5281/zenodo.10038029
title: ПСИХОЛОГИЧЕСКИЕ OСОБЕННОСТИ ПОВЕДЕНИЯ ЛЮДЕЙ В ЧРЕЗВЫЧАЙНЫХ СИТУАЦИЯХ
DOI: 10.5281/zenodo.10038055
title: UTILIZATION OF CHLORELLA VULGARIS IN FISH FEEDING IN FISHERY INDUSTRY
DOI: 10.5281/zenodo.10038054
title: UTILIZATION OF CHLORELLA VULGARIS IN FISH FEEDING IN FISHERY INDUSTRY
DOI: 10.5281/zenodo.10038064
title: ANVAR OBIDJON IJODINING BOLALAR HAJVIY LIRIKASI RIVOJIDAGI O'RNI MASALASI
DOI: 10.5281/zenodo.10038065
title: ANVAR OBIDJON IJODINING BOLALAR HAJVIY LIRIKASI RIVOJIDAGI O'RNI MASALASI
DOI: 10.5281/zenodo.10036822
title: Sailbuoy Echo PolarFront EK80 echosounder data [2/5] 2023-08-24 to 2023-08-26

description: This dataset is part 2/5 of
    Sailbuoy Echo PolarFront EK80 echosounder data 2023-08 to 2023-09
DOI: 10.5281/zenodo.10036823
title: Sailbuoy Echo PolarFront EK80 echosounder data [2/5] 2023-08-24 to 2023-08-26

description: This dataset is part 2/5 of
    Sailbuoy Echo PolarFront EK80 echosounder data 2023-08 to 2023-09
DOI: 10.5281/zenodo.8404173
title: Angiosperm genera named for women

description: This is a dataset of plant genera named for women and the women honoured. It is described in a forthcoming data paper in the Biodiversity Data Journal (BDJ).
DOI: 10.5281/zenodo.10015769
title: Angiosperm genera named for women

description: This is a dataset of plant genera named for women and the women honoured. It is described in a forthcoming data paper in the Biodiversity Data Journal (BDJ).
DOI: 10.5281/zenodo.10020280
title: Angiosperm genera named for women

description: This is a dataset of plant genera named for women and the women honoured. It is described in a forthcoming data paper in the Biodiversity Data Journal (BDJ).
DOI: 10.5281/zenodo.10038076
title: СУИЦИДАЛ ХУЛҚ – АМАЛИЙ ПСИХОЛОГИЯДА ДОЛЗАРБ МУАММО СИФАТИДА
DOI: 10.5281/zenodo.10038077
title: СУИЦИДАЛ ХУЛҚ – АМАЛИЙ ПСИХОЛОГИЯДА ДОЛЗАРБ МУАММО СИФАТИДА
DOI: 10.5281/zenodo.8397085
title: Angiosperm genera named for women

description: This is a dataset of plant genera named for women and the women honoured. It is described in a forthcoming data paper in the Biodiversity Data Journal (BDJ).
DOI: 10.5281/zenodo.10036825
title: Sailbuoy Echo PolarFront EK80 echosounder data [3/5] 2023-08-27 to 2023-09-03

description: This dataset is part 3/5 of
    Sailbuoy Echo PolarFront EK80 echosounder data 2023-08 to 2023-09
DOI: 10.5281/zenodo.10036824
title: Sailbuoy Echo PolarFront EK80 echosounder data [3/5] 2023-08-27 to 2023-09-03

description: This dataset is part 3/5 of
    Sailbuoy Echo PolarFront EK80 echosounder data 2023-08 to 2023-09
DOI: 10.34740/kaggle/dsv/6786977
title: Harry Potter's Second Wizard War Dataset
DOI: 10.25739/q46m-m857
title: EHT M87 Polarized Data

description: We release a calibrated polarimetric data set to accompany the First M87 Event Horizon Telescope Polarization Results paper series (EHT Collaboration et al. 2021a,b, 2023, see the README.md file for full references). It consists of 24 data sets covering the 4 observing days (April 5,6,10,11), two frequency bands (high and low) and two calibration pipelines (HOPS and CASA). All datasets include absolute EVPA calibration from ALMA and parallactic angle rotation. We provide two versions of the HOPS calibrated data. The base version ("\*_hops_ALMArot.uvfits") has no D-term calibration applied. The second version ("\*_hops_zbl-dtcal+selfcal") has zero-baseline-derived D-terms (covering ALMA, APEX, SMA, and JCMT) corrected, but the remaining stations are not corrected. In addition, this version has all visibility amplitudes and phases self-calibrated to fiducial images Stokes I images of M87 from the SMILI pipeline (EHTC+ Paper IV, 2019). Both versions of the HOPS data have had the relative R/L complex gains calibrated with a global, multi-source fit (Steel+ 2019). The CASA data has had all​ D-terms corrected to their reported values in EHTC+ Paper VII, 2021. It has not been self-calibrated. The R/L complex gains have been calibrated assuming the intrinsic Stokes V visibilities are zero.
DOI: 10.5281/zenodo.10036827
title: Sailbuoy Echo PolarFront EK80 echosounder data [4/5] 2023-09-04 to 2023-09-14

description: This dataset is part 4/5 of
    Sailbuoy Echo PolarFront EK80 echosounder data 2023-08 to 2023-09
DOI: 10.5281/zenodo.10036826
title: Sailbuoy Echo PolarFront EK80 echosounder data [4/5] 2023-09-04 to 2023-09-14

description: This dataset is part 4/5 of
    Sailbuoy Echo PolarFront EK80 echosounder data 2023-08 to 2023-09
DOI: 10.5281/zenodo.10036829
title: Sailbuoy Echo PolarFront EK80 echosounder data [5/5] 2023-09-15 to 2023-09-22

description: This dataset is part 5/5 of
    Sailbuoy Echo PolarFront EK80 echosounder data 2023-08 to 2023-09
DOI: 10.5281/zenodo.10036828
title: Sailbuoy Echo PolarFront EK80 echosounder data [5/5] 2023-09-15 to 2023-09-22

description: This dataset is part 5/5 of
    Sailbuoy Echo PolarFront EK80 echosounder data 2023-08 to 2023-09
DOI: 10.5281/zenodo.7982545
title: Units of Analysis Error in Orthodontic Meta-Analyses

description: Data extracted from orthodontic systematic reviews to evaluate the unit of analysis error in meta-analyses.
description: The data should not be shared on a profit basis or included in future research without permission.
DOI: 10.5281/zenodo.7982546
title: Units of Analysis Error in Orthodontic Meta-Analyses

description: Data extracted from orthodontic systematic reviews to evaluate the unit of analysis error in meta-analyses.
description: The data should not be shared on a profit basis or included in future research without permission.
DOI: 10.5281/zenodo.10035457
title: Machine learning based methods to generate conformational ensembles of disordered proteins (len36)

description: data is in rep_1 for all sequences, which contains the trajectory (xtc) file, Rg information (in the file Rg.out), pairwise distance information (in the file traj_analysis_data/pairwise_distance_matrix.csv) and the bspline coefficients (in the file bspline_info/xyz_coeff.npy)
DOI: 10.5281/zenodo.10035456
title: Machine learning based methods to generate conformational ensembles of disordered proteins (len36)

description: data is in rep_1 for all sequences, which contains the trajectory (xtc) file, Rg information (in the file Rg.out), pairwise distance information (in the file traj_analysis_data/pairwise_distance_matrix.csv) and the bspline coefficients (in the file bspline_info/xyz_coeff.npy)
DOI: 10.5281/zenodo.10035449
title: Machine learning based methods to generate conformational ensembles of disordered proteins (len18, point mutation)

description: data is organized by bin number (0-9) and mutation location (4, 8, 12). (Note: in the manuscript, we used the nomenclature bins 1-10 and mutation locations 5,8,13. We simply used a 0-index convention when naming our folders). All data is in rep_1, which contains the trajectory (xtc) file, Rg information (in the file Rg.out), pairwise distance information (in the file traj_analysis_data/pairwise_distance_matrix.csv) and the bspline coefficients (in the file bspline_info/xyz_coeff.npy). However, for bin_num=9/mutation_loc=12, the data used is in rep_2, not rep_1.
DOI: 10.5281/zenodo.10035448
title: Machine learning based methods to generate conformational ensembles of disordered proteins (len18, point mutation)

description: data is organized by bin number (0-9) and mutation location (4, 8, 12). (Note: in the manuscript, we used the nomenclature bins 1-10 and mutation locations 5,8,13. We simply used a 0-index convention when naming our folders). All data is in rep_1, which contains the trajectory (xtc) file, Rg information (in the file Rg.out), pairwise distance information (in the file traj_analysis_data/pairwise_distance_matrix.csv) and the bspline coefficients (in the file bspline_info/xyz_coeff.npy). However, for bin_num=9/mutation_loc=12, the data used is in rep_2, not rep_1.