cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/x792grbm9g.1
title: i-PI 2.0: A universal force engine for advanced molecular simulations

description: Progress in the atomic-scale modelling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomic forces that work by either solving the electronic structure problem explicitly, or by computing accurate approximations of the solution and by the development of techniques that use the Born–Oppenheimer (BO) forces to move the atoms on the BO potential energy surface. As a consequence of these developments it is now possible to identify stable or metastable states, to sample configurations consistent with the appropriate thermodynamic ensemble, and to estimate the kinetics of reactions and phase transitions. All too often, however, progress is slowed down by the bottleneck associated with implementing new optimization algorithms and/or sampling techniques into the many existing electronic-structure and empirical-potential codes. To address this problem, we are thus releasing a new version of the i-PI software. This piece of software is an easily extensible framework for implementing advanced atomistic simulation techniques using interatomic potentials and forces calculated by an external driver code. While the original version of the code (Ceriotti et al., 2014) was developed with a focus on path integral molecular dynamics techniques, this second release of i-PI not only includes several new advanced path integral methods, but also offers other classes of algorithms. In other words, i-PI is moving towards becoming a universal force engine that is both modular and tightly coupled to the driver codes that evaluate the potential energy surface and its derivatives.
DOI: 10.7910/dvn/u6oyk9/9bi9xi
title: PEI_UK-1.tab
DOI: 10.17632/z4ttmmftvm.1
title: Fractionating blunted reward processing characteristic of anhedonia by over-activating primate subgenual anterior cingulate cortex

description: The effects of pharmacological over-activation of marmoset sgACC/25 on anticipatory arousal, motivational performance and reward consumption. The effects of ketamine in ameliorating over-activation induced phenotypes.
DOI: 10.17632/sfsypw9bwz.1
title: CMAC Neural Networks

description: The Cerebellar Model Articulation Controller (CMAC) is an influential cerebrum propelled processing model in numerous pertinent fields. There are different researches done using CMAC in many applications using its characteristics in easy implementation and good results for example: facial expression recognition, pattern recognition etc. In this paper we have presented some methods of using CMAC and presents their results.
DOI: 10.17632/7w3pmrwjd6.1
title: Data for: Development of a snow kernel to better model the anisotropic reflectance of pure snow in a kernel-driven BRDF model framework

description: 1. About the visulization tool for the RossThick-LiSparseReciprocal-Snow (RTLSRS for short) BRDF model. This is the description for the interface and operation of the # rtlsr_gui.sav application, which can be used to launch runtime IDL applications. Source code will be offerred on request from email: jiaozt@bnu.edu.cn. "Constrain" botton on this application interface inplements the function that does not allow negative model parameters, which are suggested by MODIS BRDF parameter product. 2. Environment：need to install IDL8.2 and above 3. Data format：this tool supports the input format of text file. The specific input format is requested as follows: the first line defines the number of multi-angle observations and the number of bands for the multi-angle input dataset, respectively. From the beginning of the second line, each line defines an observation with each sample representing the viewing and solar geometries (in degree) and the reflectances in different bands. Specifically, from left to right, each sample represents view zenith angle,view azimuth angle,solar zenith angle,view azimuth angle and the reflectance of each band in sequence. 4. The exemplary data A typical POLDER example was obtained in Apr. 2006 from the file "brdf_ndvi02.0216_3046.txt." in POLDER database. This pixel is located on the central North Greenland Ice Sheet (i.e., 52.1W, 78.03N) and was recorded to represent a relatively pure snow and ice IGBP class with a Normalized Difference Vegetation Index (NDVI) value of -0.03. This pixel incorporates more than 1,000 POLDER reflectances and 70 solar angles representing a very good BRDF sampling distribution. The method proposed by Breon (2005) should be utilized to correct the viewing geometry from the original POLDER measurements that offer two correction parameters (DVzC and DVzA), which was used in the paper. However, please note that this input example file including 6-band observations is not corrected for the viewing geometries. Input directory: contains an input example file, e.g., "brdf_ndvi02.0216_3046.txt". Output directory: contains the corresponding output example file, e.g.,"brdf_ndvi02.0216_3046_result.txt". 5. All validation data for the RTLSRS model are open and available to users following the details of the paper.
DOI: 10.7910/dvn/aq5lh5/luqfut
title: matrix_subset_merged_wphenotype.tab
DOI: 10.7910/dvn/hs5oxf/jceqyh
title: CGMFC-21
DOI: 10.17632/vffznz4m4k.1
title: Data For: Sub-Chronic Stress Exacerbates the Pro-Thrombotic Phenotype in BDNFVal/Met Mice: Gene-Environment Interaction in the Modulation of Arterial Thrombosis

description: Brain-Derived Neurotrophic Factor (BDNF) Val66Met polymorphism has been associated with increased susceptibility to develop mood disorders and recently it has been also linked with cardiovascular disease (CVD). Interestingly, stressful conditions unveil the anxious/depressive-like behavioral phenotype in heterozygous BDNFVal66Met (BDNFVal/Met) mice, suggesting an important relationship in terms of gene-environment interaction (GxE). However, the interplay between stress and BDNFVal/Met in relation to CVD is completely unknown. Here, we showed that BDNFVal/Met mice display a greater propensity to arterial thrombosis than wild type BDNFVal/Val mice after 7 days of restraint stress (RS). RS markedly increased the number of leukocytes and platelets, and induced hyper-responsive platelets as showed by increased circulating platelet/leukocyte aggregates and enhanced expression of P-selectin and GPIIbIIIa in heterozygous mutant mice. In addition, stressed BDNFVal/Met mice had a greater number of large and reticulated platelets but comparable number and maturation profile of bone marrow megakaryocytes compared to BDNFVal/Val mice. Interestingly, RS led to a significant reduction of BDNF expression accompanied by an increased activity of tissue factor in the aorta of both BDNFVal/Val and BDNFVal/Met mice. In conclusion, we provide evidence that sub-chronic stress unveils prothrombotic phenotype in heterozygous BDNF Val66Met mice affecting both the number and functionality of blood circulating cells, and the expression of key thrombotic molecules in aorta. Human studies will be crucial to understand whether this GxE interaction need to be taken into account in risk stratification of coronary artery disease (CAD) patients.
DOI: 10.5281/zenodo.1494281
title: Group contribution models for heat of formation (SUB 2018)

description: We present a set of group contribution models for predicting heat of formation of organic compounds. A dataset containing 1004 molecular structures from DIPPR database was split into a learning and a test sets further used for model training and validation. The model building was performed with software Ambit-GCM (https://doi.org/10.5281/zenodo.1470793). A set of preliminary models were build according to various fragmentation schemes, with and without use of correction factors and external descriptors. Different orders of additive schemes were studied. Every model in the set was validated using leave-one-out procedure and Y-scrambling technique as well as model performances were tested using the external dataset. The best five models full data and corresponding statistical characteristics are available in models.zip. The model 2 is available also as a JSON file in the archive and can be used for theoretical prediction of heat of formation.

To use the model 2 please download gcm-predict.jar from https://doi.org/10.5281/zenodo.1470793). Example application of gcm-predict (Ambit-GCM) module for a single structure is given below:

java -jar gcm-predict-v1.1.jar -s CC(C)OCC(C)O -c model_2.json<br>
GCM value (Hf) for CC(C)OCC(C)O is -528.7163407123614

The gcm-predict (Ambit-GCM) module can also be applied for a set of structures. An example follows with 5 molecules inputted as a *.csv file:<br>
 <br>
java -jar gcm-predict-v1.1.jar -i Prediction_Examples.csv -c model_2.json<br>
GCM calculateting property Hf for 5 molecules ...<br>
Mol#,ModelValue(Hf),SMILES,CalcStatus<br>
1,-126.23055043388635,CCCC,OK<br>
2,-353.4883670381994,c1ccc(c(c1)O)O,OK<br>
3,-524.1758445616103,CCC(CO)O,OK<br>
4,-220.91676720730212,CC(Cc1ccccc1)O,OK<br>
5,-728.1309488149211,C(C(Cl)(Cl)F)(F)F,OK

The output lines contain:  molecule number, predicted Hf, SMILES, and calculation status

The full data for training and validation is available in learn-test-sets.zip. Data can be used for retraining or improving the models.

More examples for using Ambit-GCM software for group contribution modeling and property prediction are available in https://doi.org/10.5281/zenodo.1471646
DOI: 10.5281/zenodo.1494282
title: Group contribution models for heat of formation (SUB 2018)

description: We present a set of group contribution models for predicting heat of formation of organic compounds. A dataset containing 1004 molecular structures from DIPPR database was split into a learning and a test sets further used for model training and validation. The model building was performed with software Ambit-GCM (https://doi.org/10.5281/zenodo.1470793). A set of preliminary models were build according to various fragmentation schemes, with and without use of correction factors and external descriptors. Different orders of additive schemes were studied. Every model in the set was validated using leave-one-out procedure and Y-scrambling technique as well as model performances were tested using the external dataset. The best five models full data and corresponding statistical characteristics are available in models.zip. The model 2 is available also as a JSON file in the archive and can be used for theoretical prediction of heat of formation.

To use the model 2 please download gcm-predict.jar from https://doi.org/10.5281/zenodo.1470793). Example application of gcm-predict (Ambit-GCM) module for a single structure is given below:

java -jar gcm-predict-v1.1.jar -s CC(C)OCC(C)O -c model_2.json<br>
GCM value (Hf) for CC(C)OCC(C)O is -528.7163407123614

The gcm-predict (Ambit-GCM) module can also be applied for a set of structures. An example follows with 5 molecules inputted as a *.csv file:<br>
 <br>
java -jar gcm-predict-v1.1.jar -i Prediction_Examples.csv -c model_2.json<br>
GCM calculateting property Hf for 5 molecules ...<br>
Mol#,ModelValue(Hf),SMILES,CalcStatus<br>
1,-126.23055043388635,CCCC,OK<br>
2,-353.4883670381994,c1ccc(c(c1)O)O,OK<br>
3,-524.1758445616103,CCC(CO)O,OK<br>
4,-220.91676720730212,CC(Cc1ccccc1)O,OK<br>
5,-728.1309488149211,C(C(Cl)(Cl)F)(F)F,OK

The output lines contain:  molecule number, predicted Hf, SMILES, and calculation status

The full data for training and validation is available in learn-test-sets.zip. Data can be used for retraining or improving the models.

More examples for using Ambit-GCM software for group contribution modeling and property prediction are available in https://doi.org/10.5281/zenodo.1471646
DOI: 10.17632/j8pfkchfg8.1
title: Couples coping with nutrition-related problems in advanced cancer: narratives of the interviews.

description: Narratives of the semi-structured interviews in couples confronted with nutrition-related problems in advanced cancer. Dutch
DOI: 10.7910/dvn/uo3qib/ur3elo
title: clean_tax_data.do
DOI: 10.7910/dvn/uo3qib/vco3sj
title: clean_population_election_data.do
DOI: 10.7910/dvn/uo3qib/xbc2sa
title: clean_school_data.do
DOI: 10.17632/97p2x3hjst.1
title: Boxplot of climate indices consisting of 8 climate models with the ensemble mean for different climatic station in the Komadugou-Yobe basin, Lake Chad region, West Africa

description: The data is the result of 8 GCMs dynamically downscaled by RCA4 regional climate model (RCM). The GCMs considered are CanESM2, CNRM-CM5, EC-EARTH, GFDL-ESM2M, HadGEM2-ES, MIROC5, MPI-ESM-LR, NorESM1-M. The results from the RCM is bias-corrected using the quantile mapping approach at station scale. The results of the climate indices generated by the 8 GCMs and the ensemble mean are plotted to generate the level of uncertainty of the models. The climate indices considered are rx5day, cdd, cwd, r95p, r99p, prctot, dtr, tx10p,tx90p,tn10p, tn90p and wsdi. The climate indices were generated for 2020-2050 and 2060-2090 under RCP 4.5 and RCP 8.5
DOI: 10.7910/dvn/6tbxso/yx02xm
title: DHS_Regional_Dataset_Codebook_20160610.doc
DOI: 10.7910/dvn/xhanw7/fzenwu
title: README.doc
DOI: 10.7910/dvn/zjqzh5/nbhn2n
title: MarchCourier.tab
DOI: 10.7910/dvn/zjqzh5/unrhmi
title: JanuaryCourier.tab
DOI: 10.7910/dvn/zjqzh5/zrvkwc
title: README.pdf
DOI: 10.7910/dvn/zjqzh5/8qz3fh
title: Replication_Jan.R
DOI: 10.7910/dvn/zjqzh5/xbibbz
title: Replication_Mar.R
DOI: 10.17632/c8hwkbycfb.1
title: Data for: What makes a shape ‘baba’? The shape features prioritized in sound-shape correspondence change with development

description: Data was collected at the Museum of Science Boston in children and adults, ages 6-35. All participants had to judge which of two sounds, presented sequentially, matched a shape (Expt 1: two interval forced choice) or which of two shapes, presented simultaneously matched a sound (Expt 2: two alternative forced choice). For each experiment a key is provided in the spreadsheet indicating the conditions for the different sounds and shapes, stimulus presentation order and responses selected.
DOI: 10.7910/dvn/muf2hc/gjkjsb
title: README(1).pdf
DOI: 10.7910/dvn/muf2hc/hb1dmy
title: code.tar.gz
DOI: 10.7910/dvn/29089/sdxg5d
title: List of datasets in the World Historical Dataverse.10.15.2015.tab
DOI: 10.5517/ccdc.csd.cc1n1t3m
title: CCDC 1521080: Experimental Crystal Structure Determination

description: Related Article: Hongliang Mu, Yingyun Long, Yanping Zhang, Yuesheng Li|2018|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: 10.5517/ccdc.csd.cc1n1t4n
title: CCDC 1521081: Experimental Crystal Structure Determination

description: Related Article: Hongliang Mu, Yingyun Long, Yanping Zhang, Yuesheng Li|2018|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: 10.5517/ccdc.csd.cc1n1t5p
title: CCDC 1521082: Experimental Crystal Structure Determination

description: Related Article: Hongliang Mu, Yingyun Long, Yanping Zhang, Yuesheng Li|2018|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: 10.5517/ccdc.csd.cc1nf79m
title: CCDC 1532060: Experimental Crystal Structure Determination

description: Related Article: R. Hrdina, M. Larossa, Logemann|2018|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: 10.5517/ccdc.csd.cc1lf5ry
title: CCDC 1472430: Experimental Crystal Structure Determination

description: Related Article: Yang-Hui Luo, Jin-Wen Wang, Chen Chen, Yao-Jia Li, Ting Ding, Bo-Jun Wang, Baiwang Sun|2018|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: 10.5517/ccdc.csd.cc1nn11d
title: CCDC 1538593: Experimental Crystal Structure Determination

description: Related Article: C. Pellerito, G.M. Paternò, D.A Tocher, F. Cacialli, T. Fiore|2018|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: 10.5517/ccdc.csd.cc1nzxcx
title: CCDC 1549050: Experimental Crystal Structure Determination

description: Related Article: Yang-Hui Luo, Jin-Wen Wang, Chen Chen, Yao-Jia Li, Ting Ding, Bo-Jun Wang, Baiwang Sun|2018|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: 10.5517/ccdc.csd.cc1pkfc1
title: CCDC 1565883: Experimental Crystal Structure Determination

description: Related Article: Yassine Beldjoudi, Rui Sun, Ana Belén Arauzo, Javier Campo, Robert J. Less, Jeremy M. Rawson|2018|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: 10.7910/dvn/vnxem6/wn8vf3
title: fl-rep.do
DOI: 10.5517/ccdc.csd.cc1yw7rt
title: CCDC 1813647: Experimental Crystal Structure Determination

description: Related Article: Naohiko Akasaka, Kaho Tanaka, Shintaro Ishida and Takeaki Iwamoto|2018|Inorganics|6|21|doi:10.3390/inorganics6010021
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.cc1yydmx
title: CCDC 1815720: Experimental Crystal Structure Determination

description: Related Article: Christoph Strässler and Heinz Heimgartner|2019|Heterocycles|99|446|doi:10.3987/COM-18-S(F)41
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.cc1yydny
title: CCDC 1815721: Experimental Crystal Structure Determination

description: Related Article: Christoph Strässler and Heinz Heimgartner|2019|Heterocycles|99|446|doi:10.3987/COM-18-S(F)41
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.cc1yydpz
title: CCDC 1815722: Experimental Crystal Structure Determination

description: Related Article: Christoph Strässler and Heinz Heimgartner|2019|Heterocycles|99|446|doi:10.3987/COM-18-S(F)41
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: 10.7910/dvn/zd4m1k/46zj54
title: 高校图书馆数据监护的流程管理研究.doc
DOI: 10.7910/dvn/pcokvq/ni07qw
title: sub10_BOLD_run (10).REC
DOI: 10.7910/dvn/gfrhak/09orwi
title: CKP_README.txt
DOI: 10.7910/dvn/gfrhak/hvvxbc
title: working_2014_07_11_forR.tab
DOI: 10.7910/dvn/pcokvq/4ee501
title: sub10_BOLD_run (10).PAR
DOI: 10.7910/dvn/29967/gitarn
title: Economics_2015-43.pdf
DOI: 10.7910/dvn/agtz36/rifqpv
title: ItPSR_Simulations Fig1.R
DOI: 10.7910/dvn/29105/pgphi5
title: Economics_2015-35.pdf
DOI: 10.7910/dvn/agtz36/dybfri
title: ItPSR_Simulations Fig2.R
DOI: 10.7910/dvn/rmrjzv/tueqde
title: Economics_2015-39.pdf
DOI: 10.7910/dvn/lyz3yc/kxhxzo
title: petitions-1.xlsx
DOI: 10.7910/dvn/n5nrex/ri74nl
title: petitions.xlsx
DOI: 10.7910/dvn/wric3t/ntsmsq
title: petitions.xlsx
DOI: 10.7910/dvn/wzmipm/a8pgwz
title: petitions.xlsx
DOI: 10.7910/dvn/1p1jfg/p6jr8h
title: RetentionDataset.tab
DOI: 10.7910/dvn/gfrhak/kg4ime
title: working_2014_07_11.tab
DOI: 10.7910/dvn/gfrhak/scqt8i
title: MLM_replication_2015_10_30.R
DOI: 10.7910/dvn/9zshyb/0lhp4s
title: NGC1365_scanamorphos_spire500_signal.fits
DOI: 10.7910/dvn/o6ucim/hcim98
title: Mexico PEI Responses state-level.tab
DOI: 10.7910/dvn/o6ucim/hgbccr
title: Mexico PEI Responses state-level-1.tab
DOI: 10.17632/3r7nd7gb56.1
title: Data for: Spatially explicit forecast of feedstock potentials for second generation bioconversion industry from the EU agricultural sector until the year 2030

description: The data set includes the detailed results of the research work. Furthermore, interim results are presented as well as a comparison of interim results to literature.
DOI: 10.7910/dvn/o6ucim/anapvm
title: Mexico PEI Responses expert-level.dta
DOI: 10.7910/dvn/o6ucim/m17mqh
title: Mexico PEI Responses public state-level.tab
DOI: 10.7910/dvn/o6ucim/ryzt5j
title: Mexico PEI Responses public state-level-1.tab
DOI: 10.7910/dvn/o6ucim/up7osz
title: Mexico PEI Responses public expert-level.dta
DOI: 10.7910/dvn/o6ucim/xgatsk
title: Mexico PEI Responses expert-level.tab
DOI: 10.7910/dvn/o6ucim/8c4oyd
title: Mexico PEI Responses expert-level.tab
DOI: 10.7910/dvn/o6ucim/8eyehi
title: Mexico PEI Responses public state-level.tab
DOI: 10.7910/dvn/o6ucim/itimcj
title: Mexico PEI Responses public state-level-1.tab
DOI: 10.7910/dvn/o6ucim/ruwexf
title: Mexico Codebook.pdf
DOI: 10.7910/dvn/qdruaz/a0gaga
title: Tract Census Data.tab