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Jordan Thomas Mursinna

The Berkeley Pipeline Program

Demystifying the History PhD for Students from Historically Excluded Backgrounds

/ Article Archive

/ The Berkeley Pipeline Program

Publication Date

April 20, 2023

Perspectives Section

Graduate Education

S uccessfully entering a history PhD program requires a breadth of institutional knowledge that privileged applicants easily accumulate from connections, mentorship, and support. Students without those opportunities often face significant barriers to entry, not just during the application process but for years following enrollment. While history departments cannot change the historical and contemporary circumstances from whence the relative lack of diversity in history graduate programs emerges, they can identify and target specific hurdles that students of such backgrounds face at all phases of graduate school. One such effort is the pipeline program—a short, funded seminar series that provides training and guidance to PhD hopefuls who might otherwise lack the privileges that help many applicants succeed. Although the basic outline of a pipeline program is fairly simple, building one has proven to be nuanced and complex.

For undergraduates who do not already have familiarity with graduate education, applying to and succeeding in PhD programs is a daunting task. Pipeline programs like the one at the University of California, Berkeley, seek to close the information gap. Brian Cantoni/Flickr/CC BY 2.0

Since the spring of 2021, the history department at the University of California, Berkeley, has been hosting such a program . Now entering its third year, our program annually admits 15 students from across the country to a 10-week sequence of weekly remote seminars. These meetings cover a variety of topics regarding entering graduate school and succeeding within it. It concludes with a one-on-one mentorship phase in which fellows are paired with graduate students and faculty to workshop application materials or discuss their plans in more detail. While we continue to iterate and improve the program, what follows are some of the major design goals and challenges we faced in its inaugural run.

For a program intended to mentor students on successfully entering graduate school, the first challenge lay, somewhat ironically, in its own applications and admissions processes. Considering resource constraints and the importance of providing ample space for participants’ voices, we needed to limit the number of students in the program—but we also needed to be careful not to replicate in our selection process those same barriers to entry the program was designed to diminish. After extensive outreach to diverse colleges and universities across the country, we settled on the following requested materials: a writing sample, a statement of personal history, and a short piece on books or courses that had inspired them. The intention was to first establish an applicant’s potential for graduate admission, and then to determine the depth and earnestness of their interest in it. The goal throughout was to find an ideal middle ground—regrettably, we could not accommodate many capable students who didn’t seem to have given sufficiently serious thought to a history PhD, or some of the most impressive applicants, those who seemed motivated to take every possible opportunity to increase their chances of admission but were already well prepared.

In the end, we were overwhelmed by the response. The program had just 15 positions—a number that we felt would both enable a sense of community to emerge and assure participants the individual attention they deserved—but received 74 applications. Although the volume of applications was in part a result of our commitment to keeping the process accessible and undemanding, it serves dually as a testament to the demand for such programs that exists today.

We needed to be careful not to replicate in our selection process those same barriers to entry the program was designed to diminish.

The unanticipated need to select just one in five applicants presented another host of challenges. After an initial vetting, a committee of history faculty and graduate students made the final determinations. These were extraordinarily tough choices; our committee came to the selection meeting with independent rankings of the applicants, and some varied widely. There is no best practice here, but we think that pipeline programs run best with motivated students who readily demonstrate transparency in their uncertainties. Our priority thus lay in admitting sensitive, reflective, and passionate aspiring historians who showed both deep interest in pursuing graduate school and some form of trepidation about the process.

Those selected found that the pipeline program curriculum covers a wide variety of important topics. Some sessions focus on the more strategic elements of constructing a personal statement, requesting letters of recommendation, and introducing oneself to potential advisers, while others emphasize the importance of managing one’s mental health as a graduate student and developing techniques for dealing with imposter syndrome. This balance between guidance on entering graduate school and guidance on success following enrollment was crucial to our design team. The seminars are led by a rotating cohort of department faculty, staff, and graduate students, based on their expertise in each facet of the curriculum. The program is necessarily a collective effort of our entire department community.

This brief survey of our program’s design and aspirations understates the impact that working with our cohorts of fellows had on us in the past two years. Learning was not unidirectional. The team learned from weekly interactions with these aspiring historians, many of whom faculty usually only get the chance to meet through application packets that don’t always do justice to the sophistication of their historical voices. It’s been an enriching and humbling experience, one that program co-lead Waldo Martin perhaps best relayed by regularly referring to our Saturday-morning sessions as “going to church.”

This balance between guidance on entering graduate school and guidance on success following enrollment was crucial to our design team.

Feedback via anonymous surveys provides a measure of our success. Our fellows found the program to be an “invaluable experience” that “answered so many questions and relieved many anxieties” about the prospect of graduate school. Two other fellows independently described Saturday sessions as “something to look forward to each week” and as “the highlight of [their] week over the last few months.” Those words helped to strengthen our resolve to follow another fellow’s simple suggestion: “do not stop doing this program.”

Though fellows from our inaugural cohort have since enrolled in PhD programs at institutions including Princeton University, Stanford University, Brown University, and Northwestern University, we’re dually proud that the program has helped other students pursue a wide variety of career paths. More still have yet to apply: some of you serving on graduate admissions committees may see our fellows among your applicants this year. We here at Berkeley hope to see fellows from your undergraduate programs in the future.

The Berkeley History PhD Pipeline Program is one of many approaches to invigorating the historical discipline. Does your department or institution have a program that you think Perspectives readers might want to hear about? We welcome pitches on all aspects of the practice of history. Email [email protected] with your ideas and questions.

Jordan Thomas Mursinna is a PhD candidate at the University of California, Berkeley. He has served as a program coordinator for the Berkeley History PhD Pipeline Program since 2020.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Attribution must provide author name, article title, Perspectives on History , date of publication, and a link to this page. This license applies only to the article, not to text or images used here by permission.

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UC Berkeley Ph.D. Pipeline Program

UC Berkley have opened applications for their pipeline to Ph.D history program.

“The UC Berkeley History Ph.D. Pipeline Program provides general information and personalized guidance for prospective History Ph.D. students from historically excluded and underrepresented backgrounds. Funded by UC Berkeley’s Graduate Division and offered to participants free of charge, it features roughly a dozen Saturday seminars led by Berkeley faculty, staff, lecturers and graduate students over Zoom in the Spring semester and a program of individualized one-on-one mentorship during the subsequent summer.”

Apply here!

Applications for the program are due by February 15th. 

Michigan State University

College of Nursing

Get Connected:

Faculty/Staff Login:

Keeping the Ph.D. pipeline flowing

March 20, 2020

I recently moderated a panel discussion, hosted by the Big Ten Academic Alliance (BTAA) nursing deans on “The Ph.D. Pipeline in Nursing: Sustaining the Science in the MNRS Schools.” The panel discussion is a continuation of one hosted by the American Association of Colleges of Nursing (AACN) and the National Institute of Nursing Research (NINR).

The initial AACN-NINR discussion was prompted by concern over the decreasing enrollments in research-focused doctoral degree programs. A key focus of that discussion was “sustaining the science.” Nursing has made tremendous progress in “developing the science” through the preparation of nurse researchers (or “scientists;” I feel we should claim that role).The goal of the discussion was to analyze contributing factors surrounding the recent decrease in enrollments and to identify steps to increase interest in Ph.D. programs. AACN and NINR subsequently issued a report, “The PhD Pipeline in Nursing: Sustaining the Science,” as a way to generate continued discussion.

In a follow-up discussion, deans at the Spring AACN meeting suggested three recommendations for increasing enrollments:

• Early exposure to research
• Financial support
• Developing pathways to the Ph.D.

These three recommendations formed the foundation for the panel discussion at a Midwest Nursing Research Society (MNRS) event hosted by BTAA deans. The deans were gratified by the packed room and lively discussion, which included a great deal of items for consideration.

Several of the items – there were too many mentioned to include in this post – are things that we are either already working on or exploring in the MSU College of Nursing in an effort to increase Ph.D. enrollment while “sustaining the science.” These items are included in the first two aforementioned recommendations: “early exposure to research” and “financial support.”

Early Exposure to research: We are already working on three ways to meet this recommendation. First, we are working with the Honors College to expand our honors program options to BSN students who may be interested in research. This approach provides students with practical experience of faculty research while developing their own topics of interest.

Second, we want to think of ways to include high school students, in addition to those in the BSN program, in faculty research. This option has the additional possibility of attracting students to the profession who may not have considered all nursing career opportunities.

Lastly, we are currently enrolling students who have completed the BSN either directly into the Ph.D. program or while they are in practice. With the growth of DNP graduates and decrease in the MSN option, we anticipate more BSN- or DNP-prepared applicants to Ph.D. programs. 

In addition, there are three more areas within this recommendation that we should explore further:

• Developing clinically relevant research of importance to our practice partners
• Partnering with colleagues and students associated with non-research level colleagues and universities
• Exploring options for exposing students to team science

Financial Support:  Enrolling in a Ph.D. program is an investment, not just for the student, but for the college and discipline. 

It’s an investment in the growth and development of individuals who can contribute, not just to “sustaining the science,” but to continue growing the science. Of course, this requires money. We currently provide two-year research/graduate assistantships, limited to Ph.D. nursing students, that provide some support for matriculation.

Our teaching workload policy also provides an incentive for faculty to seek financial support for students in the Ph.D. program by recognizing it as a formal teaching activity. Other suggestions that may be useful to explore are providing a monetary amount to students and to faculty as an incentive to work with students, revisiting the program length, and considering part-time options. Currently, there are not support options for part-time students.

In summary, we are at a critical point in the development of preparing Ph.D. nurses to sustain and grow the science. Increasingly, I hear from CEOs, politicians and health care field that the inclusion -- and leadership -- of nurses will be a major driver in the transformation of the industry. Nurses must be “at the table,” and a key to this transformation will be the “sustaining, and continuing development, of the science.”  

Yours in Spartan Spirit,

Randolph F. R. Rasch , PhD, RN, FNP, FAANP

Dean and Professor

• DOI: 10.1109/MC.2015.147
• Corpus ID: 8485161

The PhD Pipeline

• Susanne E. Hambrusch , R. Libeskind-Hadas
• Published in Computer 22 May 2015
• Computer Science, Education

5 References

Exploring the baccalaureate origin of domestic ph.d. students in computing fields, related papers.

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• Editor's Pick

A Broken Pipeline: Minority Students and the Pathway to the Ph.D.

When Ph.D. student Hugo Vega-Ramirez first considered dropping out of graduate school, his motivation to soldier on came from an unlikely place: a pig farm in West Virginia.

Vega-Ramirez, the son of Mexican immigrants, traded his neuroscience lab for the open road. He decided to embark on a cross-country road trip with his boyfriend en route to his hometown in California, stopping at a network of farms and exchanging labor for food and shelter. The rigor of his second year in Harvard’s neuroscience doctoral program, coupled with immense feelings of isolation and homesickness, led him to take nine months off school.

Vega-Ramirez said he was inspired by the farmers he met across the country who were devoted to their work and felt they were carrying out their calling. He returned to Harvard the following spring feeling refreshed and ready to research again in his lab.

“My time off really helped me do away with all that noise in my head about feelings of incompetence, or not feeling like I really should be here, or that I was here because I’m Mexican,” Vega-Ramirez said.

His experience is not uncommon for minority students seeking Ph.D.s. While a high percentage of all Ph.D. candidates at the national level drop out of graduate school, attrition rates among underrepresented minority students are generally even higher. Thirty-six percent of underrepresented minority students—defined by the Council of Graduate Schools, a graduate education research organization, as African-Americans, American Indians/Alaskan Natives, and Hispanics—drop out of their programs, while 20 percent have not completed their degree after seven years.

At Harvard’s Graduate School of Arts and Sciences, minority students are few in number. Eight percent of GSAS students are underrepresented minorities, according to the school’s Associate Dean for Academic Programs and Diversity Sheila Thomas. That figure includes African Americans, Mexican Americans and Puerto Ricans—a narrower definition of Hispanic—and American Indians/Alaskan Natives. Eleven percent of the newly-admitted GSAS candidates are underrepresented minorities, according to Thomas.

But while underrepresented minorities represent a small percentage of GSAS—and those who are here often struggle to stay afloat—administrators and individual departments are actively seeking to bolster their numbers and experience at Harvard.

HOME AT HARVARD?

After Vega-Ramirez moved from California to Cambridge to begin his first year at Harvard, he began missing home and felt overwhelmed with work, as though he could not keep up with the pace of both the city and the school. He withdrew from his peers and said he found it hard to form friendships that first year.

“The way I dealt with it was to try to do it all by myself,” Vega-Ramirez said. “I was like, ‘if I open up to somebody, I’m going to sound ridiculous.’”

Many minority students see their first glimpse of life at GSAS at a visiting weekend hosted specifically for minority students. In fact, Ph.D. student Sergine Brutus, a Haitian immigrant, said that weekend alone “solidified” her decision to choose Harvard. She remembers spending time with passionate, minority students, and feeling like she belonged.

But once students move to campus in the fall, that rosy picture of life as a graduate student can fade. Some minority students enrolled in Harvard’s doctoral programs said because they were few in number and seemed to lack a strong support system, they soon began grappling with the same feelings Vega-Ramirez experienced.

“It was like a culture shock,” Ph.D. student Karina N. Gonzalez Herrera said, who spoke of the “gap” she felt socially and academically after coming to Harvard from a small state school in California to study Biological and Biomedical Sciences. “I felt more comfortable with other people who were like me or similar to me.”

Many students said they felt more comfortable reaching out to peers, rather than professors, to seek help, but said they would have found it easier to open up to a minority faculty member.

“[They] sort of understand you’re struggling in a different kind of way,” Ph.D. student Khytie K. Brown said, referring to minority faculty members, many of whom experienced the same struggles as current graduate students. “Or when they do see potential, they almost feel like it’s more of their duty to respond to that.”

Vega-Ramirez said that when he eventually reached out to his classmates for support, he found that some also shared his feelings of self-doubt and inadequacy, which he calls “imposter syndrome.” Such feelings can be amplified in minority students who, like Vega-Ramirez, are the first in their families to attend college, let alone graduate school.

When Gonzalez struggled to find a community of Latino students her first year, she decided to create her own. She said she noticed that there was no minority organization for science students, and worked with Thomas, the dean for diversity, to restart the then-defunct Minority Biomedical Scientists of Harvard.

GSAS has several other student-led cultural groups in Cambridge, including the W.E.B. Du Bois Society, an umbrella organization for all minority students that is also open to students of every background.

Brown, who sits on the steering committee for the Du Bois Society, said although the group provides a much-needed space for minority students, she “wouldn’t say it’s a community.” She added leaders are attempting to rebrand the group, as its name often incorrectly signifies to some students that it is solely focused on African-American students.

Similarly, students began organizing a GSAS Latinx Student Association last fall, and obtained funding from the Graduate Student Council and formal recognition this semester.

Aside from cultural organizations and affinity groups, graduate students primarily find support within their department, cohort, or lab—which reflects the decentralized nature of GSAS. Psychology Ph.D. student Sa-kiera T. J. Hudson said she feels fortunate to have diverse labmates and an African-American adviser.

Aside from race, Hudson said socioeconomic status also contributes to feelings of isolation. Some of Hudson’s peers have parents who help them pay rent or take care of other financial obligations, while she helps support her family back home, she said.

“That is a burden that [Harvard] doesn’t ever see; your advisers don’t even see. It’s like, ‘well you’re just supposed to be doing your work,’ and I’m like, ‘I can’t do my work when I have to field calls from my family, or I’m giving up some of my stipend to give to my parents because they need the money,” Hudson said.

FIXING A LEAKY PIPELINE

Graduate school is not the first time underrepresented minority students experience feelings of pressure and isolation in an academic setting. These issues define what many academics call the “ pipeline problem ” whereby minority students progress through their educational career towards a tenure-track professorship.

When assessing rates of attrition among minority graduate students, points of divergence early within the pipeline, such as deciding one’s field of focus or deliberating on post-college plans, can make a large difference.

Parents can play an important role in those decisions because they often pressure their children to choose a field that is practical and financially secure

“A lot of black parents, particularly immigrant parents, their mentality is ‘you need to go to law school,’ ‘you need to go to med school,’” Brown, who is originally from Jamaica and studies in the African and African American Studies Department, said. “They understand that more than getting a Ph.D. in history.”

Dean for diversity Thomas said her own parents, who are Indian immigrants, had emphasized math and science education, and more importantly, the physician profession.

“Often a student coming from a community of color, you’re trying to think about ‘what can I do to help my community,’” Thomas added. “Being a physician, being a lawyer—there’s a very obvious ‘how to give back’ component.”

Some parents and students don’t even know what a Ph.D. entails, students said.

“The graduate life doesn’t make any sense to my family,” Hudson said. “Not having to explain it to people is a form of privilege that a lot of people don’t recognize—just how draining it can be when the people who are close to you, who want to give support, literally don't know what you’re doing.”

One way to combat problems with minority student attrition pursuing academic careers is to hire more underrepresented minority professors. Some students said the presence of minority faculty members allows them to imagine themselves potentially holding such a position.

“One of the things they do is to model—subtly, silently—what it is to be a professor from an underrepresented minority group,” English Department director of graduate studies Daniel G. Donoghue said.

Although the mere presence of minority professors is important, Donoghue said students and faculty need to establish a personal connection at the undergraduate level.

“What it takes is an individual professor having a particular kind of relationship with a student, and to say ‘look this is really good, you should think about graduate school.’ Sometimes students need to hear that,” he said.

Brown said it was important that she heard encouragement from a professor.

“The whole reason I ended up being a graduate student, I think since undergrad, it was having faculty of color who took special interest in me,” Brown said. “It was like ‘oh I think you’re really smart, have you ever thought about getting a Ph.D.?’”

In addition to personal faculty encouragement, programs across the country that aim to increase diversity in higher education can also open the door to graduate school. Many minority graduate students interviewed partook in such programs.

GSAS administrators, too, recognize the importance of programs that repair problems with the pipeline. Several Harvard programs allow undergraduate students interested in the sciences to stay on campus for summer research, pairing them with Ph.D. students and professors for informal mentoring. Harvard offers a similar program for students interested in the humanities and social sciences.

Additionally, GSAS created post-baccalaureate programs open to non-Harvard students as an effort to get more minority students to pursue a Ph.D.

“We cannot see this as, if a student doesn’t come to Harvard, that this is a failure,” Thomas said. “It’s about changing the whole national landscape, not just Harvard’s landscape.”

WIDENING THE POOL

As GSAS focuses more on ways to increase diversity within its Ph.D. programs, administrators and faculty increasingly place emphasis on holistic evaluation in the admissions process.

Each of the 56 departments admits candidates separately. Many departments, including History and Philosophy, require two members of their admissions committees to read minority student applications, “so there’s no possibility that any minority candidate can be missed,” History Department chair David R. Armitage said.

This year, of the 21 students the History Department accepted, five are underrepresented minorities, the highest in the last decade, Armitage said. He added that the quality of the incoming class is just as high as always, and that no “special reservations” were made for any candidates.

Although the departments fully scrutinize every application, committees take extra care to avoid implicit biases when deliberating on minority student applications.

“You figure, ok, where do you want to spend that extra effort?” Philosophy Department chair Edward J. Hall said. “And given that we think it’s a priority to get more underrepresented minorities into Philosophy, and into positions of professional prominence, because that can only help with the pipeline issue down the line, it seems like a good idea to spend the extra effort on that pool.”

But that pool is often very small, particularly within humanities fields, so departments have altered their admissions processes to expand the numbers. For the first time this admissions cycle, Donoghue worked with other humanities departments—namely African and African American Studies, American Studies, Comparative Literature, and Romance Languages— to share applications of underrepresented minorities who were not selected for the program to which they applied, but who may find a fit within a different humanities department.

Donoghue said graduate students prompted the initiative by calling on faculty to increase the diversity in the English Department.

“I can’t say ‘oh, we would have done this anyway if the graduate students hadn’t said what they said,’ but it’s also the case that the graduate students weren’t talking to a group that really needed a lot of convincing,” Donoghue said. “There was a general meeting of minds here.”

Donoghue added that this process of sharing applications could be expanded to include non-minority students.

Once the admissions cycle is complete, departments must also work to convince minority students to accept Harvard’s offer. This year, professors personally reached out to students to sell them on the English Department. For example, University professor Henry Louis Gates, Jr. called the three African-American students admitted to the English Department to offer his personal congratulations and encourage them to attend Harvard in the fall. All three accepted their offer.

“That was a very crucial and even touching moment for these students, to have somebody with his stature to reach out to them,” Donoghue said.

Still, some departments have found it difficult to enhance diversity. The Philosophy Department has not only struggled with attracting women. Hall said he considers Philosophy “hands down the worst of the humanities” for gender diversity.

This year, Philosophy's first-year class of six students includes two underrepresented minority students—an African-American and a black student from England—which Hall called “unusual” for the department.

“Who knows if this is just a local blip, but I think we like to think it’s partly because of recent efforts and outreach,” Hall said.

Some students say they think Economics, a field that across the country generally struggles to attract minorities, is not an adequately diverse department. Eleven percent of admitted students this year were underrepresented minorities, compared to about 24 percent in the History Department.

Economics Department chair David I. Laibson ’88 said he has had conversations with Mahzarin R. Banaji, a Psychology professor who studies the effect of implicit bias, about how to search broadly for faculty members. The department applies those conversations to its graduate admissions process, he said.

After a strong pool of minority applicants are accepted and decide to come to Harvard, many recognize the impact their presence has on undergraduates considering an advanced degree.

“Being a black student in Psychology, I’ve had research assistants come up to me and go like, ‘Hi! I want to do work with you. I think the work that you do is cool, but also, I don’t see too many black people as graduate students willing to take on RAs,” Hudson said.

Hudson took it upon herself to hold weekly lab meetings with her minority research assistants, where she discusses graduate school and why they might want to pursue a Ph.D. Two of her former research assistants are now in Ph.D. programs at Cornell and Tufts, she said.

Brutus also spoke of giving back to aspiring academics, saying that minority students who are among the only ones in their programs should understand their role as a “conduit for someone in the future.”

—Staff writer Leah S. Yared can be reached at [email protected]. Follow her on Twitter @Leah_Yared .

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Masters to PhD pipeline

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Doctor of Philosophy in Petroleum Engineering (Ph.D.)

Admission to the University of Houston Ph.D. Program in Petroleum Engineering will require the following:

• GRE — An acceptable score within the Cullen College of Engineering standard on the Verbal, Quantitative, and Analytical portions of the Graduate Record Examination.
• LETTERS — Three letters of recommendation from recognized academic or industyr professionals who can attest to the applicant’s capability for independent and creative thinking for the graduate level research in petroleum engineering.
• STATEMENT — A written statement of the applicant’s professional goals.
• APPROVAL — Approval by the Petroleum Engineering Graduate Committee.

In addition, students required to submit a valid TOEFL/IELTS score, must achieve the minimum Cullen College of Engineering requirement. Click here to learn more about the English Language Proficiency Requirements.

Degree Requirements

• M.S. to Ph.D. — Obtaining an M.S. degree first at UH (or elsewhere).

M.S. degrees must have been obtained from an accredited institution in Petroleum Engineering or a relevant engineering or scientific discipline. The applicants must maintain a Grade Point Average of 3.5/4.0.

The chart below demonstrates course requirements for the path to PhD.

Students without sufficient background for the required courses must complete leveling courses that will not count toward the degree. Each Ph.D. student must maintain a Grade Point Average (GPA) above 3.50 throughout the Ph.D. program.

Candidacy/Dissertation

The Ph.D. in Petroleum Engineering will require a dissertation.

• Ph.D. Dissertation Committee: During the third semester after the student is enrolled in the Ph.D. program, a dissertation committee will be formed by the student and advisor with approval of the Chair of the Petroleum Engineering Department. The Chair of the Dissertation Committee will be a tenured, tenure-track or research faculty member associated with the Petroleum Engineering Department. The committee shall consist of at least five members including the Chair of the Committee. Among the committee members, a minimum of three members should be associated with Petroleum Engineering Department and two members can be from outside the Department. Members selected from industry should be actively involved in research and will require the approval of the Associate Dean of the College of Engineering to become a committee member.
• Drilling & Completion
• Production Engineering
• Reservoir Engineering
• Formation Evaluation & Reservoir Characterization
• Students will be asked to officially select their 3 exams and give notification by email approximately 3 months prior to the QE.
• Authors of the exams will provide study guide materials to all exam takers as soon as the students commit to the QE for a certain date. There may be review sessions scheduled by the examiners.
• Students may not take all 4 exams
• If a student passes all 3 exams — they become a Candidate
• If a student passes 2 exams and receives Conditional for the 3rd exam — they do not become a Candidate until the stipulations of the QE Committee are completed. Official notice will be sent to each exam taker.
• If a student passes 2 out of 3 exams and fails one exam — they need only re-take one exam by either sitting for an oral exam 2 weeks after the original exam or taking the written exam at the next available QE testing date. Students may not switch subjects after failing a QE.
• If a student fails 2 exams out of 3 — they are considered to fail. They must take all 3 of the written exam at the next available QE testing date. Students may not switch subjects after failing a QE.
• If a student fails any one QE exam twice — they are subject to dismissal from the PHD program. Sometimes the option to become a Master Degree Student may be offered after dismissal from the PHD program — this is not a guaranteed offer.
• QE exams will be graded and reviewed by committee and results given to students by the first day of class following the term the exam preceded.
• Exams will be archived by the department according to the UH Records and Retention Policy.
• Proposal Defense : is the ititial dissertation proposal defense/oral presentation given after the student passes the Qualifying Exam and becomes a candidate. The student is required to write a dissertation proposal of his/her proposed research to the dissertation committee members. After the submission of the dissertation proposal, the student must give a public oral presentation of the proposed research to the dissertation committee. Each committee member must indicate acceptance of the preliminary examination with “Satisfactory” or “Unsatisfactory”. Typically, it is held no later than two semesters before a students plan to graduate. A unanimous “Satisfactory” by the committee members is required.
• Dissertation Defense: The Ph.D. candidate must provide the committee members with the final version of the dissertation after the completion of his or her research. After consultation with the committee members, the Ph.D. candidate will defend his or her dissertation at a public oral defense. The dissertation committee will make the final judgment of the acceptance of the dissertation defense and the contribution of the work to the existing knowledge base of petroleum engineering.

Of the Total Courses needed for degree, two can be selected outside of the Petroleum Engineering Department Course Offerings. Students will need their advisors consent.

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We have 13 Engineering (pipeline) PhD Projects, Programmes & Scholarships

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Engineering (pipeline) PhD Projects, Programmes & Scholarships

Gaseous pipeline transport and its underground porous media storage for net zero innovation, phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Self-Funded PhD Students Only

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

Investigation of hydrogen embrittlement of steels for the hydrogen transport energy future

Use of artificial intelligence in imaging in obstetrics and gynaecology, funded phd project (students worldwide).

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Fully funded 3-year Ph.D. Position in Smart Water Technologies, University of Canterbury, New Zealand

Self-funded 3.5-year phd – automatic segmentation of femur using deep learning combined with phantomless calibration for rapid personalised fracture risk predictions in clinical applications, hot spots and pressure points: developing ai to identify normal and pathological periprosthetic osteoblastic activity for total knee arthroplasty on spect/ct, competition funded phd project (students worldwide).

This project is in competition for funding with other projects. Usually the project which receives the best applicant will be successful. Unsuccessful projects may still go ahead as self-funded opportunities. Applications for the project are welcome from all suitably qualified candidates, but potential funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

PhD in Mechanical Engineering - A Digital Twin-based approach for Nuclear Reactor Design and Prognosis

Funded phd project (uk students only).

This research project has funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

Unravelling the role of corrosion products in the localised corrosion of large scale energy system infrastructure

Remote retrieval of evidence using robotic systems, phd in cyber-physical systems for medicine development and manufacturing, full scholarship at epsrc cdt in advanced engineering for personalised surgery & intervention, funded phd programme (students worldwide).

Some or all of the PhD opportunities in this programme have funding attached. Applications for this programme are welcome from suitably qualified candidates worldwide. Funding may only be available to a limited set of nationalities and you should read the full programme details for further information.

EPSRC Centre for Doctoral Training

EPSRC Centres for Doctoral Training conduct research and training in priority areas funded by the UK Engineering and Physical Sciences Research Council. Potential PhD topics are usually defined in advance. Students may receive additional training and development opportunities as part of their programme.

Identification and characterisation of liquids using ultrasound and machine learning

Uk atomic energy authority sponsored phd scholarship – developing a miniaturised end-effector for repair in confined spaces (f0200915).

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What are Data Pipelines? (Complete Guide)

Written By: Cheyenne Kolosky

• August 28, 2024
• Build an OEM Partner Portal with No-Code (Tutorial)
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Businesses and organizations rely on a steady flow of information to make informed decisions, optimize operations, and stay competitive. However, the journey from raw data to actionable insights is complex, often involving multiple stages and technologies working together seamlessly. This is where data pipelines come into play, acting as the backbone that ensures data is collected, transformed, and delivered to the right place at the right time. Understanding how these pipelines work and their significance in the modern data landscape is crucial for anyone looking to harness the full potential of their data.

Key Takeaways

• Data pipelines are structured systems that automate the flow of data from various sources through a series of processing stages, ultimately delivering it to a destination for analysis or storage. They are important because they enable efficient data management, ensuring that accurate and timely insights can be derived from vast amounts of data to support informed decision-making.
• Exploring various types and architectures of data pipelines is essential because each organization has unique data processing requirements and workflows that can significantly impact performance and efficiency. 
• The three key components of data pipelines are data ingestion, transformation, and storage. Together, these components facilitate the seamless flow of data through the pipeline, enabling organizations to derive actionable insights.

What Are Data Pipelines?

Data pipelines are a series of automated processes that transport data from one system or stage to another, transforming and organizing it along the way to ensure it is clean, consistent, and ready for analysis. 

They begin with the extraction of raw data from various sources, which is then processed through a sequence of steps such as data cleaning, filtering, aggregation, and enrichment. 

Finally, the refined data is loaded into a destination system, such as a data warehouse, where it can be accessed and used for reporting, analytics, or machine learning. Data pipelines are essential for managing large volumes of data efficiently and ensuring that the right data is available to the right people at the right time.

Related: Data Consolidation: How to Enhance Operational Efficiency

Why Are Data Pipelines Important in Data Management?

Data pipelines are crucial because they enable organizations to handle vast amounts of data efficiently and effectively, ensuring that information flows smoothly from its source to its final destination. In an era where timely and accurate data is the lifeblood of decision-making, data pipelines automate the complex processes of collecting, transforming, and delivering data, reducing the risk of errors and delays. This automation not only saves time and resources but also ensures that data is consistently accurate and up-to-date, allowing businesses to derive valuable insights, improve operations, and gain a competitive edge. Without robust data pipelines, managing and leveraging data at scale would be significantly more challenging.

Benefits of Data Pipelines

In an increasingly data-driven world, the ability to efficiently manage and utilize large volumes of data is a key differentiator for successful organizations. Here are some of the key benefits of implementing data pipelines:

• Efficiency and Scalability: Data pipelines automate data collection, transformation, and loading, allowing organizations to handle large volumes of data with minimal manual intervention.
• Consistency and Accuracy: By standardizing the process of data transformation and cleaning, data pipelines help ensure that the data used across the organization is consistent and accurate. 
• Real-Time Data Processing: Data pipelines can be designed to handle real-time data, enabling organizations to make faster decisions based on the most current information available.
• Flexibility: Data pipelines can be customized to meet the specific needs of an organization, allowing for the integration of various data sources and the application of tailored transformation processes.
• Improved Data Governance: Data pipelines facilitate better data governance by providing a clear and auditable path for data as it moves through different stages of processing. This transparency helps organizations comply with data regulations and maintain high standards of data quality.
• Enhanced Decision-Making: With reliable and timely data at their disposal, organizations can make more informed decisions, driving better business outcomes.

Types of Data Pipelines

There are two commonly used types of data pipelines: batch processing and streaming processing. Let’s take a closer look at each one.

Batch Processing Pipelines

Batch processing data pipelines handle data in large, grouped segments, or “batches,” rather than processing each data point individually in real-time. In these pipelines, data is collected over a specific period and then processed in bulk at scheduled intervals, such as hourly, daily, or weekly. This approach is particularly useful when dealing with large volumes of data that do not require immediate processing, as it allows organizations to manage and analyze significant amounts of data efficiently without overwhelming system resources.

Batch processing data pipelines are commonly used in scenarios where data latency is acceptable, such as generating end-of-day reports, processing payroll, or analyzing historical data trends. They are also effective for aggregating and transforming data from multiple sources before loading it into a data warehouse or data lake for further analysis. By processing data in batches, organizations can optimize system performance and reduce the complexity and cost associated with real-time data processing, making it a practical solution for many business applications.

Streaming Processing Pipelines

Streaming processing data pipelines handle data in real-time, processing each data point as it is generated or received rather than waiting to process data in bulk. In these pipelines, data flows continuously through the system, allowing for immediate analysis and action. This approach is essential for scenarios where timely insights and rapid responses are critical, such as tracking real-time user activity on websites or managing sensor data in Internet of Things (IoT) applications.

Streaming processing data pipelines are particularly valuable in environments where data is produced at high velocity, and decisions need to be made quickly. For instance, in the financial industry, streaming pipelines can detect suspicious activity and trigger alerts within milliseconds. In e-commerce, they can personalize user experiences in real-time based on current behavior. By enabling organizations to process and react to data as it arrives, streaming processing data pipelines provide the agility and responsiveness needed in dynamic, fast-paced industries.

Related: Table Relationships for Data Harmony & Efficiency

ETL Pipeline vs. ELT Pipeline?

When managing data pipelines, understanding the differences between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) is crucial for selecting the right approach for your organization’s needs.

ETL Pipelines 

ETL (Extract, Transform, Load) is a data integration process that involves extracting data from various sources, transforming it into a desired format or structure, and then loading it into a target database or data warehouse. During the transformation phase, data is cleaned, filtered, aggregated, or enriched to ensure it meets the specific needs of the target system and is consistent, accurate, and usable. ETL pipelines are typically used in scenarios where data quality and structure are critical before loading, making it ideal for traditional data warehouses where pre-processed, structured data is required for reporting and analysis.

ELT Pipelines 

ELT (Extract, Load, Transform) is a data integration process in which data is first extracted from various sources and then loaded directly into a target database or data lake. The data is transformed after it has been loaded using the processing power of the target system. This approach is well-suited for modern data environments, such as cloud-based data lakes, where large volumes of raw data can be stored and processed at scale. ELT pipelines allow for greater flexibility and faster data loading since the transformation step is deferred, making it ideal for big data and real-time analytics, where raw data can be transformed as needed for specific use cases.

Key Components of Data Pipelines

Data pipelines consist of three main components: data ingestion, transformation, and storage. Each component is crucial for developing and maintaining effective pipelines.

Data Ingestion

Data ingestion is a critical component of a data pipeline, responsible for collecting and importing raw data from various sources into a centralized system for further processing. This step involves capturing data from different formats and sources, such as databases, APIs, sensors, and files, and moving it into the pipeline for subsequent transformation and analysis. 

Effective data ingestion ensures that data is consistently and accurately transferred, regardless of its origin, and can handle both batch and real-time data streams depending on the pipeline’s design. By efficiently managing the flow of data into the pipeline, data ingestion sets the foundation for all downstream processing, ensuring that the right data is available at the right time for decision-making and analytics.

Data Transformation

Data transformation involves converting raw data into a structured and usable format tailored to the needs of the target system or analysis. During this stage, data is cleaned, normalized, aggregated, enriched, and formatted to ensure consistency, accuracy, and relevance. 

Transformation can involve various processes such as filtering out unnecessary data, converting data types, merging datasets, and applying business rules or calculations. This step is essential for making data meaningful and actionable, as it prepares the raw input into a form that can be effectively utilized by analytics tools, machine learning models, or reporting systems.

Data Storage

Data storage involves the preservation and organization of processed data in a suitable format and location for future access and analysis. Once data has undergone extraction and transformation, it is loaded into a target storage system, which can range from traditional databases and data warehouses to modern cloud-based data lakes. 

The choice of storage solution depends on various factors, including data volume, structure, access patterns, and analytical requirements. Effective data storage ensures that data is not only secure and easily retrievable but also optimized for performance and scalability. Data storage plays a crucial role in enabling organizations to leverage their data for insights, reporting, and informed decision-making.

Modern Data Pipeline Architectures

Modern data pipeline architectures are structured frameworks and designs that dictate how data flows from its source through various processing stages to its final storage and analysis. As organizations increasingly adopt cloud-based solutions, machine learning, and big data technologies, data pipeline architectures have evolved to support diverse data processing needs and improve scalability, reliability, and flexibility. 

The 2 most common modern data pipeline architectures are:

Lambda Architecture

Lambda architecture is a data processing framework designed to handle large volumes of data by combining both batch and real-time processing methods. It consists of three main layers:

• Batch Layer: This layer manages comprehensive, historical data processing and stores the master dataset.
• Speed Layer: This layer handles real-time data processing to provide immediate insights and updates.
• Serving Layer: This layer merges the results from both the batch and speed layers to deliver a unified view of the data for analysis and reporting.

This architecture enables organizations to benefit from the accuracy and completeness of batch processing while also capturing the timeliness and responsiveness of real-time analytics. It is particularly effective for scenarios requiring both comprehensive historical insights and immediate operational intelligence.

Serverless Architectures

Serverless architectures are designed to simplify data processing by allowing organizations to build and manage data pipelines without provisioning or managing servers. In this model, cloud providers handle the underlying infrastructure, automatically scaling resources up or down based on the workload and usage patterns.

Serverless architectures typically leverage event-driven computing, where data processing tasks are triggered by specific events, such as the arrival of new data or changes in data state. This approach enables organizations to focus on developing and deploying their data processing logic without worrying about server maintenance, capacity planning, or infrastructure costs. As a result, serverless data pipeline architectures enhance agility, reduce operational overhead, and allow for seamless integration with various data sources and services.

Common Data Pipeline Use Cases

Data pipelines play a vital role across various industries by facilitating the seamless flow of data from multiple sources to actionable insights. Some common use cases include:

• E-commerce: Data pipelines can analyze customer behavior and transaction data in real-time, allowing businesses to personalize recommendations, optimize pricing strategies, and enhance the overall shopping experience.
• Healthcare: In the healthcare industry, data pipelines are used to aggregate patient data from multiple sources, such as electronic health records, wearables, and lab results, enabling real-time monitoring, predictive analytics, and improved patient care.
• Finance: Financial institutions utilize data pipelines to monitor transactions for fraud detection, analyze market trends for risk assessment, and generate timely reports for regulatory compliance.
• Manufacturing: Data pipelines help manufacturers collect and analyze data from sensors and machines on the production floor, enabling predictive maintenance, quality control, and operational optimization.
• Retail: Retailers implement data pipelines to track inventory levels, sales data, and customer preferences, facilitating efficient supply chain management and targeted marketing strategies.

Tips and Tricks for Designing and Managing Data Pipelines

With the increasing complexity of data environments and the growing volume of data being handled, having a strategic approach to pipeline design and management can significantly enhance performance and reliability. In this section, we will explore valuable tips and tricks that can help you optimize your data pipelines, streamline operations, and address common challenges.

Scalability Strategies

Ensuring that your data pipeline can scale effectively with growing data needs is crucial for maintaining performance and reliability as your organization expands. Here are some key tips to achieve this:

• Choose a Scalable Architecture: Opt for a data pipeline architecture that supports scalability, such as serverless or microservices-based architectures, which can easily adapt to fluctuating data loads and user demands.
• Utilize Cloud Services: Leverage cloud-based data storage and processing solutions that offer built-in scalability features. Cloud providers can automatically allocate resources based on demand, allowing your pipeline to grow seamlessly.
• Optimize Data Processing: Implement efficient data processing techniques, such as parallel processing or partitioning, to enable faster handling of large datasets. This can significantly reduce bottlenecks during peak data loads.
• Implement Caching Strategies: Use caching mechanisms to store frequently accessed data temporarily. This reduces the load on the pipeline by allowing quicker access to common queries and minimizing repetitive processing.
• Monitor Performance Metrics: Regularly monitor key performance metrics, such as data throughput, processing times, and resource utilization. This allows you to identify potential bottlenecks and address them before they become critical issues.
• Design for Flexibility: Build your data pipeline with flexibility in mind, allowing for easy integration of new data sources and changes in processing requirements. This adaptability ensures that your pipeline can evolve alongside your data needs.
• Leverage Data Lakes: Consider using a data lake architecture to store raw and structured data. This allows you to scale storage independently from processing, accommodating large volumes of data without performance degradation.
• Automate Scaling Processes: Implement automation tools to dynamically scale resources based on data volume and processing requirements. This ensures that your pipeline can handle sudden spikes in data without manual intervention.

Fault Tolerance

Designing data pipelines that can handle failures gracefully is essential for maintaining data integrity and ensuring continuous operation in the face of unexpected issues. Some key best practices to consider are:

• Implement Retry Mechanisms: Design your pipeline to automatically retry failed tasks or operations a set number of times before escalating the issue. This can help recover from transient errors without manual intervention.
• Use Idempotent Operations: Ensure that operations in your pipeline are idempotent, meaning they can be safely retried without causing unintended side effects. This reduces the risk of data corruption and ensures consistency.
• Incorporate Data Validation: Include data validation checks at various stages of the pipeline to catch errors early. This can prevent bad data from propagating through the pipeline and causing further issues.
• Design for Isolation: Structure your pipeline components to be loosely coupled, allowing individual components to fail without affecting the entire pipeline. This isolation enables easier troubleshooting and recovery.
• Implement Monitoring and Alerts: Set up robust monitoring and alerting systems to track pipeline performance and catch errors in real-time. Prompt notifications can help your team address issues quickly and minimize impact.
• Maintain Comprehensive Logging: Implement detailed logging throughout your data pipeline to capture relevant information about each processing step. This can aid in diagnosing failures and understanding the root causes of issues.
• Create Backups and Snapshots: Regularly create backups and snapshots of your data at critical stages. In the event of a failure, you can restore data to a previous state, minimizing data loss.
• Design for Graceful Degradation: Plan for scenarios where certain components may fail. Ensure that the pipeline can continue to function, even if some features or data may be temporarily unavailable, providing essential services without complete interruption.
• Perform Regular Testing: Conduct failure and recovery testing to evaluate how well your pipeline handles errors. Simulate various failure scenarios to ensure that your pipeline can recover smoothly and effectively.
• Document Recovery Procedures: Maintain clear documentation outlining recovery procedures for different types of failures. This can help your team respond quickly and efficiently to issues, reducing downtime.

Performance Optimization

Implementing effective strategies can enhance the overall performance of your pipeline and improve the quality of insights derived from your data. Follow these tips:

• Profile and Monitor Performance: Regularly profile your data pipeline to identify bottlenecks and areas for improvement. Use monitoring tools to track metrics such as throughput, processing time, and resource utilization, enabling you to pinpoint performance issues.
• Utilize Parallel Processing: Leverage parallel processing techniques to execute multiple tasks simultaneously. This can significantly reduce processing times and improve the efficiency of data transformations, especially when handling large datasets.
• Optimize Data Formats: Choose the most efficient data formats for storage and transmission. For instance, using columnar storage formats like Parquet or ORC can reduce I/O operations and improve query performance.
• Implement Incremental Loading: Instead of processing all data at once, use incremental loading to only process new or updated data. This approach reduces the volume of data to be processed at any given time, leading to faster execution.
• Reduce Data Movement: Minimize the movement of data between different systems or stages of the pipeline. Keep processing local to the data source whenever possible to reduce latency and improve performance.
• Optimize Resource Allocation: Fine-tune the allocation of resources, such as CPU and memory, based on the specific demands of your data pipeline. Ensure that your infrastructure can dynamically scale to meet varying workloads.
• Employ Load Balancing: Distribute workloads evenly across processing nodes to prevent any single node from becoming a bottleneck. Load balancing helps maintain consistent performance and ensures efficient resource utilization.
• Continuously Refine and Iterate: Regularly review and refine your data pipeline based on performance metrics and user feedback. Iterative improvements can help you adapt to changing requirements and enhance overall performance over time.

How to Select the Best Tool for Building Your Data Pipelines

Selecting the right data pipeline tool is critical or ensuring efficient data processing and management tailored to your organization’s needs. Here are some key considerations to help guide your decision:

• Define Your Requirements: Start by identifying your specific data processing needs, including the types of data sources you’ll be using, the volume of data, required transformation processes, and the desired output formats.
• Scalability: Choose a tool that can scale with your data growth. As your data volume increases, ensure that the tool can handle larger datasets and more complex processing without compromising performance.
• Integration Capabilities: Look for a data pipeline tool that easily integrates with your existing systems, databases, and cloud services. Seamless connectivity to various data sources and destinations is essential for a smooth data flow.
• Ease of Use: Consider the user interface and the overall ease of use of the tool. A user-friendly interface can simplify the setup and management of data pipelines, enabling your team to focus on data analysis rather than technical complexities.
• Real-Time vs. Batch Processing: Determine whether you need real-time processing, batch processing, or a combination of both. Some tools are optimized for one or the other, so choose a solution that aligns with your data processing requirements.
• Security and Compliance: Ensure that the tool adheres to your organization’s security requirements and compliance standards. Look for features such as data encryption, access controls, and audit logs to protect sensitive information.
• Performance and Reliability: Research the performance benchmarks of the tool, including processing speed and reliability. Check user reviews and case studies to gauge how well the tool performs under different workloads.
• Flexibility and Customization: Choose a tool that allows for flexibility and customization in pipeline design. This ensures that you can adapt the pipeline to evolving business needs and incorporate new data sources or processing techniques as required.

How Can Knack Support Your Data Pipeline Needs?

Knack is a powerful no-code platform that simplifies the creation and management of data pipelines, making it easier for organizations to collect, process, and analyze data without extensive coding knowledge. By providing an intuitive interface and robust features, Knack enables users to design customized data pipelines that align with their specific needs. 

Here are some key features of Knack that support data pipelines :

• Visual Data Modeling: Knack allows users to create custom data models visually, making it easy to define relationships between different data points. Benefit: This feature streamlines the data organization process, enabling users to structure their data intuitively and accurately, ensuring that the right information flows seamlessly through the pipeline.
• Integrations with External Services: Knack offers integrations with third-party applications and services, enabling smooth data exchange and interaction with other tools. Benefit: This connectivity ensures that users can effortlessly pull data from various sources and push processed data to other platforms, enhancing collaboration and data utilization.
• Automated Workflows: Users can automate repetitive tasks and processes within their data pipelines using Knack’s built-in workflow automation features. Benefit: This automation reduces manual effort and errors, allowing teams to focus on more strategic tasks while maintaining a consistent flow of data.
• Real-Time Data Access: Knack provides real-time access to data, ensuring that users can view and interact with the most current information. Benefit: This capability empowers organizations to make timely, data-driven decisions based on up-to-date insights, ultimately improving responsiveness and agility.

If you’re ready to transform how you manage your data pipelines, sign up with Knack and start building for free today!

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