cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14272/ffzhicfahsdfkz-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (4,4,5,5-tetramethyl-2-thiophen-2-yl-1,3,2-dioxaborolane)
title: InChI=1S/C10H15BO2S/c1-9(2)10(3,4)13-11(12-9)8-6-5-7-14-8/h5-7H,1-4H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, Chloroform-d [7.27 ppm], ppm) δ = 7.68–7.64 (m, 2H), 7.21 (dd, J = 3.4 Hz, J = 4.7 Hz, 1H), 1.36 (s, 12H).
DOI: 10.14272/ffzhicfahsdfkz-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (4,4,5,5-tetramethyl-2-thiophen-2-yl-1,3,2-dioxaborolane)
title: InChI=1S/C10H15BO2S/c1-9(2)10(3,4)13-11(12-9)8-6-5-7-14-8/h5-7H,1-4H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, Chloroform-d [77.0 ppm], ppm) δ = 137.1 (1C), 132.3 (1C), 128.2 (1C), 84.0 (2C), 24.7 (4C), C-B (1C) signal missing due to coupling with B.
DOI: 10.14272/ffzhicfahsdfkz-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) (4,4,5,5-tetramethyl-2-thiophen-2-yl-1,3,2-dioxaborolane)
title: InChI=1S/C10H15BO2S/c1-9(2)10(3,4)13-11(12-9)8-6-5-7-14-8/h5-7H,1-4H3

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 2997, 2978, 2928, 1519, 1482, 1468, 1422, 1361, 1329, 1295, 1266, 1208, 1166, 1135, 1112, 1078, 1055, 1017, 1000, 956, 929, 914, 849, 826, 778, 754, 722, 687, 664, 630, 578 cm–1.
DOI: 10.14272/ffzhicfahsdfkz-uhfffaoysa-n/chmo0001146
title: 1H--13C heteronuclear single quantum coherence (1H-13C HSQC) (4,4,5,5-tetramethyl-2-thiophen-2-yl-1,3,2-dioxaborolane)
title: InChI=1S/C10H15BO2S/c1-9(2)10(3,4)13-11(12-9)8-6-5-7-14-8/h5-7H,1-4H3

description: dataset for 1H--13C heteronuclear single quantum coherence (1H-13C HSQC)
DOI: 10.14272/ffzhicfahsdfkz-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) (4,4,5,5-tetramethyl-2-thiophen-2-yl-1,3,2-dioxaborolane)
title: InChI=1S/C10H15BO2S/c1-9(2)10(3,4)13-11(12-9)8-6-5-7-14-8/h5-7H,1-4H3

description: dataset for mass spectrometry (MS) EI (m/z, 70 eV, 110 °C): 210 (76) [M]+, 195 (60), 181 (31), 131 (32), 124 (83), 111/110 (100,52), 69 (74); HRMS–EI (m/z): [M]+ calcd for C10H15O2B1S1, 210.0880; found, 210.0882.
DOI: 10.14272/ffzhicfahsdfkz-uhfffaoysa-n/chmo0000594
title: 11B nuclear magnetic resonance spectroscopy (11B NMR) (4,4,5,5-tetramethyl-2-thiophen-2-yl-1,3,2-dioxaborolane)
title: InChI=1S/C10H15BO2S/c1-9(2)10(3,4)13-11(12-9)8-6-5-7-14-8/h5-7H,1-4H3

description: dataset for 11B nuclear magnetic resonance spectroscopy (11B NMR) 11B NMR (128 MHz, Chloroform-d, ppm) δ = 10.3.
DOI: 10.14272/ldumpodwxylkdp-uhfffaoysa-n.1
title: bis(4-tert-butylphenyl)-[4-(2-thienyl)phenyl]amine
title: InChI=1S/C30H33NS/c1-29(2,3)23-11-17-26(18-12-23)31(27-19-13-24(14-20-27)30(4,5)6)25-15-9-22(10-16-25)28-8-7-21-32-28/h7-21H,1-6H3

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367]. The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]: InChI descriptor[cheminf_000113]: InChI=1S/C30H33NS/c1-29(2,3)23-11-17-26(18-12-23)31(27-19-13-24(14-20-27)30(4,5)6)25-15-9-22(10-16-25)28-8-7-21-32-28/h7-21H,1-6H3, and canonical SMILES descriptor[cheminf_000007]: CC(c1ccc(cc1)N(c1ccc(cc1)C(C)(C)C)c1ccc(cc1)c1cccs1)(C)C, and by the IUPAC name[cheminf_000107]: N,N-bis(4-tert-butylphenyl)-4-thiophen-2-ylaniline. The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]: The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-28482 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]: Melting point descriptor[CHEMINF_000256]: 204.0 (°C) Boiling point descriptor[CHEMINF_000257]: Refractive index descriptor[CHEMINF_000253]: The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]: CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR) CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR) CHMO:0000470 | mass spectrometry (MS) CHMO:0000630 | infrared absorption spectroscopy (IR) The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: Used ontologies: CHEBI - Chemical Entities of Biological Interest CHEMINF - chemical information ontology (information entities about chemical entities) CHMO - Chemical Methods Ontology OBI - Ontology for Biomedical Investigations
DOI: 10.14272/ldumpodwxylkdp-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) (N,N-bis(4-tert-butylphenyl)-4-thiophen-2-ylaniline)
title: InChI=1S/C30H33NS/c1-29(2,3)23-11-17-26(18-12-23)31(27-19-13-24(14-20-27)30(4,5)6)25-15-9-22(10-16-25)28-8-7-21-32-28/h7-21H,1-6H3

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 3037, 2958, 2900, 2861, 1598, 1533, 1507, 1496, 1460, 1431, 1391, 1361, 1320, 1298, 1278, 1266, 1210, 1200, 1181, 1111, 1082, 1050, 1021, 1014, 956, 945, 922, 849, 829, 817, 751, 742, 737, 724, 696, 579, 568, 545 cm–1.
DOI: 10.14272/ldumpodwxylkdp-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) (N,N-bis(4-tert-butylphenyl)-4-thiophen-2-ylaniline)
title: InChI=1S/C30H33NS/c1-29(2,3)23-11-17-26(18-12-23)31(27-19-13-24(14-20-27)30(4,5)6)25-15-9-22(10-16-25)28-8-7-21-32-28/h7-21H,1-6H3

description: dataset for mass spectrometry (MS) FAB-MS (m/z, 3-NBA): 440/439 (49/100) [M]+, 154 (59), 137/136 (37/41); HRMS–FAB (m/z): [M]+ calcd for C30H33N1S1, 439.2328, found 439.2329.
DOI: 10.14272/ldumpodwxylkdp-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (N,N-bis(4-tert-butylphenyl)-4-thiophen-2-ylaniline)
title: InChI=1S/C30H33NS/c1-29(2,3)23-11-17-26(18-12-23)31(27-19-13-24(14-20-27)30(4,5)6)25-15-9-22(10-16-25)28-8-7-21-32-28/h7-21H,1-6H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, Dichloromethane-d2 [54.0 ppm], ppm) δ = 148.3 (1C), 146.7 (2C), 145.4 (2C), 144.9 (1C), 128.5 (1C), 128.1 (1C), 127.0 (2C), 126.7 (4C), 124.8 (4C), 124.3 (1C), 123.1 (2C), 122.5 (1C), 34.8 (2C), 31.7 (6C).
DOI: 10.14272/ldumpodwxylkdp-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (N,N-bis(4-tert-butylphenyl)-4-thiophen-2-ylaniline)
title: InChI=1S/C30H33NS/c1-29(2,3)23-11-17-26(18-12-23)31(27-19-13-24(14-20-27)30(4,5)6)25-15-9-22(10-16-25)28-8-7-21-32-28/h7-21H,1-6H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, Dichloromethane-d2 [5.32 ppm], ppm) δ = 7.47–7.44 (m, 2H), 7.32–7.28 (m, 4H), 7.22 (d, J = 4.3 Hz, 2H), 7.07–6.99 (m, 7H), 1.32 (s, 18H). Impurities: water from CD2Cl2 at 1.53 ppm.
DOI: 10.17909/zdfp-m905
title: Jupiter's FUV auroras during the Juno EM
DOI: 10.13139/olcf/1411410
title: Populus Trichocarpa Genome-Wide Association Study (GWAS) Population SNP Dataset Released

description: This dataset includes genetic variations found in 882 poplar trees, and provides useful information to scientists studying plants as well as researchers more generally in the fields of biofuels, materials science, and secondary plant compounds. For nearly 10 years, researchers with DOE’s BioEnergy Science Center (BESC), a multi-institutional organization headquartered at ORNL, have studied the genome of Populus — a fast-growing perennial tree recognized for its economic potential in biofuels production. This Genome-Wide Association Study (GWAS) dataset includes more than 28 million single nucleotide polymorphisms, or SNPs that have been derived from 17 trillion bases of sequence data generated from 882 undomesticated Populus genotypes. Each SNP represents a variation in a single DNA nucleotide, or building block, that can act as a biological marker and/or causal allele within a protein sequence, helping scientists locate genes associated with certain characteristics, conditions or diseases. The results of this analysis have been used, among other things, to 1) seek genetic control of cell-wall recalcitrance — a natural characteristic of plant cell walls that prevent the release of sugars under microbial conversion and restricts biofuels production and 2) identify the molecular mechanisms controlling deposition of lignin in plant structures. Lignin is a polyphenolic polymer that strengthens plant cell walls and acts as a barrier to microbial access to cellulose during saccharfication — the process of breaking cellulose down into simple sugars for fermentation. Although the dataset’s most immediate applications are in fundamental plant sciences, ORNL researchers plan to use the GWAS data to inform applied work in areas such as cleaner, sustainable transportation biofuels, carbon fiber for lightweight vehicles and alternatives to conventional plastics and building insulation materials.
DOI: 10.17632/ft9bhdgybs.2
title: AN EMG DATASET FOR ARABIC SIGN LANGUAGE ALPHABET AND NUMBERS

description: ABSTRACT Sign languages are natural, gestural languages that use visual channel to communicate. Deaf people develop them to overcome their inability to communicate orally. Sign language interpreters bridge the gap that deaf people face in society and provide them with an equal opportunity to thrive in all environments. However, Deaf people often struggle to communicate on a daily basis, especially in public service spaces such as hospitals, post offices, and municipal buildings. Therefore, the implementation of a tool for automatic recognition of sign language is essential to allow the autonomy of deaf people. Moreover, it is difficult to provide full-time interpreters to help deaf people in all public services and administrations. Although surface electromyography (sEMG) provides an important potential technology for the detection of hand gestures, the related research in automatic SL recognition remains limited. To date, most works have focused on the recognition of hand gestures from images, videos, or gloves. The works of BEN HAJ AMOR et al. on EMG signals have shown that these multichannel signals contain rich and detailed information that can be exploited, in particular for the recognition of handshape and for the control prosthesis. Consequently, these successes represent a great step towards the recognition of gestures in sign language. We build a large database of EMG data, recorded while signing the 28 characters of the Arabic sign language alphabet. This provides a valuable resource for research into how the muscles involved in signing produce the shapes needed to form the letters of the alphabet. Instructions: The data for this project is provided as zipped NumPy arrays with custom headers. In order to load these files, you will need to have the NumPy package installed. The respective loadz primitive allows for a straight forwardloading of the datasets. The data is organized as follows: The data for each label (handshape) is stored in a separate folder. Each folder contains .npz files. An npz file contains the data for one record (a matrix 8x400). For more details, please refer to the paper.
DOI: 10.26180/20999185.v1
title: Evaluation of statistical methods used to meta-analyse results from interrupted time series studies: a simulation study - Code and Data

description: The datasets containing simulation performance results during the current study, in addition to the code to replicate the simulation study in its entirety, are available here. See the README file for a description the Stata do-files, R-script files, tips to run the code, and the performance result dataset dictionaries.
DOI: 10.5066/p9huikrl
title: Data Release: Buttonland Swamp, health survey, 2022

description: This dataset contains tree health information in 2022 for ten trees in Buttonland Swamp, part of the Cache River Watershed in Illinois. The data includes the height of the trees along with their crown ratio. This allows an understanding of the size and canopy health of Buttonland Swamp cypress trees. A Munsell color chart was utilized in determining leaf color. Leaf color tells whether the trees are healthy or if they are experiencing stress such as lack of nutrients, exposure to pesticides, or impoundment.
DOI: 10.5066/p9z9l6dq
title: Data Release: Buttonland Swamp, reproduction, 2006 to 2021

description: This dataset represents annual reproductive litterfall collections and seedling data from Crawford Tract, Eagle Pond, Section 8 Woods, Deer Pond, Wildcat Bluff, Snake Hole, and Heron Pond from 2006 to 2021. These sites are part of the study location, Buttonland Swamp, within the Cache River Watershed, Illinois. Summer and winter water depths were also included in the dataset.
DOI: 10.5281/zenodo.8112213
title: LML-Learning Meta-Learning Dataset_update version

description: The LML dataset presented in this paper contains both categorical and continuous data for 7 meta-learning parameters: age, gender, degree of illusion of competence, sleep duration, chronotype, experience of imposter phenomenon and multiple intelligence. Convenience sampling and simple random sampling method are used to structure the volunteered anonymous online survey data collection project for LML dataset creation. Survey questionnaires are set to explore adult learners' (age over 18) meta-learning parameters. The responses from the 54 survey questionnaires contains raw data from 1021 current university students from 11 universities of Bangladesh. Mean and standard deviation for the participant's baseline attributes are given for scale parameters, and frequency and percentage are calculated for categorical parameters. Academic curriculum, courses as well as professional training materials can be reviewed and re-designed with focusing on the diversity of learners. How the designed courses will be learned by learners along with how they will be taught is a significant point for education in any discipline. As the survey questionnaires are set for adult learners and only current university students have participated in this survey, this dataset is appropriate for study andragogy and heutagogy but pedagogy. Ethics statements Ethical approval (Involvement of Human Subjects) were obtained from the Biosafety, Biosecurity and Ethical Clearance Committee, Jahangirnagar University (reference no. is BBEC,JU/M 2022/01 (18)). The dataset presented in this article is open for public access. It is mandatory to follow the correct citation guidelines when using this LML dataset. Credit author statement Sonia Corraya: Conceptualization, Methodology, Data curation, Visualization, Writing – original draft; Professor Shamim Al Mamun: Supervision; Professor M. Shamim Kaiser: Supervision. This dataset belongs to Authors, Institute of Information Technology, Jahangirnagar University, savar, Dhaka-1342, Bangladesh.
DOI: 10.14272/yfmxadmhxaezba-uhfffaoysa-n/chmo0000595
title: 13C nuclear magnetic resonance spectroscopy (13C NMR) (4-(2,3-dihydrothieno[3,4-b][1,4]dioxin-5-yl)-N,N-bis(4-methoxyphenyl)aniline)
title: InChI=1S/C26H23NO4S/c1-28-22-11-7-20(8-12-22)27(21-9-13-23(29-2)14-10-21)19-5-3-18(4-6-19)26-25-24(17-32-26)30-15-16-31-25/h3-14,17H,15-16H2,1-2H3

description: dataset for 13C nuclear magnetic resonance spectroscopy (13C NMR) 13C NMR (100 MHz, Dichloromethane-d2 [54.0 ppm], ppm) δ = 156.6 (2C), 148.0 (1C), 142.9 (1C), 141.3 (2C), 137.8 (1C), 127.2 (2C), 127.1 (4C), 125.9 (1C), 120.9 (2C), 118.0 (1C), 115.2 (4C), 96.5 (1C), 65.3 (1C), 65.2 (1C), 56.0 (2C).
DOI: 10.14272/yfmxadmhxaezba-uhfffaoysa-n/chmo0000470
title: mass spectrometry (MS) (4-(2,3-dihydrothieno[3,4-b][1,4]dioxin-5-yl)-N,N-bis(4-methoxyphenyl)aniline)
title: InChI=1S/C26H23NO4S/c1-28-22-11-7-20(8-12-22)27(21-9-13-23(29-2)14-10-21)19-5-3-18(4-6-19)26-25-24(17-32-26)30-15-16-31-25/h3-14,17H,15-16H2,1-2H3

description: dataset for mass spectrometry (MS) FAB-MS (m/z, 3-NBA): 447/446/445 (13/41/100) [M]+; HRMS–FAB (m/z): [M]+ calcd for C26H23O4N1S1, 445.1342, found 445.1344.
DOI: 10.14272/yfmxadmhxaezba-uhfffaoysa-n/chmo0000630
title: infrared absorption spectroscopy (IR) (4-(2,3-dihydrothieno[3,4-b][1,4]dioxin-5-yl)-N,N-bis(4-methoxyphenyl)aniline)
title: InChI=1S/C26H23NO4S/c1-28-22-11-7-20(8-12-22)27(21-9-13-23(29-2)14-10-21)19-5-3-18(4-6-19)26-25-24(17-32-26)30-15-16-31-25/h3-14,17H,15-16H2,1-2H3

description: dataset for infrared absorption spectroscopy (IR) IR (ATR, ṽ) = 3108, 3034, 2928, 2836, 1604, 1500, 1489, 1460, 1453, 1436, 1421, 1364, 1317, 1289, 1276, 1264, 1237, 1191, 1177, 1169, 1143, 1109, 1069, 1030, 980, 953, 922, 901, 823, 781, 724, 707, 683, 671, 633, 601, 572, 562 cm–1.
DOI: 10.14272/yfmxadmhxaezba-uhfffaoysa-n.1
title: [4-(2,3-dihydrothieno[3,4-b][1,4]dioxin-5-yl)phenyl]-bis(4-methoxyphenyl)amine
title: InChI=1S/C26H23NO4S/c1-28-22-11-7-20(8-12-22)27(21-9-13-23(29-2)14-10-21)19-5-3-18(4-6-19)26-25-24(17-32-26)30-15-16-31-25/h3-14,17H,15-16H2,1-2H3

description: This is a physical chemical entity[CHEBI_24431] associated with a molecule[CHEBI_25367]. The molecule[CHEBI_25367] can be described by the following structural desciptors[cheminf_000085]: InChI descriptor[cheminf_000113]: InChI=1S/C26H23NO4S/c1-28-22-11-7-20(8-12-22)27(21-9-13-23(29-2)14-10-21)19-5-3-18(4-6-19)26-25-24(17-32-26)30-15-16-31-25/h3-14,17H,15-16H2,1-2H3, and canonical SMILES descriptor[cheminf_000007]: COc1ccc(cc1)N(c1ccc(cc1)OC)c1ccc(cc1)c1scc2c1OCCO2, and by the IUPAC name[cheminf_000107]: 4-(2,3-dihydrothieno[3,4-b][1,4]dioxin-5-yl)-N,N-bis(4-methoxyphenyl)aniline. The physical chemical entity[CHEBI_24431] has a component solvent[CHEBI_46787] which is described by the canonical SMILES descriptor[cheminf_000007]: The physical chemical entity[CHEBI_24431] has the following Sample ID as registered in the research data repository chemotion (www.chemotion-repository.net, https://doi.org/10.25504/FAIRsharing.iagXcR): CRS-28476 The physical chemical entity[CHEBI_24431] can be described by the physical descriptors [CHEMINF_000025]: Melting point descriptor[CHEMINF_000256]: 128.0 (°C) Boiling point descriptor[CHEMINF_000257]: Refractive index descriptor[CHEMINF_000253]: The physical chemical entity[CHEBI_24431] can be further described by the following assays[OBI:0000070][CHMO:0001133]: CHMO:0000593 | 1H nuclear magnetic resonance spectroscopy (1H NMR) CHMO:0000595 | 13C nuclear magnetic resonance spectroscopy (13C NMR) CHMO:0001146 | 1H--13C heteronuclear single quantum coherence (1H-13C HSQC) CHMO:0000470 | mass spectrometry (MS) CHMO:0000630 | infrared absorption spectroscopy (IR) The physical chemical entity[CHEBI_24431] was deposited to the Molecule Archive of the Karlsruhe Insitute of Technology (KIT) with the following Sample ID: Used ontologies: CHEBI - Chemical Entities of Biological Interest CHEMINF - chemical information ontology (information entities about chemical entities) CHMO - Chemical Methods Ontology OBI - Ontology for Biomedical Investigations
DOI: 10.14272/yfmxadmhxaezba-uhfffaoysa-n/chmo0001146
title: 1H--13C heteronuclear single quantum coherence (1H-13C HSQC) (4-(2,3-dihydrothieno[3,4-b][1,4]dioxin-5-yl)-N,N-bis(4-methoxyphenyl)aniline)
title: InChI=1S/C26H23NO4S/c1-28-22-11-7-20(8-12-22)27(21-9-13-23(29-2)14-10-21)19-5-3-18(4-6-19)26-25-24(17-32-26)30-15-16-31-25/h3-14,17H,15-16H2,1-2H3

description: dataset for 1H--13C heteronuclear single quantum coherence (1H-13C HSQC)
DOI: 10.14272/yfmxadmhxaezba-uhfffaoysa-n/chmo0000593
title: 1H nuclear magnetic resonance spectroscopy (1H NMR) (4-(2,3-dihydrothieno[3,4-b][1,4]dioxin-5-yl)-N,N-bis(4-methoxyphenyl)aniline)
title: InChI=1S/C26H23NO4S/c1-28-22-11-7-20(8-12-22)27(21-9-13-23(29-2)14-10-21)19-5-3-18(4-6-19)26-25-24(17-32-26)30-15-16-31-25/h3-14,17H,15-16H2,1-2H3

description: dataset for 1H nuclear magnetic resonance spectroscopy (1H NMR) 1H NMR (400 MHz, Dichloromethane-d2 [5.32 ppm], ppm) δ = 7.50–7.48 (m, 2H), 7.06–7.02 (m, 4H), 6.89–6.87 (m, 2H), 6.85–6.81 (m, 4H), 6.20 (s, 1H), 4.27–4.20 (m, 4H), 3.78 (s, 6H). Impurities: water from CD2Cl2 at 1.55 ppm, grease at 1.26 - 0.8 ppm.
DOI: 10.17609/smpm-3c34
title: Egress Behavior from Select NYC COVID-19 Exposed Health Facilities March-May 2020

description: This rapid response surveillance project was funded by the National Science Foundation (NSF) to collect "perishable" data on egress behaviors and neighborhood conditions surrounding healthcare centers (HCCs) in New York City (NYC) during the initial NYC COVID-19 PAUSE ordinance from March 22nd to May 19th, 2020.  Anonymized data on NYC HCC egress behaviors were collected by observational field workers using a phone-based mapping application.  Each egress trip record includes the day of week, time of day, destination category type, along with an array of behavioral outcome categories, ambient weather conditions and socio-economic factors.  Egress trajectories with precise estimates of distance traveled and the spatial dispersion or "spread" around each HCC were added via post-processing.  The data collection and cleaning process resulted in 5,030 individual egress records from 18 facilities.
description: This rapid response surveillance project was funded by the National Science Foundation (NSF) to collect "perishable" data on egress behaviors and neighborhood conditions surrounding healthcare centers (HCCs) in New York City (NYC) during the initial NYC COVID-19 PAUSE ordinance from March 22nd to May 19th, 2020.  Anonymized data on NYC HCC egress behaviors were collected by observational field workers using a phone-based mapping application.  Each egress trip record includes the day of week, time of day, destination category type, along with an array of behavioral outcome categories, ambient weather conditions and socio-economic factors.  Egress trajectories with precise estimates of distance traveled and the spatial dispersion or "spread" around each HCC were added via post-processing.  The data collection and cleaning process resulted in 5,030 individual egress records from 18 facilities. The study was funded for 9 weeks with student observers collecting data 10-20 hours per week until May 19, 2020.   Ultimately, 18 facilities across 4 of New York City's 5 boroughs (Queens, Brooklyn, Manhattan, and the Bronx) were selected.   Procedurally, field observers positioned themselves across the street from their assigned HCC egress location, and then traced each subject's egress route, noting locations of interactions with the built environment or other individuals.  Final destinations were categorized by location type (e.g., coffee shop, pharmacy, deli, food trucks), including whether subjects returned to the medical facility or entering a nearby one (e.g., temporary tent, adjacent clinic or campus building).  For each HCC, egress recordings extended from the same pre-specified point until one of three outcomes occurred: (1) the subject entered a vehicle, subway station, building or other final destination and was no longer visible, 2) tracking exceeded 20 minutes (an average observation period lasted 5 minutes in duration); or (3) the subject walked more than 1.3 km from the HCC.